Welcome to the Data Engineering category of DZone, where you will find all the information you need for AI/ML, big data, data, databases, and IoT. As you determine the first steps for new systems or reevaluate existing ones, you're going to require tools and resources to gather, store, and analyze data. The Zones within our Data Engineering category contain resources that will help you expertly navigate through the SDLC Analysis stage.
Artificial intelligence (AI) and machine learning (ML) are two fields that work together to create computer systems capable of perception, recognition, decision-making, and translation. Separately, AI is the ability for a computer system to mimic human intelligence through math and logic, and ML builds off AI by developing methods that "learn" through experience and do not require instruction. In the AI/ML Zone, you'll find resources ranging from tutorials to use cases that will help you navigate this rapidly growing field.
Big data comprises datasets that are massive, varied, complex, and can't be handled traditionally. Big data can include both structured and unstructured data, and it is often stored in data lakes or data warehouses. As organizations grow, big data becomes increasingly more crucial for gathering business insights and analytics. The Big Data Zone contains the resources you need for understanding data storage, data modeling, ELT, ETL, and more.
Data is at the core of software development. Think of it as information stored in anything from text documents and images to entire software programs, and these bits of information need to be processed, read, analyzed, stored, and transported throughout systems. In this Zone, you'll find resources covering the tools and strategies you need to handle data properly.
A database is a collection of structured data that is stored in a computer system, and it can be hosted on-premises or in the cloud. As databases are designed to enable easy access to data, our resources are compiled here for smooth browsing of everything you need to know from database management systems to database languages.
IoT, or the Internet of Things, is a technological field that makes it possible for users to connect devices and systems and exchange data over the internet. Through DZone's IoT resources, you'll learn about smart devices, sensors, networks, edge computing, and many other technologies — including those that are now part of the average person's daily life.
Enterprise AI
In recent years, artificial intelligence has become less of a buzzword and more of an adopted process across the enterprise. With that, there is a growing need to increase operational efficiency as customer demands arise. AI platforms have become increasingly more sophisticated, and there has become the need to establish guidelines and ownership.In DZone's 2022 Enterprise AI Trend Report, we explore MLOps, explainability, and how to select the best AI platform for your business. We also share a tutorial on how to create a machine learning service using Spring Boot, and how to deploy AI with an event-driven platform. The goal of this Trend Report is to better inform the developer audience on practical tools and design paradigms, new technologies, and the overall operational impact of AI within the business.This is a technology space that's constantly shifting and evolving. As part of our December 2022 re-launch, we've added new articles pertaining to knowledge graphs, a solutions directory for popular AI tools, and more.
PlatformCon 2024 Session Recap: Platform Engineering and AI
Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Low-Code Development: Elevating the Engineering Experience With Low and No Code. The advent of large language models (LLMs) has led to a rush to shoehorn artificial intelligence (AI) into every product that makes sense, as well as into quite a few that don't. But there is one area where AI has already proven to be a powerful and useful addition: low- and no-code software development. Let's look at how and why AI makes building applications faster and easier, especially with low- and no-code tools. AI's Role in Development First, let's discuss two of the most common roles AI has in simplifying and speeding up the development process: Generating code Acting as an intelligent assistant AI code generators and assistants use LLMs trained on massive codebases that teach them the syntax, patterns, and semantics of programming languages. These models predict the code needed to fulfill a prompt — the same way chatbots use their training to predict the next word in a sentence. Automated Code Generation AI code generators create code based on input. These prompts take the form of natural language input or code in an integrated development environment (IDE) or on the command line. Code generators speed up development by freeing programmers from writing repetitive code. They can reduce common errors and typographical mistakes, too. But similar to the LLMs used to generate text, code generators require scrutiny and can make their own errors. Developers need to be careful when accepting code generated by AI, and they must test not just whether it builds but also that it does what the user asks. gpt-engineer is an open-source AI code generator that accepts natural language prompts to build entire codebases. It works with ChatGPT or custom LLMs like Llama. Intelligent Assistants for Development Intelligent assistants provide developers with real-time help as they work. They work as a form of AI code generator, but instead of using natural language prompts, they can autocomplete, provide in-line documentation, and accept specialized commands. These assistants can work inside programming tools like Eclipse and Microsoft's VS Code, the command line, or all three. These tools offer many of the same benefits as code generators, including shorter development times, fewer errors, and reduced typos. They also serve as learning tools since they provide developers programming information as they work. But like any AI tool, AI assistants are not foolproof — they require close and careful monitoring. GitHub's Copilot is a popular AI programming assistant. It uses models built on public GitHub repositories, so it supports a very wide variety of languages and plugs into all the most popular programming tools. Microsoft's Power Platform and Amazon Q Developer are two popular commercial options, while Refact.ai is an open-source alternative. AI and Low and No Code: Perfect Together Low and no code developed in response to a need for tools that allow newcomers and non-technologists to quickly customize software for their needs. AI takes this one step further by making it even easier to translate ideas into software. Democratizing Development AI code generators and assistants democratize software development by making coding more accessible, enhancing productivity, and facilitating continuous learning. These tools lower the entry barriers for newcomers to programming. A novice programmer can use them to quickly build working applications by learning on the job. For example, Microsoft Power Apps include Copilot, which generates application code for you and then works with you to refine it. How AI Enhances Low- and No-Code Platforms There are several important ways that AI enhances low- and no-code platforms. We've already covered AI's ability to generate code snippets from natural language prompts or the context in a code editor. You can use LLMs like ChatGPT and Gemini to generate code for many low-code platforms, while many no-code platforms like AppSmith and Google AppSheet use AI to generate integrations based on text that describes what you want the integration to do. You can also use AI to automate preparing, cleaning, and analyzing data, too. This makes it easier to integrate and work with large datasets that need tuning before they're suitable for use with your models. Tools like Amazon SageMaker use AI to ingest, sort, organize, and streamline data. Some platforms use AI to help create user interfaces and populate forms. For example, Microsoft's Power Platform uses AI to enable users to build user interfaces and automate processes through conversational interactions with its copilot. All these features help make low- and no-code development faster, including in terms of scalability, since more team members can take part in the development process. How Low and No Code Enable AI Development While AI is invaluable for generating code, it's also useful in your low- and no-code applications. Many low- and no-code platforms allow you to build and deploy AI-enabled applications. They abstract away the complexity of adding capabilities like natural language processing, computer vision, and AI APIs from your app. Users expect applications to offer features like voice prompts, chatbots, and image recognition. Developing these capabilities "from scratch" takes time, even for experienced developers, so many platforms offer modules that make it easy to add them with little or no code. For example, Microsoft has low-code tools for building Power Virtual Agents (now part of its Copilot Studio) on Azure. These agents can plug into a wide variety of skills backed by Azure services and drive them using a chat interface. Low- and no-code platforms like Amazon SageMaker and Google's Teachable Machine manage tasks like preparing data, training custom machine learning (ML) models, and deploying AI applications. And Zapier harnesses voice to text from Amazon's Alexa and directs the output to many different applications. Figure 1. Building low-code AI-enabled apps with building blocks Examples of AI-Powered Low- and No-Code Tools This table contains a list of widely used low- and no-code platforms that support AI code generation, AI-enabled application extensions, or both: Table 1. AI-powered low- and no-code tools Application Type Primary Users Key Features AI/ML Capabilities Amazon CodeWhisperer AI-powered code generator Developers Real-time code suggestions, security scans, broad language support ML-powered code suggestions Amazon SageMaker Fully managed ML service Data scientists, ML engineers Ability to build, train, and deploy ML models; fully integrated IDE; support for MLOps Pre-trained models, custom model training and deployment GitHub Copilot AI pair programmer Developers Code suggestions, multi-language support, context-aware suggestions Generative AI model for code suggestions Google Cloud AutoML No-code AI Data scientists, developers High-quality custom ML models can be trained with minimal effort; support for various data types, including images, text, and audio Automated ML model training and deployment Microsoft Power Apps Low-code app development Business users, developers Custom business apps can be built; support for many diverse data sources; automated workflows AI builder for app enhancement Microsoft Power Platform Low-code platform Business analysts, developers Business intelligence, app development, app connectivity, robotic process automation AI app builder for enhancing apps and processes Pitfalls of Using AI for Development AI's ability to improve low- and no-code development is undeniable, but so are its risks. Any use of AI requires proper training and comprehensive governance. LLM's tendency to "hallucinate" answers to prompts applies to code generation, too. So while AI tools lower the barrier to entry for novice developers, you still need experienced programmers to review, verify, and test code before you deploy it to production. Developers use AI by submitting prompts and receiving responses. Depending on the project, those prompts may contain sensitive information. If the model belongs to a third-party vendor or isn't correctly secured, your developers expose that information. When it works, AI suggests code that is likely to fulfill the prompt it's evaluating. The code is correct, but it's not necessarily the best solution. So a heavy reliance on AI to generate code can lead to code that is difficult to change and represents a large amount of technical debt. AI is already making important contributions toward democratizing programming and speeding up low- and no-code development. As LLMs gradually improve, AI tools for creating software will only get better. Even as these tools improve, IT leaders still need to proceed cautiously. AI offers great power, but that power comes with great responsibility. Any and all use of AI requires comprehensive governance and complete safeguards that protect organizations from errors, vulnerabilities, and data loss. Conclusion Integrating AI into low- and no-code development platforms has already revolutionized software development. It has democratized access to advanced coding and empowered non-experts so that they can build sophisticated applications. AI-driven tools and intelligent assistants have reduced development times, improved development scalability, and helped minimize common errors. But these powerful capabilities come with risks and responsibilities. Developers and IT leaders need to establish robust governance, testing regimes, and validation systems if they want to safely harness AI's full potential. AI technologies and models continue to improve, and it's probable that they will become the cornerstone of innovative, efficient, and secure software development. See how AI can help your organization widen your development efforts via low- and no-code tools. This is an excerpt from DZone's 2024 Trend Report, Low-Code Development: Elevating the Engineering Experience With Low and No Code.Read the Free Report
Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Cloud Native: Championing Cloud Development Across the SDLC. 2024 and the dawn of cloud-native AI technologies marked a significant jump in computational capabilities. We're experiencing a new era where artificial intelligence (AI) and platform engineering converge to transform cloud computing landscapes. AI is now merging with cloud computing, and we're experiencing an age where AI transcends traditional boundaries, offering scalable, efficient, and powerful solutions that learn and improve over time. Platform engineering is providing the backbone for these AI systems to operate within cloud environments seamlessly. This shift entails designing, implementing, and managing the software platforms that serve as the fertile ground for AI applications to flourish. Together, the integration of AI and platform engineering in cloud-native environments is not just an enhancement but a transformative force, redefining the very fabric of how services are now being delivered, consumed, and evolved in the digital cosmos. The Rise of AI in Cloud Computing Azure and Google Cloud are pivotal solutions in cloud computing technology, each offering a robust suite of AI capabilities that cater to a wide array of business needs. Azure brings to the table its AI Services and Azure Machine Learning, a collection of AI tools that enable developers to build, train, and deploy AI models rapidly, thus leveraging its vast cloud infrastructure. Google Cloud, on the other hand, shines with its AI Platform and AutoML, which simplify the creation and scaling of AI products, integrating seamlessly with Google's data analytics and storage services. These platforms empower organizations to integrate intelligent decision-making into their applications, optimize processes, and provide insights that were once beyond reach. A quintessential case study that illustrates the successful implementation of AI in the cloud is that of the Zoological Society of London (ZSL), which utilized Google Cloud's AI to tackle the biodiversity crisis. ZSL's "Instant Detect" system harnesses AI on Google Cloud to analyze vast amounts of images and sensor data from wildlife cameras across the globe in real time. This system enables rapid identification and categorization of species, transforming the way conservation efforts are conducted by providing precise, actionable data, leading to more effective protection of endangered species. Such implementations as ZSL's not only showcase the technical prowess of cloud AI capabilities but also underscore their potential to make a significant positive impact on critical global issues. Platform Engineering: The New Frontier in Cloud Development Platform engineering is a multifaceted discipline that refers to the strategic design, development, and maintenance of software platforms to support more efficient deployment and application operations. It involves creating a stable and scalable foundation that provides developers the tools and capabilities needed to develop, run, and manage applications without the complexity of maintaining the underlying infrastructure. The scope of platform engineering spans the creation of internal development platforms, automation of infrastructure provisioning, implementation of continuous integration and continuous deployment (CI/CD) pipelines, and the insurance of the platforms' reliability and security. In cloud-native ecosystems, platform engineers play a pivotal role. They are the architects of the digital landscape, responsible for constructing the robust frameworks upon which applications are built and delivered. Their work involves creating abstractions on top of cloud infrastructure to provide a seamless development experience and operational excellence. Figure 1. Platform engineering from the top down Platform engineers enable teams to focus on creating business value by abstracting away complexities related to environment configurations, along with resource scaling and service dependencies. They guarantee that the underlying systems are resilient, self-healing, and can be deployed consistently across various environments. The convergence of DevOps and platform engineering with AI tools is an evolution that is reshaping the future of cloud-native technologies. DevOps practices are enhanced by AI's ability to predict, automate, and optimize processes. AI tools can analyze data from development pipelines to predict potential issues, automate root cause analyses, and optimize resources, leading to improved efficiency and reduced downtime. Moreover, AI can drive intelligent automation in platform engineering, enabling proactive scaling and self-tuning of resources, and personalized developer experiences. This synergy creates a dynamic environment where the speed and quality of software delivery are continually advancing, setting the stage for more innovative and resilient cloud-native applications. Synergies Between AI and Platform Engineering AI-augmented platform engineering introduces a layer of intelligence to automate processes, streamline operations, and enhance decision-making. Machine learning (ML) models, for instance, can parse through massive datasets generated by cloud platforms to identify patterns and predict trends, allowing for real-time optimizations. AI can automate routine tasks such as network configurations, system updates, and security patches; these automations not only accelerate the workflow but also reduce human error, freeing up engineers to focus on more strategic initiatives. There are various examples of AI-driven automation in cloud environments, such as implementing intelligent systems to analyze application usage patterns and automatically adjust computing resources to meet demand without human intervention. The significant cost savings and performance improvements provide exceptional value to an organization. AI-operated security protocols can autonomously monitor and respond to threats more quickly than traditional methods, significantly enhancing the security posture of the cloud environment. Predictive analytics and ML are particularly transformative in platform optimization. They allow for anticipatory resource management, where systems can forecast loads and scale resources accordingly. ML algorithms can optimize data storage, intelligently archiving or retrieving data based on usage patterns and access frequencies. Figure 2. AI resource autoscaling Moreover, AI can oversee and adjust platform configurations, ensuring that the environment is continuously refined for optimal performance. These predictive capabilities are not limited to resource management; they also extend to predicting application failures, user behavior, and even market trends, providing insights that can inform strategic business decisions. The proactive nature of predictive analytics means that platform engineers can move from reactive maintenance to a more visionary approach, crafting platforms that are not just robust and efficient but also self-improving and adaptive to future needs. Changing Landscapes: The New Cloud Native The landscape of cloud native and platform engineering is rapidly evolving, particularly with leading cloud service providers like Azure and Google Cloud. This evolution is largely driven by the growing demand for more scalable, reliable, and efficient IT infrastructure, enabling businesses to innovate faster and respond to market changes more effectively. In the context of Azure, Microsoft has been heavily investing in Azure Kubernetes Service (AKS) and serverless offerings, aiming to provide more flexibility and ease of management for cloud-native applications. Azure's emphasis on DevOps, through tools like Azure DevOps and Azure Pipelines, reflects a strong commitment to streamlining the development lifecycle and enhancing collaboration between development and operations teams. Azure's focus on hybrid cloud environments, with Azure Arc, allows businesses to extend Azure services and management to any infrastructure, fostering greater agility and consistency across different environments. In the world of Google Cloud, they've been leveraging expertise in containerization and data analytics to enhance cloud-native offerings. Google Kubernetes Engine (GKE) stands out as a robust, managed environment for deploying, managing, and scaling containerized applications using Google's infrastructure. Google Cloud's approach to serverless computing, with products like Cloud Run and Cloud Functions, offers developers the ability to build and deploy applications without worrying about the underlying infrastructure. Google's commitment to open-source technologies and its leading-edge work in AI and ML integrate seamlessly into its cloud-native services, providing businesses with powerful tools to drive innovation. Both Azure and Google Cloud are shaping the future of cloud-native and platform engineering by continuously adapting to technological advancements and changing market needs. Their focus on Kubernetes, serverless computing, and seamless integration between development and operations underlines a broader industry trend toward more agile, efficient, and scalable cloud environments. Implications for the Future of Cloud Computing AI is set to revolutionize cloud computing, making cloud-native technologies more self-sufficient and efficient. Advanced AI will oversee cloud operations, enhancing performance and cost effectiveness while enabling services to self-correct. Yet integrating AI presents ethical challenges, especially concerning data privacy and decision-making bias, and poses risks requiring solid safeguards. As AI reshapes cloud services, sustainability will be key; future AI must be energy efficient and environmentally friendly to ensure responsible growth. Kickstarting Your Platform Engineering and AI Journey To effectively adopt AI, organizations must nurture a culture oriented toward learning and prepare by auditing their IT setup, pinpointing AI opportunities, and establishing data management policies. Further: Upskilling in areas such as machine learning, analytics, and cloud architecture is crucial. Launching AI integration through targeted pilot projects can showcase the potential and inform broader strategies. Collaborating with cross-functional teams and selecting cloud providers with compatible AI tools can streamline the process. Balancing innovation with consistent operations is essential for embedding AI into cloud infrastructures. Conclusion Platform engineering with AI integration is revolutionizing cloud-native environments, enhancing their scalability, reliability, and efficiency. By enabling predictive analytics and automated optimization, AI ensures cloud resources are effectively utilized and services remain resilient. Adopting AI is crucial for future-proofing cloud applications, and it necessitates foundational adjustments and a commitment to upskilling. The advantages include staying competitive and quickly adapting to market shifts. As AI evolves, it will further automate and refine cloud services, making a continued investment in AI a strategic choice for forward-looking organizations. This is an excerpt from DZone's 2024 Trend Report,Cloud Native: Championing Cloud Development Across the SDLC.Read the Free Report
Stream processing has existed for decades. However, it really kicks off in the 2020s thanks to the adoption of open-source frameworks like Apache Kafka and Flink. Fully managed cloud services make it easy to configure and deploy stream processing in a cloud-native way; even without the need to write any code. This blog post explores the past, present, and future of stream processing. The discussion includes various technologies and cloud services, low code/ no code trade-offs, outlooks into the support of machine learning and GenAI, streaming databases, and the integration between data streaming and data lakes with Apache Iceberg. In December 2023, the research company proved that data streaming is a new software category and not just yet another integration or data platform. Forrester published “The Forrester Wave™: Streaming Data Platforms, Q4 2023“. Get free access to the report here. The leaders are Microsoft, Google, and Confluent, followed by Oracle, Amazon, Cloudera, and a few others. A great time to review the past, present, and future of stream processing as a key component in a data streaming architecture. The Past of Stream Processing: The Move from Batch to Real-Time The evolution of stream processing began as industries sought more timely insights from their data. Initially, batch processing was the norm. Data was collected over a period, stored, and processed at intervals. This method, while effective for historical analysis, proved inefficient for real-time decision-making. In parallel to batch processing, message queues were created to provide real-time communication for transactional data. Message Brokers like IBM MQ or TIBCO EMS were a common way to decouple applications. Applications send data and receive data in an event-driven architecture without worrying about whether the recipient is ready, how to handle backpressure, etc. The stream processing journey began. Stream Processing Is a Journey Over Decades... ... and we are still in a very early stage at most enterprises. Here is an excellent timeline of TimePlus about the journey of stream processing open source frameworks, proprietary platforms, and SaaS cloud services: Source: TimePlus The stream processing journey started decades ago with research and the first purpose-built proprietary products for specific use cases like stock trading. Open source stream processing frameworks emerged during the big data and Hadoop era to make at least the ingestion layer a bit more real-time. Today, most enterprises at least get started understanding the value of stream processing for analytical and transactional use cases across industries. The cloud is a fundamental change as you can start streaming and processing data with a button click leveraging fully managed SaaS and simple UIs (if you don't want to operate infrastructure or write low-level source code). TIBCO StreamBase, Software AG Apama, IBM Streams The advent of message queue technologies like IBM MQ and TIBCO EMS moved many critical applications to real-time message brokers. Real-time messaging enables the consumption of data in real-time to store it in a database, mainframe, or application for further processing. However, only true stream processing capabilities included in tools like TIBCO StreamBase, Software AG Apama, or IBM (InfoSphere) Streams marked a significant shift towards real-time data processing. These products enable businesses to react to information as it arrives by processing and correlating the data in motion. Visual coding in tools like StreamBase or Apama represents an innovative approach to developing stream processing solutions. These tools provide a graphical interface that allows developers and analysts to design, build, and test applications by connecting various components and logic blocks visually, rather than writing code manually. Under the hood, the code generation worked with a Streaming SQL language. Here is a screenshot of the TIBCO StreamBase IDE for visual drag & drop of streaming pipelines: TIBCO StreamBase IDE Some drawbacks of these early stream processing solutions include high cost, vendor lock-in, no flexibility regarding tools or APIs, and missing communities. These platforms are monolithic and were built far before cloud-native elasticity and scalability became a requirement for most RFIs and RFPs when evaluating vendors. Open Source Event Streaming With Apache Kafka The actual significant change for stream processing came with the introduction of Apache Kafka, a distributed streaming platform that allowed for high-throughput, fault-tolerant handling of real-time data feeds. Kafka, alongside other technologies like Apache Flink, revolutionized the landscape by providing the tools necessary to move from batch to real-time stream processing seamlessly. The adoption of open-source technologies changed all industries. Openness, flexibility, and community-driven development enabled easier influence on the features and faster innovation. Over 100.000 organizations use Apache Kafka. The massive adoption came from a unique combination of capabilities: Messaging, storage, data integration, and stream processing, all in one scalable and distributed infrastructure. Various open-source stream processing engines emerged. Kafka Streams was added to the Apache Kafka project. Other examples include Apache Storm, Spark Streaming, and Apache Flink. The Present of Stream Processing: Architectural Evolution and Mass Adoption The fundamental change to processing data in motion has enabled the development of data products and data mesh. Decentralizing data ownership and management with domain-driven design and technology-independent microservices promotes a more collaborative and flexible approach to data architecture. Each business unit can choose its own technology, API, cloud service, and communication paradigms like real-time, batch, or request-response. From Lambda Architecture to Kappa Architecture Today, stream processing is at the heart of modern data architecture, thanks in part to the emergence of the Kappa architecture. This model simplifies the traditional Lambda Architecture by using a single stream processing system to handle both real-time and historical data analysis, reducing complexity and increasing efficiency. Lambda architecture with separate real-time and batch layers: Kappa architecture with a single pipeline for real-time and batch processing: For more details about the pros and cons of Kappa vs. Lambda, check out my "Kappa Architecture is Mainstream Replacing Lambda". It explores case studies from Uber, Twitter, Disney and Shopify. Kafka Streams and Apache Flink Become Mainstream Apache Kafka has become synonymous with building scalable and fault-tolerant streaming data pipelines. Kafka facilitating true decoupling of domains and applications makes it integral to microservices and data mesh architectures. Plenty of stream processing frameworks, products, and cloud services emerged in the past years. This includes open-source frameworks like Kafka Streams, Apache Storm, Samza, Flume, Apex, Flink, and Spark Streaming, and cloud services like Amazon Kinesis, Google Cloud Dataflow, and Azure Stream Analytics. The "Data Streaming Landscape 2024" gives an overview of relevant technologies and vendors. Apache Flink seems to become the de facto standard for many enterprises (and vendors). The adoption is like Kafka four years ago: Source: Confluent This does not mean other frameworks and solutions are bad. For instance, Kafka Streams is complementary to Apache Flink, as it suits different use cases. No matter what technology enterprises choose, the mass adoption of stream processing is in progress right now. This includes modernizing existing batch processes AND building innovative new business models that only work in real-time. As a concrete example, think about ride-hailing apps like Uber, Lyft, FREENOW, and Grab. They are only possible because events are processed and correlated in real time. Otherwise, everyone would still prefer a traditional taxi. Stateless and Stateful Stream Processing In data streaming, stateless and stateful stream processing are two approaches that define how data is handled and processed over time: The choice between stateless and stateful processing depends on the specific requirements of the application, including the nature of the data, the complexity of the processing needed, and the performance and scalability requirements. Stateless Stream Processing Stateless Stream Processing refers to the handling of each data point or event independently from others. In this model, the processing of an event does not depend on the outcomes of previous events or requires keeping track of the state between events. Each event is processed based on the information it contains, without the need for historical context or future data points. This approach is simpler and can be highly efficient for tasks that don't require knowledge beyond the current event being processed. The implementation could be a stream processor (like Kafka Streams or Flink), functionality in a connector (like Kafka Connect Single Message Transforms), or a Web Assembly (WASM) embedded into a streaming platform. Stateful Stream Processing Stateful Stream Processing involves keeping track of information (state) across multiple events to perform computations that depend on data beyond the current event. This model allows for more complex operations like windowing (aggregating events over a specific time frame), joining streams of data based on keys, and tracking sequences of events or patterns over time. Stateful processing is essential for scenarios where the outcome depends on accumulated knowledge or trends derived from a series of data points, not just on a single input. The implementation is much more complex and challenging than stateless stream processing. A dedicated stream processing implementation is required. Dedicated distributed engines (like Apache Flink) handle stateful computations, memory usage, and scalability better than Kafka-native tools like Kafka Streams or KSQL (because the latter are bound to Kafka Topics). Low Code, No Code, AND A Lot of Code! No-code and low-code tools are software platforms that enable users to develop applications quickly and with minimal coding knowledge. These tools provide graphical user interfaces with drag-and-drop capabilities, allowing users to assemble and configure applications visually rather than writing extensive lines of code. Common features and benefits of visual coding: Rapid development: Both types of platforms significantly reduce development time, enabling faster delivery of applications. User-friendly interface: The graphical interface and drag-and-drop functionality make it easy for users to design, build, and iterate on applications. Cost reduction: By enabling quicker development with fewer resources, these platforms can lower the cost of software creation and maintenance. Accessibility: They make application development accessible to a broader range of people, reducing the dependency on skilled developers for every project. So far, the theory. Disadvantages of Visual Coding Tools Actually, StreamBase, Apama, et al., had great visual coding offerings. However, no-code/low-code tools have many drawbacks and disadvantages, too: Limited customization and flexibility: While these platforms can speed up development for standard applications, they may lack the flexibility needed for highly customized solutions. Developers might find it challenging to implement specific functionalities that aren't supported out of the box. Dependency on vendors: Using no-code/low-code platforms often means relying on third-party vendors for the platform's stability, updates, and security. This dependency can lead to potential issues if the vendor cannot maintain the platform or goes out of business. And often the platform team is the bottleneck for implementing new business or integration logic. Performance concerns: Applications built with no-code/low-code platforms may not be as optimized as those developed with traditional coding, potentially leading to lower performance or inefficiencies, especially for complex applications. Scalability issues: As businesses grow, applications might need to scale up to support increased loads. No-code/low-code platforms might not always support this level of scalability or might require significant workarounds, affecting performance and user experience. Over-reliance on non-technical users: While empowering citizen developers is a key advantage of these platforms, it can also lead to governance challenges. Without proper oversight, non-technical users might create inefficient workflows or data structures, leading to technical debt and maintenance issues. Cost over time: Initially, no-code/low-code platforms can reduce development costs. However, as applications grow and evolve, the ongoing subscription costs or fees for additional features and scalability can become significant. Flexibility Is King: Stream Processing for Everyone! Microservices, domain-driven design, data mesh... All these modern design approaches taught us to provide flexible enterprise architectures. Each business unit and persona should be able to choose its own technology, API, or SaaS. And no matter if you do real-time, near real-time, batch, or request-response communication. Apache Kafka provides the true decoupling out-of-the-box. Therefore, low-code or now-code tools are an option. However, a data scientist, data engineer, software developer, or citizen integrator can choose their own technology for stream processing. The past, present, and future of stream processing show different frameworks, visual coding tools and even applied generative AI. One solution does NOT replace but complement the other alternatives: The Future of Stream Processing: Serverless SaaS, GenAI, and Streaming Databases Stream processing is set to grow exponentially in the future, thanks to advancements in cloud computing, SaaS, and AI. Let's explore the future of stream processing and look at the expected short, mid and long-term developments. SHORT TERM: Fully Managed Serverless SaaS for Stream Processing The cloud's scalability and flexibility offer an ideal environment for stream processing applications, reducing the overhead and resources required for on-premise solutions. As SaaS models continue to evolve, stream processing capabilities will become more accessible to a broader range of businesses, democratizing real-time data analytics. For instance, look at the serverless Flink Actions in Confluent Cloud. You can configure and deploy stream processing for use cases like deduplication or masking without any code: Source: Confluent MIDTERM: Automated Tooling and the Help of GenAI Integrating AI and machine learning with stream processing will enable more sophisticated predictive analytics. This opens new frontiers for automated decision-making and intelligent applications while continuously processing incoming event streams. The full potential of embedding AI into stream processing has to be learned and implemented in the upcoming years. For instance, automated data profiling is one instance of stream processing that GenAI can support significantly. Software tools analyze and understand the quality, structure, and content of a dataset without manual intervention as the events flow through the data pipeline in real time. This process typically involves examining the data to identify patterns, anomalies, missing values, and inconsistencies. A perfect fit for stream processing! Automated data profiling in the stream processor can provide insights into data types, frequency distributions, relationships between columns, and other metadata information crucial for data quality assessment, governance, and preparation for further analysis or processing. MIDTERM: Streaming Storage and Analytics With Apache Iceberg Apache Iceberg is an open-source table format for huge analytic datasets that provides powerful capabilities in managing large-scale data in data lakes. Its integration with streaming data sources like Apache Kafka and analytics platforms, such as Snowflake, Starburst, Dremio, AWS Athena, or Databricks, can significantly enhance data management and analytics workflows. Integration Between Streaming Data From Kafka and Analytics on Databricks or Snowflake Using Apache Iceberg Supporting the Apache Iceberg table format might be a crucial strategic move by streaming and analytics frameworks, vendors, and cloud services. Here are some key benefits from the enterprise architecture perspective: Unified batch and stream processing: Iceberg tables can serve as a bridge between streaming data ingestion from Kafka and downstream analytic processing. By treating streaming data as an extension of a batch-based table, Iceberg enables a seamless transition from real-time to batch analytics, allowing organizations to analyze data with minimal latency. Schema evolution: Iceberg supports schema evolution without breaking downstream systems. This is useful when dealing with streaming data from Kafka, where the schema might evolve. Consumers can continue reading data using the schema they understand, ensuring compatibility and reducing the need for data pipeline modifications. Time travel and snapshot isolation: Iceberg's time travel feature allows analytics on data as it looked at any point in time, providing snapshot isolation for consistent reads. This is crucial for reproducible reporting and debugging, especially when dealing with continuously updating streaming data from Kafka. Cross-platform compatibility: Iceberg provides a unified data layer accessible by different compute engines, including those used by Databricks and Snowflake. This enables organizations to maintain a single copy of their data that is queryable across different platforms, facilitating a multi-tool analytics ecosystem without data silos. LONG-TERM: Transactional + Analytics = Streaming Database? Streaming databases, like RisingWave or Materialize, are designed to handle real-time data processing and analytics. This offers a way to manage and query data that is continuously generated from sources like IoT devices, online transactions, and application logs. Traditional databases that are optimized for static data are stored on disk. Instead, streaming databases are built to process and analyze data in motion. They provide insights almost instantaneously as the data flows through the system. Streaming databases offer the ability to perform complex queries and analytics on streaming data, further empowering organizations to harness real-time insights. The ongoing innovation in streaming databases will probably lead to more advanced, efficient, and user-friendly solutions, facilitating broader adoption and more creative applications of stream processing technologies. Having said this, we are still in the very early stages. It is not clear yet when you really need a streaming database instead of a mature and scalable stream processor like Apache Flink. The future will show us that competition is great for innovation. The Future of Stream Processing is Open Source and Cloud The journey from batch to real-time processing has transformed how businesses interact with their data. The continued evolution couples technologies like Apache Kafka, Kafka Streams, and Apache Flink with the growth of cloud computing and SaaS. Stream processing will redefine the future of data analytics and decision-making. As we look ahead, the future possibilities for stream processing are boundless, promising more agile, intelligent, and real-time insights into the ever-increasing streams of data. If you want to learn more, listen to the following on-demand webinar about the past, present, and future of stream processing with me joined by the two streaming industry veterans Richard Tibbets (founder of StreamBase) and Michael Benjamin (TimePlus). I had the please work with them for a few years at TIBCO where we deployed StreamBase at many Financial Services companies for stock trading and similar use cases: What does your stream processing journey look like? In which decade did you join? Or are you just learning with the latest open-source frameworks or cloud services? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.
I bet you might have come across a scenario while automating API/web or mobile applications where, while registering a user, you may be setting the address for checking out a product in the end-to-end user journey in test automation. So, how do you do that? Normally, we create a POJO class in Java with the fields required to register a user or to set the address for checking out the product and then set the values in the test using the constructor of the POJO class. Let’s take a look at an example of registering a user where the following are mandatory fields required to fill in the registration form: First Name Last Name Address City State Country Mobile Number As we need to handle these fields in automation testing we will have to pass on respective values in the fields at the time of executing the tests. Before Using the Builder Pattern A POJO class, with the above-mentioned mandatory fields, will be created with the Getter and Setter methods inside that POJO class, and using a constructor values are set in the respective fields. Check out the code example of RegisterUser class given below for the representation of what we are discussing. Java public class RegisterUser { private String firstName; private String lastName; private String address; private String city; private String state; private String country; private String mobileNumber; public RegisterUser (final String firstName, final String lastName, final String address, final String city, final String state, final String country, final String mobileNumber) { this.firstName = firstName; this.lastName = lastName; this.address = address; this.city = city; this.state = state; this.country = country; this.mobileNumber = mobileNumber; } public String getFirstName () { return firstName; } public void setFirstName (final String firstName) { this.firstName = firstName; } public String getLastName () { return lastName; } public void setLastName (final String lastName) { this.lastName = lastName; } public String getAddress () { return address; } public void setAddress (final String address) { this.address = address; } public String getCity () { return city; } public void setCity (final String city) { this.city = city; } public String getState () { return state; } public void setState (final String state) { this.state = state; } public String getCountry () { return country; } public void setCountry (final String country) { this.country = country; } public String getMobileNumber () { return mobileNumber; } public void setMobileNumber (final String mobileNumber) { this.mobileNumber = mobileNumber; } } Now, if we want to use this POJO, we would have to create an instance of RegisterUser class and pass the values in the constructor parameters as given in the code example below to set the data in the respective fields. Check out the below example of the Register User test of how we do it. Java public class RegistrationTest { @Test public void testRegisterUser () { RegisterUser registerUser = new RegisterUser ("John", "Doe", "302, Adam Street, 1st Lane", "New Orleans", "New Jersey", "US", "52145364"); assertEquals (registerUser.getFirstName (), "John"); assertEquals (registerUser.getCountry (), "US"); } } There were just seven fields in the example we took for registering the user. However, this would not be the case with every application. There would be some more additional fields required and as the fields keep on increasing every time, we would need to update the POJO class with respective Getter and Setter methods and also update the parameters in the constructor. Finally, we would need to add the values to those fields so the data could be passed in the actual field required. Long story short, we would need to update the code even if there is a single new field added, also, it doesn’t look clean to add values as parameters in the tests. Luckily, the Builder Design Pattern in Java comes to the rescue here. What Is Builder Design Pattern in Java? Builder design pattern is a creational design pattern that lets you construct complex objects step by step. The pattern allows you to produce different types and representations of an object using the same construction code. Builder Pattern helps us solve the issue of setting the parameters by providing a way to build the objects step by step by providing a method that returns the final object which can be used in the actual tests. What Is Lombok? Project Lombok is a Java library that automatically plugs into your editor and builds tools, spicing up your Java. It is an annotation-based Java library that helps in reducing the boilerplate code. It helps us in writing short and crisp code without having to write the boilerplate code. Bypassing the @Getterannotation over the class, it automatically generates Getter methods. Similarly, you don’t have to write the code for Setter methods as well, its @Setterannotation updated over the class automatically generates the Setter methods. It also has support for using the Builder design pattern so we just need to put the @Builderannotation above the class and the rest will be taken care of by the Lombok library. To use Lombok annotations in the project we need to add the following Maven dependency: <!-- https://mvnrepository.com/artifact/org.projectlombok/lombok --> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <version>1.18.32</version> <scope>provided</scope> </dependency> Using the Builder Design Pattern With Lombok Before we start refactoring the code we have written, let me tell you about the DataFaker library as well as how it helps in generating fake data that can be used for testing. Ideally, in our example, every newly registered user’s data should be unique otherwise we may get a duplicate data error and the test will fail. Here, the DataFaker library will help us in providing unique data in each test execution thereby helping us with registering a new user with unique data every time the registration test is run. To use the DataFaker library, we need to add the following Maven dependency to our project. XML <!-- https://mvnrepository.com/artifact/net.datafaker/datafaker --> <dependency> <groupId>net.datafaker</groupId> <artifactId>datafaker</artifactId> <version>2.2.2</version> </dependency> Now, let's start refactoring the code. First, we will make the changes to the RegisterUser class. We would be removing all the Getter and Setter methods and also the constructor and adding the @Getter and @Builder annotation tags on the top of the RegisterUser class. Here is how the RegisterUser class looks now after the refactoring Java @Getter @Builder public class RegisterUserWithBuilder { private String firstName; private String lastName; private String address; private String city; private String state; private String country; private String mobileNumber; } How clean and crisp it looks with that refactoring being done. Multiple lines of code are removed still it will still work in the same fashion as it used to earlier, thanks to Lombok. We would have to add a new Java class for generating the fake data on runtime using the Builder design pattern. We would be calling this new class the DataBuilder class. Java public class DataBuilder { private static final Faker FAKER = new Faker(); public static RegisterUserWithBuilder getUserData () { return RegisterUserWithBuilder.builder () .firstName (FAKER.name () .firstName ()) .lastName (FAKER.name () .lastName ()) .address (FAKER.address () .streetAddress ()) .state (FAKER.address () .state ()) .city (FAKER.address () .city ()) .country (FAKER.address () .country ()) .mobileNumber (String.valueOf (FAKER.number () .numberBetween (9990000000L, 9999999999L))) .build (); } } The getUserData() method will return the test data required for registering the user using the DataFaker library. Notice the builder() method used after the class name RegisterUserWithBuilder. It appears because of the @Builder annotation we have used on the top of the RegisterUserWithBuilder class. After the builder() method we have to pass on the variables we have declared in the RegisterUserWithBuilder class and accordingly, pass the fake data that we need to generate for the respective variables. Java RegisterUserWithBuilder.builder () .firstName (FAKER.name () .firstName ()); The above piece of code will generate a fake first name and set it in the first name variable. Likewise, we have set fake data in all other variables. Now, let’s move towards how we use these data in the tests. It's very simple, the below code snippet explains it all. Java @Test public void testRegisterUserWithBuilder () { RegisterUserWithBuilder registerUserWithBuilder = getUserData (); System.out.println (registerUserWithBuilder.getFirstName ()); System.out.println (registerUserWithBuilder.getLastName ()); System.out.println (registerUserWithBuilder.getAddress ()); System.out.println (registerUserWithBuilder.getCity ()); System.out.println (registerUserWithBuilder.getState ()); System.out.println (registerUserWithBuilder.getCountry ()); System.out.println (registerUserWithBuilder.getMobileNumber ()); } We just need to call the getUserData() method while instantiating the RegisterUserWithBuilder class. Next, we would be calling the Getter methods for the respective variables we declared inside the RegisterUserWithBuilder class. Remember we had passed the @Getter annotation on the top of the RegisterUserWithBuilder class, this actually helps in calling the Getter methods here. Also, we are not required to pass on multiple data as the constructor parameters for the RegisterUserWithBuilder class, instead we just need to instantiate the class and call the getUserData() method! How easy it is to generate the unique data and pass it on in the tests without having to write multiple lines of boilerplate codes. Thanks to the Builder design pattern and Lombok! Running the Test Let’s run the test and check if the user details get printed in the console. We can see that the fake data is generated successfully in the following screenshot of the test execution. Conclusion In this blog, we discussed making use of the builder design pattern in Java with Lombok and DataFaker library to generate fake test data on run time and use it in our automated tests. It would ease the test data generation process by eliminating the need to update the test data before running the tests. I hope it will help you a lot in reducing the code lines and writing your code in a much cleaner way. Happy Testing!
In the first part of our system design series, we introduced MarsExpress, a fictional startup tackling the challenge of scaling from a local entity to a global presence. We explored the initial steps of transitioning from a monolithic architecture to a scalable solution, setting the stage for future growth. As we continue this journey, Part 2 focuses on the critical role of the caching layer. We’ll delve into the technological strategies and architectural decisions essential for implementing effective caching, which are crucial for handling millions of users efficiently. architectural decisions pivotal for scaling to meet the demands of millions. Cache Layer For read-heavy applications, relying solely on a primary-replica database architecture often falls short of meeting the performance and scalability demands. While this architecture can improve read throughput by distributing read queries among replicas, it still encounters bottlenecks, especially under massive read load scenarios. This is where the implementation of a distributed cache layer becomes not just beneficial, but essential. A distributed cache, positioned in front of the database layer, can serve frequently accessed data with significantly lower latency than a database, dramatically reducing the load on the primary database and its replicas. By caching the results of read operations, applications can achieve instant data access for the majority of requests, leading to a more responsive user experience and higher throughput. Moreover, a distributed cache scales horizontally, offering a more flexible and cost-effective solution for managing read-heavy workloads compared to scaling databases vertically or adding more replicas. This approach not only alleviates pressure on the database but also ensures high availability and fault tolerance, as cached data is distributed across multiple nodes, minimizing the impact of individual node failures. In essence, for read-heavy applications aiming for scalability and high performance, incorporating a distributed cache layer is a critical strategy that complements and extends the capabilities of primary-replica database architectures. A key characteristic of distributed caches is their ability to handle massive amounts of data by partitioning it across multiple nodes. This approach, often implemented using consistent hashing, balances the load evenly and allows for easy scaling by adding or removing nodes. Additionally, replication ensures data redundancy, enhancing fault tolerance. For instance, Redis Cluster and Hazelcast are popular implementations that provide automatic data partitioning and failover. One of the primary benefits of distributed caches is their ability to significantly improve read performance. By caching frequently accessed data across multiple nodes, applications can serve data with minimal latency, bypassing the database for most read requests. This reduction in database load not only improves response times but also enhances overall system throughput. Furthermore, eviction policies like Least Recently Used (LRU) or Least Frequently Used (LFU) help manage memory efficiently by discarding less important data, ensuring the cache remains performant. However, implementing a distributed cache requires careful consideration of several factors. Network latency can be a critical issue, especially in geo-distributed setups, and must be minimized through strategic placement of cache nodes. Consistency models, ranging from eventual consistency to strong consistency, need to be chosen based on application requirements. Security is also paramount, necessitating encryption of data in transit and at rest, along with robust authentication and authorization mechanisms. Monitoring and metrics play a crucial role in maintaining a distributed cache. Tracking metrics such as hit/miss ratios, latency, and throughput helps identify performance bottlenecks and optimize the cache configuration. Regular monitoring ensures the cache operates efficiently, adapting to changing workloads and maintaining high availability. Distributed caches excel in environments with high read traffic, such as social media platforms, e-commerce sites, and real-time analytics systems. They are also effective in session storage, providing quick access to user session data across large-scale web applications. By leveraging distributed caches, engineers can significantly enhance the performance, scalability, and reliability of their systems, ensuring they can meet the demands of millions of users. Sharding and Horizontal Scaling Sharding and horizontal scaling are fundamental strategies in distributed systems to improve performance, scalability, and fault tolerance. Each approach addresses different aspects of data distribution and system growth: Sharding Shading involves dividing data into smaller subsets (shards) and distributing them across multiple nodes or databases. Each shard operates independently, handling a portion of the overall workload. Sharding enhances scalability by allowing distributed systems to manage larger datasets and higher transaction volumes effectively. Horizontal Scaling Horizontal scaling refers to adding more identical resources (e.g., servers, cache nodes) to a system to distribute workload and increase capacity. It aims to improve performance and accommodate growing demands by leveraging additional hardware resources in parallel. In distributed caching systems, sharding is crucial for efficiently managing data across multiple cache nodes. By partitioning data into shards and distributing them among cache servers, sharding enhances data locality and reduces contention for resources. Each cache node manages a subset of data, enabling parallel processing and improving overall throughput. For example, in a sharded Redis cluster, data keys are distributed across multiple Redis instances (shards), ensuring scalable read and write operations across the cache. On the other side, horizontal scaling complements sharding by allowing distributed caching systems to expand capacity seamlessly. Adding more cache nodes enhances system performance and accommodates increased data storage and access requirements. For instance, a horizontally scaled Memcached cluster can handle growing volumes of cached data and client requests by adding additional cache servers and distributing the workload evenly across nodes to maintain low-latency access. As you might read in Part 1, our fictitious MarsExpress (a local delivery startup based in Albuquerque), uses a distributed caching system to optimize delivery tracking and logistics operations. Here’s how sharding and horizontal scaling play crucial roles in their system. MarsExpress employs sharding in its distributed caching solution to manage real-time tracking data for delivery orders. The system partitions tracking data into geographical regions (shards), with each shard corresponding to deliveries in specific areas (e.g., downtown, suburbs). By distributing data across shards, MarsExpress ensures efficient access and updates to delivery statuses, minimizing latency and optimizing resource utilization. By dividing data into smaller subsets and distributing them among separate cache nodes, each responsible for a specific region, MarsExpress can optimize data access and update speeds. Previously, with a single caching server, latency averaged 20 milliseconds per request. After sharding, this latency can be significantly reduced to 10 milliseconds or less, as data relevant to each region is stored closer to where it is needed most. This approach not only enhances delivery tracking efficiency but also supports scalability as MarsExpress expands its service areas. As MarsExpress expands its delivery services to cover more neighborhoods and handle increasing delivery volumes, horizontal scaling becomes essential. They scale their distributed caching infrastructure horizontally by adding more cache nodes. Each new node enhances system capacity and performance, allowing MarsExpress to handle concurrent requests and store larger datasets without compromising delivery tracking accuracy or responsiveness. on the other hand, involves adding more identical cache nodes to the system to distribute workload and increase overall capacity. In this case, horizontal scaling plays a crucial role in accommodating increased transaction volumes and customer demands. Initially, MarsExpress might handle 5,000 delivery tracking updates per minute with a single caching server. By horizontally scaling and adding more nodes, this capacity can be doubled or even tripled, enabling the system to handle peak delivery periods without compromising performance. This scalability ensures that MarsExpress can maintain real-time visibility into delivery operations, providing customers with accurate tracking information and enhancing overall service reliability. In terms of fault tolerance and availability, the adoption of distributed caching strategies provides our system with improved resilience against system failures. Implementing sharding and maintaining redundant copies of data across multiple nodes, can minimize the risk of service disruptions. For instance, with sharding and redundant caching nodes in place, it can achieve uptime rates of 99.9% or higher. This high availability ensures that customers can track their deliveries seamlessly, even during unforeseen technical issues or maintenance activities. Moreover, the cost efficiency of MarsExpress’s operations is positively impacted by these caching strategies. Initially, operational costs associated with managing and scaling the caching infrastructure may be high due to limited capacity. However, through effective sharding and horizontal scaling, MarsExpress can optimize resource utilization and reduce overhead costs per transaction. In fact, optimizing resource usage through sharding can lead to a 30% reduction in operational costs, while horizontal scaling can further enhance cost efficiency by leveraging economies of scale and improving overall performance metrics Popular Distributed Caches Let’s explore Redis, Memcached, and Apache Ignite in practical scenarios to understand their strengths and use cases. Redis Redis is renowned for its versatility and speed in handling various types of data structures. It’s commonly used as a distributed cache due to its in-memory storage and support for data persistence. In practice, Redis excels in scenarios requiring fast read and write operations, such as session caching, real-time analytics, and leaderboard systems. Its ability to store not just simple key-value pairs but also lists, sets, and sorted sets makes it adaptable to a wide range of caching needs. Redis’s replication and clustering features enhance its resilience and scalability. In real-world applications, setting up Redis as a distributed cache involves configuring master-slave replication or using Redis Cluster for automatic sharding and high availability. These features ensure that even under heavy loads, Redis can maintain performance and reliability. Memcached Memcached is another popular choice for distributed caching, valued for its simplicity and speed. Unlike Redis, Memcached focuses solely on key-value caching without persistence. It’s highly optimized for fast data retrieval and is typically used to alleviate database load by caching frequently accessed data. In practical applications, Memcached shines in scenarios where data volatility isn’t a concern and where rapid access to cached items is critical, such as in web applications handling session data, API responses, and content caching. Its distributed nature allows scaling out by adding more nodes to the cluster, increasing caching capacity, and improving overall performance. Apache Ignite Apache Ignite combines in-memory data grid capabilities with distributed caching and processing. It’s often chosen for applications requiring both caching and computing capabilities in a single platform. In practice, Apache Ignite is used for distributed SQL queries, machine learning model training with cached data, and real-time analytics. What sets Apache Ignite apart is its ability to integrate with existing data sources like RDBMS, NoSQL databases, and Hadoop, making it suitable for hybrid data processing and caching scenarios. Its distributed nature ensures high availability and fault tolerance, critical for handling large-scale datasets and processing complex queries across a cluster. When selecting a distributed cache, practical considerations such as ease of integration, operational overhead, and community support often play a crucial role. From an operational standpoint, configuring and monitoring distributed caches requires expertise in managing clusters, handling failover scenarios, and optimizing cache eviction policies to ensure efficient memory usage. In fact, understanding the trade-offs between consistency, availability, and partition tolerance (CAP theorem) is essential. Distributed caches like Redis and Memcached prioritize availability and partition tolerance, making them suitable for use cases where immediate access to cached data is paramount. Apache Ignite, with its focus on consistency and integration with other data processing frameworks, appeals to applications needing unified data management and computation. Ultimately, the choice of a distributed cache depends on specific application requirements, performance goals, and the operational expertise available. Each of these caches brings unique strengths and trade-offs, making them valuable tools in modern distributed computing environments. Caching Policies Effective caching policies are crucial for optimizing distributed cache performance and reliability. Studies indicate that implementing appropriate caching strategies can reduce database load by up to 70% and improve response times by 80%, significantly enhancing user experience and system efficiency. To illustrate these strategies, let’s revisit MarsExpress, our fictitious startup aiming for global scalability. As MarsExpress expanded, it faced increased load and latency issues, particularly with read-heavy operations. The team implemented several caching policies to address these challenges. Cache-Aside (Lazy Loading) We used this policy to minimize initial load times. When a user requested data not in the cache, the system fetched it from the database, cached it, and returned the result. For example, when users frequently accessed the latest mission updates, the first request after a cache miss would be slightly slower, but subsequent requests were instant. This reduced direct database queries and ensured that frequently accessed data was readily available. For example, Facebook employs cache-aside to manage its massive scale. Frequently accessed user data, like profile information, is fetched from the database upon cache misses, and then cached for subsequent requests. This reduces database load and speeds up response times for users. This approach is preferred when application data access patterns are unpredictable. It ensures that only necessary data is cached, optimizing memory usage and reducing unnecessary cache population. Read-Through To streamline data access, we configured our cache to query the database on a cache miss directly. This approach simplified application logic and ensured that data in the cache was always up-to-date, reducing the complexity of manually managing cache refreshes. For instance, when users looked up historical mission data, the cache would fetch the latest data if not already available, ensuring consistency. Netflix uses read-through caching for its recommendation engine. When a cache miss occurs, the system fetches the latest recommendations from the database and updates the cache, ensuring users always see the most current data. Read-through is better for applications where data consistency is critical, and frequent database updates are needed. It simplifies the development process by abstracting the caching layer from the application code. Write-Through Ensuring data consistency was critical for MarsExpress, especially for transactions. By writing data to the cache and database simultaneously, they maintained synchronization, ensuring users always had access to the most current information without added complexity. This was crucial for real-time telemetry data, where accuracy was paramount. Financial institutions often use write-through caching to ensure transactional data consistency. Every write operation updates both the cache and the database, guaranteeing that cached data is always synchronized with the underlying data store. This policy is ideal for applications requiring strong consistency and immediate data propagation, ensuring that the cache and database remain in sync. Write-Behind (Write-Back) To optimize write performance, MarsExpress adopted a write-behind policy for non-critical data, such as user activity logs. This allowed the cache to handle writes quickly and batch database updates asynchronously, reducing write latency and database load. For example, user feedback and interaction logs were cached and later written to the database in batches, ensuring the system remained responsive. E-commerce platforms like Amazon use write-behind caching for logging user activities and interactions. This ensures fast write performance and reduces the immediate load on the database. This policy is preferred for applications where high write throughput is needed, and eventual consistency is acceptable. It improves performance by deferring database updates. Refresh-Ahead Anticipating user behavior, MarsExpress used refresh-ahead to update cache entries before they expired. By predicting which data would be requested next, they ensured that users experienced minimal latency, particularly during peak times. This was particularly useful for scheduled data releases, where the cache preloaded updates right before they went live. News websites use refresh-ahead to keep their front-page articles updated. By preloading anticipated popular articles, they ensure minimal latency when users access the latest news. This strategy is useful for applications with predictable access patterns. It ensures that frequently accessed data is always fresh, reducing latency during peak access times. Eviction Policies: Ensuring Optimal Cache Performance Managing cache memory efficiently is critical for maintaining high performance and responsiveness. Eviction policies determine which data to remove when the cache reaches its capacity, ensuring that the most relevant data remains accessible. Least Recently Used (LRU) MarsExpress implemented the LRU eviction policy to manage its high volume of data. This policy evicts the least recently accessed items, ensuring that frequently accessed data remains in the cache. For instance, older telemetry data was evicted in favor of newer, more relevant data. Twitter uses LRU eviction to manage tweet caches. Older, less accessed tweets are evicted to make room for new ones, ensuring the cache contains the most relevant data. LRU is effective in scenarios where recently accessed data is likely to be accessed again. It optimizes cache usage by retaining the most relevant data, making it ideal for applications with access patterns that favor recency. Least Frequently Used (LFU) In contrast to LRU, the LFU policy evicts items that are accessed least often. MarsExpress considered LFU for its user profile cache, ensuring that popular profiles remained cached while infrequently accessed profiles were evicted. Content delivery networks (CDNs) often use LFU to manage cached content, ensuring that popular content remains available to users while less popular content is evicted. LFU is beneficial for applications where certain data is accessed repeatedly over a long period. It ensures that the most popular data remains in the cache, optimizing for long-term access patterns. Time-To-Live (TTL) MarsExpress utilized TTL settings to automatically expire stale data. Each cache entry had a defined lifespan, after which it was removed from the cache, ensuring that outdated information did not linger. Online retail platforms like Shopify use TTL to keep product availability and pricing information current. Changes in inventory or price immediately invalidate outdated cache entries. TTL is crucial for applications where data freshness is vital. It ensures that the cache reflects the most current data, reducing the risk of serving stale information. TTL is particularly useful in dynamic environments where data changes frequently. Custom Eviction Policies MarsExpress experimented with custom eviction policies tailored to specific application needs. For example, they combined LRU with TTL for their mission data cache, ensuring both recency and freshness were maintained. Google uses custom eviction policies for its search index, balancing freshness and relevance to provide the most accurate search results. Custom policies offer flexibility to address unique application requirements. They can combine elements of different eviction strategies to optimize cache performance based on specific data access patterns and business needs. By carefully selecting and implementing these eviction policies, MarsExpress ensured that its cache remained performant and responsive, even as data volumes grew. These strategies not only improved system performance but also enhanced the overall user experience, showcasing the importance of well-implemented eviction policies in large-scale system design. Conclusion As MarsExpress continues to evolve and meet the demands of millions, the integration of a distributed caching layer has proven to be pivotal. By strategically employing sharding, horizontal scaling, and carefully chosen caching policies, MarsExpress has optimized performance, enhanced scalability, and ensured data consistency and availability. These strategies have not only improved user experience but have also demonstrated the critical role of distributed caching in modern system design. In Part 3 of our series, we will explore the transition to microservices, delving into how breaking down applications into smaller, independent services can further enhance scalability, resilience, and flexibility. Stay tuned as we continue to guide MarsExpress on its journey to mastering system design.
Organizations heavily rely on data analysis and automation to drive operational efficiency. In this piece, we will look into the basics of data analysis and automation with examples done in Python which is a high-level programming language used for general-purpose programming. What Is Data Analysis? Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data so as to identify useful information, draw conclusions, and support decision-making. It is an essential activity that helps in transforming raw data into actionable insights. The following are key steps involved in data analysis: Collecting: Gathering data from different sources. Cleaning: Removing or correcting inaccuracies and inconsistencies contained in the collected dataset. Transformation: Converting the collected dataset into a format that is suitable for further analysis. Modeling: Applying statistical or machine learning models on the transformed dataset. Visualization: Representing the findings visually by creating charts, and graphs among others using suitable tools such as MS Excel or Python's matplotlib library. The Significance of Data Automation Data automation involves the use of technology to execute repetitive tasks associated with handling large datasets with minimal human intervention required. Automating these processes can greatly improve their efficiency thereby saving time for analysts who can then focus more on complex duties. Some common areas where it’s employed include: Data ingestion: Automatically collecting and storing data from various sources. Data cleaning and transformation: Using scripts or tools (e.g., Python Pandas library) for preprocessing the collected dataset before performing other operations on it like modeling or visualization. Report generation: Creating automated reports or dashboards that update themselves whenever new records arrive at our system etcetera. Data integration: Combining information obtained from multiple sources so as to get a holistic view when analyzing it further down during the decision-making process. Introduction to Python for Data Analysis Python is a widely used programming language for data analysis due to its simplicity, readability, and vast libraries available for statistical computing. Here are some simple examples that demonstrate how one can read large datasets as well as perform basic analysis using Python: Reading Large Datasets Reading datasets into your environment is one of the initial stages in any data analysis project. For this case, we will need the Pandas library which provides powerful data manipulation and analysis tools. Python import pandas as pd # Define the file path to the large dataset file_path = 'path/to/large_dataset.csv' # Specify the chunk size (number of rows per chunk) chunk_size = 100000 # Initialize an empty list to store the results results = [] # Iterate over the dataset in chunks for chunk in pd.read_csv(file_path, chunksize=chunk_size): # Perform basic analysis on each chunk # Example: Calculate the mean of a specific column chunk_mean = chunk['column_name'].mean() results.append(chunk_mean) # Calculate the overall mean from the results of each chunk overall_mean = sum(results) / len(results) print(f'Overall mean of column_name: {overall_mean}') Basic Data Analysis Once you have loaded the data, it is important to conduct some preliminary examination on it so as to familiarize yourself with its contents. Performing Aggregated Analysis There are times you might wish to perform a more advanced aggregated analysis over the entire dataset. For instance, let’s say we want to find the sum of a particular column across the whole dataset by processing it in chunks. Python # Initialize a variable to store the cumulative sum cumulative_sum = 0 # Iterate over the dataset in chunks for chunk in pd.read_csv(file_path, chunksize=chunk_size): # Calculate the sum of the specific column for the current chunk chunk_sum = chunk['column_name'].sum() cumulative_sum += chunk_sum print(f'Cumulative sum of column_name: {cumulative_sum}') Missing Values Treatment in Chunks It is common for missing values to exist during data preprocessing. Instead, here is an instance where missing values are filled using the mean of each chunk. Python # Initialize an empty DataFrame to store processed chunks processed_chunks = [] # Iterate over the dataset in chunks for chunk in pd.read_csv(file_path, chunksize=chunk_size): # Fill missing values with the mean of the chunk chunk.fillna(chunk.mean(), inplace=True) processed_chunks.append(chunk) # Concatenate all processed chunks into a single DataFrame processed_data = pd.concat(processed_chunks, axis=0) print(processed_data.head()) Final Statistics From Chunks At times, there is a need to get overall statistics from all chunks. This example illustrates how to compute the average and standard deviation of an entire column by aggregating outcomes from each chunk. Python import numpy as np # Initialize variables to store the cumulative sum and count cumulative_sum = 0 cumulative_count = 0 squared_sum = 0 # Iterate over the dataset in chunks for chunk in pd.read_csv(file_path, chunksize=chunk_size): # Calculate the sum and count for the current chunk chunk_sum = chunk['column_name'].sum() chunk_count = chunk['column_name'].count() chunk_squared_sum = (chunk['column_name'] ** 2).sum() cumulative_sum += chunk_sum cumulative_count += chunk_count squared_sum += chunk_squared_sum # Calculate the mean and standard deviation overall_mean = cumulative_sum / cumulative_count overall_std = np.sqrt((squared_sum / cumulative_count) - (overall_mean ** 2)) print(f'Overall mean of column_name: {overall_mean}') print(f'Overall standard deviation of column_name: {overall_std}') Conclusion Reading large datasets in chunks using Python helps in efficient data processing and analysis without overwhelming system memory. By taking advantage of Pandas’ chunking functionality, various tasks involving data analytics can be done on large datasets while ensuring scalability and efficiency. The provided examples illustrate how to read large datasets in portions, address missing values, and perform aggregated analysis; thus providing a strong foundation for working with huge amounts of data in Python.
In today's data-driven world, organizations rely heavily on the efficient processing and analysis of vast amounts of data to gain insights and make informed decisions. At the heart of this capability lies the data pipeline — a crucial component of modern data infrastructure. A data pipeline serves as a conduit for the seamless movement of data from various sources to designated destinations, facilitating its transformation, processing, and storage along the way. The data pipeline architecture diagram above depicts a data pipeline architecture, showcasing the flow of data from diverse sources such as databases, flat files, and application and streaming data. The data travels through various stages of processing, including ingestion, transformation, processing, storage, and consumption, before reaching its final destination. This visual representation highlights how the data pipeline facilitates the efficient movement of data, ensuring its integrity, reliability, and accessibility throughout the process. What Is Data Pipeline Architecture? Data pipeline architecture encompasses the structural design and framework employed to orchestrate the flow of data through various components, stages, and technologies. This framework ensures the integrity, reliability, and scalability of data processing workflows, enabling organizations to derive valuable insights efficiently. Importance of Data Pipeline Architecture Data pipeline architecture is vital for integrating data from various sources, ensuring its quality and optimizing processing efficiency. It enables scalability to handle large volumes of data and supports real-time processing for timely insights. Flexible architectures adapt to changing needs, while governance features ensure compliance and security. Ultimately, data pipeline architecture enables organizations to derive value from their data assets efficiently and reliably. Evolution of Data Pipeline Architecture Historically, data processing involved manual extraction, transformation, and loading (ETL) tasks performed by human operators. These processes were time-consuming, error-prone, and limited in scalability. However, with the emergence of computing technologies, early ETL tools began automating and streamlining data processing workflows. As the volume, velocity, and variety of data increased, there was a growing need for real-time data processing capabilities. This led to the development of stream processing frameworks and technologies, enabling continuous ingestion and analysis of data streams. Additionally, the rise of cloud computing introduced new paradigms for data processing, storage, and analytics. Cloud-based data pipeline architectures offered scalability, flexibility, and cost-efficiency, leveraging managed services and serverless computing models. With the proliferation of artificial intelligence (AI) and machine learning (ML) technologies, data pipeline architectures evolved to incorporate advanced analytics, predictive modeling, and automated decision-making capabilities. As data privacy regulations and compliance requirements became more stringent, data pipeline architectures evolved to prioritize data governance, security, and compliance, ensuring the protection and privacy of sensitive information. Today, data pipeline architecture continues to evolve in response to advancements in technology, changes in business requirements, and shifts in market dynamics. Organizations increasingly adopt modern, cloud-native architectures that prioritize agility, scalability, and automation, enabling them to harness the full potential of data for driving insights, innovation, and competitive advantage. Components of a Data Pipeline Architecture A robust data pipeline architecture comprises several interconnected components, each fulfilling a pivotal role in the data processing workflow: Component Definition Examples Data sources Data sources serve as the starting point of the pipeline where raw data originates from various channels. Databases (SQL, NoSQL) Applications (CRM, ERP, etc.) IoT devices Sensors External APIs Data processing engines Processing engines transform and process raw data into a usable format, performing tasks such as data cleansing, enrichment, aggregation, and analysis. Batch processing engines Apache Spark Apache Hadoop Stream processing engines Apache Flink Apache Kafka Streams Storage systems Storage systems provide the infrastructure for storing both raw and processed data, offering scalability, durability, and accessibility for storing vast amounts of data. Data warehouses Amazon Redshift Google BigQuery Snowflake Data lakes Apache Hadoop AWS S3 Google Cloud Storage Data destinations Data destinations are the final endpoints where processed data is stored or consumed by downstream applications, analytics tools, or machine learning models. Data warehouses Analytical databases Machine learning platforms TensorFlow PyTorch Data visualization and BI tools Tableau Power BI Orchestration tools Data pipeline orchestration tools manage the flow and execution of data pipelines, ensuring that data is processed, transformed, and moved efficiently through the pipeline. These tools provide scheduling, monitoring, and error-handling capabilities. Apache Airflow Apache NiFi AWS Data Pipeline Google Cloud Composer Monitoring & logging Monitoring and logging components track the health, performance, and execution of data pipelines, offering visibility into pipeline activities, identifying bottlenecks, and troubleshooting issues. ELK stack (Elasticsearch, Logstash, Kibana) Grafana Splunk Cloud monitoring services (e.g., AWS CloudWatch, Google Cloud Monitoring) Six Stages of a Data Pipeline Data processing within a pipeline travels through several stages, each contributing to the transformation and refinement of data. The stages of a data pipeline represent the sequential steps through which data flows — from its ingestion in raw form to its storage or consumption in a processed format. Here are the key stages of a data pipeline: STAGE Definition Use Cases Data ingestion Involves capturing and importing raw data from various sources into the pipeline. Collecting data from diverse sources such as databases, applications, IoT devices, sensors, logs, or external APIs. Extracting data in its raw format without any transformations. Validating and sanitizing incoming data to ensure its integrity and consistency. Data transformation Involves cleansing, enriching, and restructuring raw data to prepare it for further processing and analysis. Cleansing data by removing duplicates, correcting errors, and handling missing values. Enriching data by adding contextual information, performing calculations, or joining with external datasets. Restructuring data into a standardized format suitable for downstream processing and analysis. Data processing Encompasses the computational tasks performed on transformed data to derive insights, perform analytics, or generate actionable outputs. Performing various analytical tasks such as aggregation, filtering, sorting, and statistical analysis. Applying machine learning algorithms for predictive modeling, anomaly detection, or classification. Generating visualizations, reports, or dashboards to communicate insights and findings. Data storage Involves persisting processed data in designated storage systems for future retrieval, analysis, or archival purposes. Storing processed data in data lakes, data warehouses, or analytical databases. Organizing data into structured schemas or formats optimized for query performance. Implementing data retention policies to manage the lifecycle of stored data and ensure compliance with regulatory requirements. Data movement Refers to the transfer of data between different storage systems, applications, or environments within the data pipeline. Moving data between on-premises and cloud environments. Replicating data across distributed systems for redundancy or disaster recovery purposes. Streaming data in real time to enable continuous processing and analysis. Data consumption Involves accessing, analyzing, and deriving insights from processed data for decision-making or operational purposes. Querying data using analytics tools, SQL queries, or programming languages like Python or R. Visualizing data through dashboards, charts, or reports to facilitate data-driven decision-making. Integrating data into downstream applications, business processes, or machine learning models for automation or optimization. By traversing through these stages, raw data undergoes a systematic transformation journey, culminating in valuable insights and actionable outputs that drive business outcomes and innovation. Data Pipeline Architecture Designs Several architectural designs cater to diverse data processing requirements and use cases, including: ETL (Extract, Transform, Load) ETL architectures have evolved to become more scalable and flexible, with the adoption of cloud-based ETL tools and services. Additionally, there's been a shift towards real-time or near-real-time ETL processing to enable faster insights and decision-making. Benefits: Well-established and mature technology. Suitable for complex transformations and batch processing. Handles large volumes of data efficiently. Challenges: Longer processing times for large data sets. Requires significant upfront planning and design. Not ideal for real-time analytics or streaming data. ELT (Extract, Load, Transform) ELT architectures have gained popularity with the advent of cloud-based data warehouses like Snowflake and Google BigQuery, which offer native support for performing complex transformations within the warehouse itself. Additionally, ELT pipelines have become more scalable and cost-effective due to advancements in cloud computing. Benefits: Simplifies the data pipeline by leveraging the processing power of the target data warehouse. Allows for greater flexibility and agility in data processing. Well-suited for cloud-based environments and scalable workloads. Challenges: May lead to increased storage costs due to storing raw data in the target data warehouse. Requires careful management of data quality and governance within the target system. Not ideal for complex transformations or scenarios with high data latency requirements. Streaming Architectures Streaming architectures have evolved to handle large data volumes and support more sophisticated processing operations. They have integrated with stream processing frameworks and cloud services for scalability and fault tolerance. Benefits: Enables real-time insights and decision-making. Handles high-volume data streams with low latency. Supports continuous processing and analysis of live data. Challenges: Requires specialized expertise in stream processing technologies. May incur higher operational costs for maintaining real-time infrastructure. Complex event processing and windowing can introduce additional latency and complexity. Zero ETL Zero ETL architectures have evolved to support efficient data lake storage and processing frameworks. They have integrated with tools for schema-on-read and late-binding schema to enable flexible data exploration and analysis. Benefits: Simplifies data ingestion and storage by avoiding upfront transformations. Enables agility and flexibility in data processing. Reduces storage costs by storing raw data in its native format. Challenges: May lead to increased query latency for complex transformations. Requires careful management of schema evolution and data governance. Not suitable for scenarios requiring extensive data preparation or complex transformations. Data Sharing Data sharing architectures have evolved to support secure data exchange across distributed environments. They have integrated with encryption, authentication, and access control mechanisms for enhanced security and compliance. Benefits: Enables collaboration and data monetization opportunities. Facilitates real-time data exchange and integration. Supports fine-grained access control and data governance. Challenges: Requires robust security measures to protect sensitive data. Complex integration and governance challenges across organizations. Potential regulatory and compliance hurdles in sharing sensitive data. Each architecture has its own unique characteristics, benefits, and challenges, enabling organizations to choose the most suitable design based on their specific requirements and preferences. How to Choose a Data Pipeline Architecture Choosing the right data pipeline architecture is crucial for ensuring the efficiency, scalability, and reliability of data processing workflows. Organizations can follow these steps to select the most suitable architecture for their needs: 1. Assess Data Processing Needs Determine the volume of data you need to process. Are you dealing with large-scale batch processing or real-time streaming data? Consider the types of data you'll be processing. Is it structured, semi-structured, or unstructured data? Evaluate the speed at which data is generated and needs to be processed. Do you require real-time processing, or can you afford batch processing? Evaluate the accuracy and reliability of your data. Are there any data integrity concerns that should be resolved prior to processing? 2. Understand Use Cases Identify the types of analyses you need to perform on your data. Do you need simple aggregations, complex transformations, or predictive analytics? Determine the acceptable latency for processing your data. Is real-time processing critical for your use case, or can you tolerate some delay? Consider the integration with other systems or applications. Do you need to integrate with specific cloud services, databases, or analytics platforms Based on your requirements, use cases, and considerations regarding scalability, cost, complexity, and latency, it is essential to determine the appropriate architecture design. Evaluate the above discussed architectural designs and select the one that aligns best with your needs and objectives. It is crucial to choose an architecture that is flexible, scalable, cost-effective, and capable of meeting both current and future data processing requirements. 3. Consider Scalability and Cost Evaluate the scalability of the chosen architecture to handle growing data volumes and processing requirements. Ensure the architecture can scale horizontally or vertically as needed. Assess the cost implications of the chosen architecture, including infrastructure costs, licensing fees, and operational expenses. Choose an architecture that meets your performance requirements while staying within budget constraints. 4. Factor in Operational Considerations Consider the operational complexity of implementing and managing the chosen architecture. Ensure you have the necessary skills and resources to deploy, monitor, and maintain the pipeline. Evaluate the reliability and fault tolerance mechanisms built into the architecture. Ensure the pipeline can recover gracefully from failures and handle unexpected errors without data loss. 5. Future-Proof Your Decision Choose an architecture that offers flexibility to adapt to future changes in your data processing needs and technology landscape. Ensure the chosen architecture is compatible with your existing infrastructure, tools, and workflows. Avoid lock-in to proprietary technologies or vendor-specific solutions. By carefully considering data volume, variety, velocity, quality, use cases, scalability, cost, and operational considerations, organizations can choose a data pipeline architecture that best aligns with their objectives and sets them up for success in their data processing endeavors. Best Practices for Data Pipeline Architectures To ensure the effectiveness and reliability of data pipeline architectures, organizations should adhere to the following best practices: Modularize workflows: Break down complex pipelines into smaller, reusable components or modules for enhanced flexibility, scalability, and maintainability. Implement error handling: Design robust error handling mechanisms to gracefully handle failures, retries, and data inconsistencies, ensuring data integrity and reliability. Optimize storage and processing: Strive to strike a balance between cost-effectiveness and performance by optimizing data storage and processing resources through partitioning, compression, and indexing techniques. Ensure security and compliance: Uphold stringent security measures and regulatory compliance standards to safeguard sensitive data and ensure privacy, integrity, and confidentiality throughout the pipeline. Continuous monitoring and optimization: Embrace a culture of continuous improvement by regularly monitoring pipeline performance metrics, identifying bottlenecks, and fine-tuning configurations to optimize resource utilization, minimize latency, and enhance overall efficiency. By embracing these best practices, organizations can design and implement robust, scalable, and future-proof data pipeline architectures that drive insights, innovation, and strategic decision-making. Real World Use Cases and Applications In various industries, data pipeline architecture serves as a foundational element for deriving insights, enhancing decision-making, and delivering value to organizations. Let's explore some exemplary use cases across healthcare and financial services domains: Healthcare Healthcare domain encompasses various organizations, professionals, and systems dedicated to maintaining and improving the health and well-being of individuals and communities. Electronic Health Records (EHR) Integration Imagine a scenario where a hospital network implements a data pipeline architecture to consolidate EHRs from various sources, such as inpatient and outpatient systems, clinics, and specialty departments. This integrated data repository empowers clinicians and healthcare providers with access to comprehensive patient profiles, streamlining care coordination and facilitating informed treatment decisions. For example, during emergency department visits, the data pipeline retrieves relevant medical history, aiding clinicians in diagnosing and treating patients more accurately and promptly. Remote Patient Monitoring (RPM) A telemedicine platform relies on data pipeline architecture to collect and analyze RPM data obtained from wearable sensors, IoT devices, and mobile health apps. Real-time streaming of physiological metrics like heart rate, blood pressure, glucose levels, and activity patterns to a cloud-based analytics platform enables healthcare providers to remotely monitor patient health status. Timely intervention can be initiated to prevent complications, such as alerts for abnormal heart rhythms or sudden changes in blood glucose levels, prompting adjustments in medication or teleconsultations. Financial Services Financial services domain encompasses institutions, products, and services involved in managing and allocating financial resources, facilitating transactions, and mitigating financial risks. Fraud Detection and Prevention A leading bank deploys data pipeline architecture to detect and prevent fraudulent transactions in real-time. By ingesting transactional data from banking systems, credit card transactions, and external sources, the data pipeline applies machine learning models and anomaly detection algorithms to identify suspicious activities. For instance, deviations from a customer's typical spending behavior, such as transactions from unfamiliar locations or unusually large amounts, trigger alerts for further investigation, enabling proactive fraud prevention measures. Customer Segmentation and Personalization In the retail banking sector, data pipeline architecture is utilized to analyze customer data for segmentation and personalization of banking services and marketing campaigns. By aggregating transaction history, demographic information, and online interactions, the data pipeline segments customers into distinct groups based on their financial needs, preferences, and behaviors. For example, high-net-worth individuals can be identified for personalized wealth management services, or relevant product recommendations can be made based on past purchasing behavior, enhancing customer satisfaction and loyalty. In conclusion, the data pipeline architecture examples provided underscore the transformative impact of data pipeline architecture across healthcare and financial services industries. By harnessing the power of data, organizations can drive innovation, optimize operations, and gain a competitive edge in their respective sectors. Future Trends in Data Pipeline Architecture As technology continues to evolve, several emerging trends are reshaping the future landscape of data pipeline architecture, including: Serverless and microservices: The ascendancy of serverless computing and microservices architectures for crafting more agile, scalable, and cost-effective data pipelines. AI and ML integration: The convergence of artificial intelligence (AI) and machine learning (ML) capabilities into data pipelines for automating data processing, analysis, and decision-making, thereby unlocking new realms of predictive insights and prescriptive actions. Blockchain: The integration of blockchain technology to fortify data security, integrity, and transparency, particularly in scenarios involving sensitive or confidential data sharing and transactions. Edge computing: This involves processing data closer to the source of data generation, such as IoT devices, sensors, or mobile devices, rather than in centralized data centers. These trends signify the evolving nature of data pipeline architecture, driven by technological innovation, evolving business needs, and shifting market dynamics. By embracing these trends, organizations can stay ahead of the curve and leverage data pipeline architecture to unlock new insights, optimize operations, and drive competitive advantage in an increasingly data-driven world. Conclusion In conclusion, data pipeline architecture serves as the backbone of modern data infrastructure, empowering organizations to harness the transformative potential of data for driving insights, innovation, and strategic decision-making. By embracing the principles of modularity, error handling, optimization, security, and continuous improvement, businesses can design and implement robust, scalable, and future-proof data pipeline architectures that navigate the complexities of today's data-driven landscape with aplomb, propelling them toward sustained success and competitive advantage in this digital age.
Advanced SQL is an indispensable tool for retrieving, analyzing, and manipulating substantial datasets in a structured and efficient manner. It is extensively utilized in data analysis and business intelligence, as well as in various domains such as software development, finance, and marketing. Mastering advanced SQL can empower you to: Efficiently retrieve and analyze large datasets from databases. Create intricate reports and visualizations to derive meaningful insights from your data. Write optimized queries to enhance the performance of your database. Utilize advanced features such as window functions, common table expressions, and recursive queries. Understand and fine-tune the performance of your database. Explore, analyze, and derive insights from data more effectively. Provide data-driven insights and make decisions based on solid evidence. In today's data-driven landscape, the ability to handle and interpret big data is increasingly vital. Proficiency in advanced SQL can render you a valuable asset to any organization that manages substantial amounts of data. Below are some examples of advanced SQL queries that illustrate the utilization of complex and powerful SQL features: Using Subqueries in the SELECT Clause SQL SELECT customers.name, (SELECT SUM(amount) FROM orders WHERE orders.customer_id = customers.id) AS total_spent FROM customers ORDER BY total_spent DESC; This query employs a subquery in the SELECT clause to compute the total amount spent by each customer, returning a list of customers along with their total spending, ordered in descending order. Using the WITH Clause for Common Table Expressions (CTEs) SQL WITH top_customers AS (SELECT customer_id, SUM(amount) AS total_spent FROM orders GROUP BY customer_id ORDER BY total_spent DESC LIMIT 10), customer_info AS (SELECT id, name, email FROM customers) SELECT customer_info.name, customer_info.email, top_customers.total_spent FROM top_customers JOIN customer_info ON top_customers.customer_id = customer_info.id; This query uses the WITH clause to define two CTEs, "top_customers" and "customer_info" which simplifies and modularizes the query. The first CTE identifies the top 10 customers based on their total spending, and the second CTE retrieves customer information. The final result is obtained by joining these two CTEs. Using Window Functions To Calculate Running Totals SQL SELECT name, amount, SUM(amount) OVER (PARTITION BY name ORDER BY date) AS running_total FROM transactions ORDER BY name, date; This query utilizes a window function,`SUM(amount) OVER (PARTITION BY name ORDER BY date)`, to calculate the running total of transactions for each name. It returns all transactions along with the running total for each name, ordered by name and date. Using Self-Join SQL SELECT e1.name AS employee, e2.name AS manager FROM employees e1 JOIN employees e2 ON e1.manager_id = e2.id; This query employs a self-join to link a table to itself, illustrating the relationship between employees and their managers. It returns a list of all employees and their corresponding managers. Using JOIN, GROUP BY, HAVING SQL SELECT orders.product_id, SUM(order_items.quantity) AS product_sold, products.name FROM orders JOIN order_items ON orders.id = order_items.order_id JOIN products ON products.id = order_items.product_id GROUP BY orders.product_id HAVING SUM(order_items.quantity) > 100; This query uses JOIN to combine the orders and order_items tables on the order_id column, and joins with the product table on the product_id column. It then uses the GROUP BY clause to group results by product_id and the HAVING clause to filter products with more than 100 units sold. The SELECT clause lists the product_id, total quantity sold, and product name. Using COUNT() and GROUP BY SQL SELECT department, COUNT(employee_id) AS total_employees FROM employees GROUP BY department ORDER BY total_employees DESC; This query uses the COUNT() function to tally the number of employees in each department and the GROUP BY clause to group results by department. The SELECT clause lists the department name and total number of employees, ordered by total employees in descending order. Using UNION and ORDER BY SQL (SELECT id, name, 'customer' AS type FROM customers) UNION (SELECT id, name, 'employee' AS type FROM employees) ORDER BY name; This query uses the UNION operator to combine the results of two separate SELECT statements—one for customers and one for employees — and orders the final result set by name. The UNION operator removes duplicates if present. Recursive Queries A recursive query employs a self-referencing mechanism to perform tasks, such as traversing a hierarchical data structure like a tree or graph. Example: SQL WITH RECURSIVE ancestors (id, parent_id, name) AS ( -- Anchor query to select the starting node SELECT id, parent_id, name FROM nodes WHERE id = 5 UNION -- Recursive query to select the parent of each node SELECT nodes.id, nodes.parent_id, nodes.name FROM nodes JOIN ancestors ON nodes.id = ancestors.parent_id ) SELECT * FROM ancestors; This query uses a CTE called "ancestors" to define the recursive query with columns: id, parent_id, and name. The anchor query selects the starting node (id = 5), and the recursive query selects each node's parent, joining it with the "ancestors" CTE on the parent_id column. This process continues until the root of the tree is reached or the maximum recursion level is attained. The final query retrieves all identified ancestors. While recursive queries are potent, they can be resource-intensive; therefore, they should be used judiciously to avoid performance issues. Ensure proper recursion termination and consider the maximum recursion level permitted by your DBMS. Not all SQL implementations support recursion, but major RDBMS systems such as PostgreSQL, Oracle, SQL Server, and SQLite do support recursive queries using the WITH RECURSIVE keyword. These examples showcase just a few of SQL's powerful capabilities and the diverse types of queries you can construct. The specific details of the queries will depend on your database structure and the information you seek to retrieve, but these examples should provide a foundational understanding of what is achievable with advanced SQL.
When it comes to data integration, some people may wonder what there is to discuss — isn't it just ETL? That is, extracting from various databases, transforming, and ultimately loading into different data warehouses. However, with the rise of big data, data lakes, real-time data warehouses, and large-scale models, the architecture of data integration has evolved from the ETL of the data warehouse era to the ELT of the big data era and now to the current stage of EtLT. In the global tech landscape, emerging EtLT companies like FiveTran, Airbyte, and Matillion have emerged, while giants like IBM have invested $2.3 billion in acquiring StreamSets and webMethods to upgrade their product lines from ETL to EtLT (DataOps). Whether you're a data professional or a manager, it's crucial to re-evaluate the recent changes and future trends in data integration. Chapter 1: From ETL to ELT, to EtLT When it comes to data integration, many in the industry may think it's just about ETL. However, with the rise of big data, data lakes, real-time data warehouses, and large-scale models, the architecture of data integration has evolved from the ETL of the data warehouse era to the ELT of the big data era and now to the EtLT. Globally, new emerging EtLT companies like FiveTran, Airbyte, and Matllion have been established, while established players like IBM are upgrading their product lines from ETL to EtLT (DataOps) with offerings such as StreamSet and webMethods. Whether you're a manager in an enterprise or a professional in the data field, it's essential to re-examine the changes in data integration in recent times and future trends. ETL Architecture Most experts in the data field are familiar with the term ETL. During the heyday of data warehousing, ETL tools like IBM DataStage, Informatica, Talend, and Kettle were popular. Some companies still use these tools to extract data from various databases, transform it, and load it into different data warehouses for reporting and analysis. The pros and cons of the ETL architecture are as follows: Advantages of ETL Architecture Data consistency and quality Integration of complex data sources Clear technical architecture Implementation of business rules Disadvantages of ETL Architecture Lack of real-time processing High hardware costs Limited flexibility Maintenance costs Limited handling of unstructured data ELT Architecture With the advent of the big data era, facing the challenges of ETL's inability to load complex data sources and its poor real-time performance, a variant of ETL architecture, ELT, emerged. Companies started using ELT tools provided by various data warehousing vendors, such as Teradata's BETQ/Fastload/TPT and Hadoop Hive's Apache Sqoop. The characteristics of ELT architecture include directly loading data into data warehouses or big data platforms without complex transformations and then using SQL or H-SQL to process the data. The pros and cons of the ELT architecture are as follows: Advantages of ELT Architecture Handling large data volumes Improved development and operational efficiency Cost-effectiveness Flexibility and scalability Integration with new technologies Disadvantages of ELT Architecture Limited real-time support High data storage costs Data quality issues Dependence on target system capabilities EtLT Architecture With the popularity of data lakes and real-time data warehouses, the weaknesses of ELT architecture in real-time processing and handling unstructured data have been highlighted. Thus, a new architecture, EtLT, has emerged. EtLT architecture enhances ELT by adding real-time data extraction from sources like SaaS, Binlog, and cloud components, as well as incorporating small-scale transformations before loading the data into the target storage. This trend has led to the emergence of several specialized companies worldwide, such as StreamSets, Attunity (acquired by Qlik), Fivetran, and SeaTunnel by the Apache Foundation. The pros and cons of the EtLT architecture are as follows: Advantages of EtLT Architecture Real-time data processing Support for complex data sources Cost reduction Flexibility and scalability Performance optimization Support for large models Data quality and governance Disadvantages of EtLT Architecture Technical complexity Dependence on target system capabilities Management and monitoring challenges Increased data change management complexity Dependency on tools and platforms Overall, in recent years, with the rise of data, real-time data warehouses, and large models, the EtLT architecture has gradually become mainstream worldwide in the field of data integration. For specific historical details, you can refer to the relevant content in the article, "ELT is dead, and EtLT will be the end of modern data processing architecture." Under this overarching trend, let's interpret the maturity model of the entire data integration track. Overall, there are four clear trends: In the trend of ETL evolving into EtLT, the focus of data integration has shifted from traditional batch processing to real-time data collection and batch-stream integrated data integration. The hottest scenarios have also shifted from past single-database batch integration scenarios to hybrid cloud, SaaS, and multiple data sources integrated in a batch-stream manner. Data complexity transformation has gradually shifted from traditional ETL tools to processing complex transformations in data warehouses. At the same time, support for automatic schema changes (Schema Evolution) in the case of DDL (field definition) changes during real-time data integration has also begun. Even adapting to DDL changes in lightweight transformations has become a trend. Support for data source types has expanded from files and traditional databases to include emerging data sources, open-source big data ecosystems, unstructured data systems, cloud databases, and support for large models. These are also the most common scenarios encountered in every enterprise, and in the future, real-time data warehouses, lakes, clouds, and large models will be used in different scenarios within each enterprise. In terms of core capabilities and performance, diversity of data sources, high accuracy, and ease of troubleshooting are the top priorities for most enterprises. Conversely, there are not many examination points for capabilities such as high throughput and high real-time performance. Regarding data virtualization, DataFabric, and ZeroETL mentioned in the report, let's delve into the interpretation of the data integration maturity model below. Chapter 2: Data Integration Maturity Model Interpretation Data Production The data production segment refers to how data is obtained, distributed, transformed, and stored within the context of data integration. This part poses the greatest workload and challenges in integrating data. When users in the industry use data integration tools, their primary consideration is whether the tools support integration with their databases, cloud services, and SaaS systems. If these tools do not support the user's proprietary systems, then additional costs are incurred for customizing interfaces or exporting data into compatible files, which can pose challenges to the timeliness and accuracy of data. Data Collection Most data integration tools now support batch collection, rate limiting, and HTTP collection. However, real-time data acquisition (CDC) and DDL change detection are still in their growth and popularity stages. Particularly, the ability to handle DDL changes in source systems is crucial. Real-time data processing is often interrupted by changes in source system structures. Effectively addressing the technical complexity of DDL changes remains a challenge, and various industry vendors are still exploring solutions. Data Transformation With the gradual decline of ETL architectures, complex business processing (e.g., Join, Group By) within integration tools has gradually faded into history. Especially in real-time scenarios, there is limited memory available for operations like stream window Join and aggregation. Therefore, most ETL tools are migrating towards ELT and EtLT architectures. Lightweight data transformation using SQL-like languages has become mainstream, allowing developers to perform data cleaning without having to learn various data integration tools. Additionally, the integration of data content monitoring and DDL change transformation processing, combined with notification, alerts, and automation, is making data transformation a more intelligent process. Data Distribution Traditional JDBC loading, HTTP, and bulk loading have become essential features of every mainstream data integration tool, with competition focusing on the breadth of data source support. Automated DDL changes reduce developers' workload and ensure the smooth execution of data integration tasks. Various vendors employ their methods to handle complex scenarios where data table definitions change. Integration with large models is emerging as a new trend, allowing internal enterprise data to interface with large models, though it is currently the domain of enthusiasts in some open-source communities. Data Storage Next-generation data integration tools come with caching capabilities. Previously, this caching existed locally, but now distributed storage and distributed checkpoint/snapshot technologies are used. Effective utilization of cloud storage is also becoming a new direction, especially in scenarios involving large data caches requiring data replay and recording. Data Structure Migration This part deals with whether automatic table creation and inspection can be performed during the data integration process. Automatic table creation involves automatically creating tables/data structures in the target system that are compatible with those in the source system. This significantly reduces the workload of data development engineers. Automatic schema inference is a more complex scenario. In the EtLT architecture, in the event of real-time data DDL changes or changes in data fields, automatic inference of their rationality allows users to identify issues with data integration tasks before they run. The industry is still in the experimentation phase regarding this aspect. Computational Model The computational model evolves with the changing landscape of ETL, ELT, and EtLT. It has transitioned from emphasizing computation in the early stages to focusing on transmission in the middle stages, and now emphasizes lightweight computation during real-time transmission: Offline Data Synchronization This has become the most basic data integration requirement for every enterprise. However, the performance varies under different architectures. Overall, ETL architecture tools have much lower performance than ELT and EtLT tools under conditions of large-scale data. Real-Time Data Synchronization With the popularity of real-time data warehouses and data lakes, real-time data synchronization has become an essential factor for every enterprise to consider when integrating data. More and more companies are beginning to use real-time synchronization. Batch-Streaming Integration New-generation data integration engines are designed from the outset to consider batch-stream integration, providing more effective synchronization methods for different enterprise scenarios. In contrast, most traditional engines were designed to focus on either real-time or offline scenarios, resulting in poor performance for batch data synchronization. Unified use of batch and streaming can perform better in data initialization and hybrid batch-stream environments. Cloud-Native Overseas data integration tools are more aggressive in this aspect because they are billed on a pay-as-you-go basis. Therefore, the ability to quickly obtain/release responsive computing resources for each task is the core competitiveness and profit source for every company. In contrast, progress in big data cloud-native integration in China is still relatively slow, so it remains a subject of exploration for only a few companies domestically. Data Types and Typical Scenarios File Collection This is a basic feature of every integration tool. However, unlike in the past, apart from standard text files, the collection of data in formats like Parquet and ORC has become standard. Big Data Collection With the popularity of emerging data sources such as Snowflake, Redshift, Hudi, Iceberg, ClickHouse, Doris, and StarRocks, traditional data integration tools are significantly lagging in this regard. Users in China and the United States are generally at the same level in terms of big data usage, hence requiring vendors to adapt to these emerging data sources. Binlog Collection This is a burgeoning industry in China, as it has replaced traditional tools like DataStage and Informatica during the process of informatization. However, the replacement of databases like Oracle and DB2 has not been as rapid, resulting in a large number of specialized Binlog data collection companies emerging to solve CDC problems overseas. Informatization Data Collection This is a scenario unique to China. With the process of informatization, numerous domestic databases have emerged. Whether these databases' batch and real-time collection can be adapted, presents a higher challenge for Chinese vendors. Sharding In most large enterprises, sharding is commonly used to reduce the pressure on databases. Therefore, whether data integration tools support sharding has become a standard feature of professional data integration tools. Message Queues Driven by data lakes and real-time data warehouses, everything related to real-time is booming. Message queues, as the representatives of enterprise real-time data exchange centers, have become indispensable options for advanced enterprises. Whether data integration tools support a sufficient number of memory/disk message queue types has become one of the hottest features. Unstructured Data Non-structural data sources such as MongoDB and Elasticsearch have become essential for enterprises. Data integration also supports such data sources correspondingly. Big Model Data Numerous startups worldwide are working on quickly interacting with enterprise data and large datasets. SaaS Integration This is a very popular feature overseas but has yet to generate significant demand in China. Data Unified Scheduling Integrating data integration with scheduling systems, especially coordinating real-time data through scheduling systems and subsequent data warehouse tasks, is essential for building real-time data warehouses. Real-Time Data Warehouse/Data Lake These are currently the most popular scenarios for enterprises. Real-time data entry into warehouses/lakes enables the advantages of next-generation data warehouses/lakes to be realized. Data Disaster Recovery Backup With the enhancement of data integration real-time capabilities and CDC support, integration in the traditional disaster recovery field has emerged. Some data integration and disaster recovery vendors have begun to work in each other's areas. However, due to significant differences in detail between disaster recovery and integration scenarios, vendors penetrating each other's domains may lack functionality and require iterative improvements over time. Operation and Monitoring In data integration, operation and monitoring are essential functionalities. Effective operation and monitoring significantly reduce the workload of system operation and development personnel in case of data issues. Flow Control Modern data integration tools control traffic from multiple aspects such as task parallelism, single-task JDBC parallelism, and single JDBC reading volume, ensuring minimal impact on source systems. Task/Table-Level Statistics Task-level and table-level synchronization statistics are crucial for managing operations and maintenance personnel during data integration processes. Step-By-Step Trial Run Due to support for real-time data, SaaS, and lightweight transformation, running a complex data flow directly becomes more complicated. Therefore, some advanced companies have introduced step-by-step trial run functionality for efficient development and operation. Table Change Event Capture This is an emerging feature in real-time data processing, allowing users to make changes or alerts in a predefined manner when table changes occur in the source system, thereby maximizing the stability of real-time data. Batch-Stream Integrated Scheduling After real-time CDC and stream processing, integration with traditional batch data warehouse tasks is inevitable. However, ensuring accurate startup of batch data without affecting data stream operation remains a challenge. This is why integration and batch-stream integrated scheduling are related. Intelligent Diagnosis/Tuning/Resource Optimization In cluster and cloud-native scenarios, effectively utilizing existing resources and recommending correct solutions in case of problems are hot topics among the most advanced data integration companies. However, achieving production-level intelligent applications may take some time. Core Capabilities There are many important functionalities in data integration, but the following points are the most critical. The lack of these capabilities may have a significant impact during enterprise usage. Full/Incremental Synchronization Separate full/incremental synchronization has become a necessary feature of every data integration tool. However, the automatic switch from full to incremental mode has not yet become widespread among small and medium-sized vendors, requiring manual switching by users. CDC Capture As enterprise demands for real-time data increase, CDC capture has become a core competitive advantage of data integration. The support for the CDC from multiple data sources, the requirements, and the impact of the CDC on source databases, often become the core competitiveness of data integration tools. Data Diversity Supporting multiple data sources has become a "red ocean competition" in data integration tools. Better support for users' existing system data sources often leads to a more advantageous position in business competition. Checkpoint Resumption Whether real-time and batch data integration supports checkpoint resumption is helpful in quickly recovering from error data scenes in many scenarios or assisting in recovery in some exceptional cases. However, only a few tools currently support this feature. Concurrency/Limiting Speed Data integration tools need to be highly concurrent when speed is required and effectively reduce the impact on source systems when slow. This has become a necessary feature of integration tools. Multi-Table Synchronization/Whole-Database Migration This refers not only to convenient selection in the interface but also to whether JDBC or existing integration tasks can be reused at the engine level, thereby making better use of existing resources and completing data integration quickly. Performance Optimization In addition to core capabilities, performance often represents whether users need more resources or whether the hardware and cloud costs of data integration tools are low enough. However, extreme performance is currently unnecessary, and it is often considered the third factor after interface support and core capabilities. Timeliness Minute-level integration has gradually exited the stage of history, and supporting second-level data integration has become a very popular feature. However, millisecond-level data integration scenarios are still relatively rare, mostly appearing in disaster recovery special scenarios. Data Scale Most scenarios currently involve Tb-level data integration, while Pb-level data integration is implemented by open-source tools used by Internet giants. Eb-level data integration will not appear in the short term. High Throughput High throughput mainly depends on whether integration tools can effectively utilize network and CPU resources to achieve the maximum value of theoretical data integration. In this regard, tools based on ELT and EtLT have obvious advantages over ETL tools. Distributed Integration Dynamic fault tolerance is more important than dynamic scaling and cloud-native. The ability of a large data integration task to automatically tolerate errors in hardware and network failure situations is a basic function when doing large-scale data integration. Scalability and cloud-native are derived requirements in this scenario. Accuracy How data integration ensures consistency is a complex task. In addition to using multiple technologies to ensure "Exactly Once" CRC verification is done. Third-party data quality inspection tools are also needed rather than just "self-certification." Therefore, data integration tools often cooperate with data scheduling tools to verify data accuracy. Stability This is the result of multiple functions. Ensuring the stability of individual tasks is important in terms of availability, task isolation, data isolation, permissions, and encryption control. When problems occur in a single task or department, they should not affect other tasks and departments. Ecology Excellent data integration tools have a large ecosystem that supports synchronization with multiple data sources and integration with upstream and downstream scheduling and monitoring systems. Moreover, tool usability is also an important indicator involving enterprise personnel costs. Chapter 3: Trends In the coming years, with the proliferation of the EtLT architecture, many new scenarios will emerge in data integration, while data virtualization and DataFabric will also have significant impacts on future data integration: Multi-Cloud Integration This is already widespread globally, with most data integrations having cross-cloud integration capabilities. In China, due to the limited prevalence of clouds, this aspect is still in the early incubation stage. ETL Integration As the ETL cycle declines, most enterprises will gradually migrate from tools like Kettle, Informatica, Talend, etc., to emerging EtLT architectures, thereby supporting batch-stream integrated data integration and more emerging data sources. ELT Currently, most mainstream big data architectures are based on ELT. With the rise of real-time data warehouses and data lakes, ELT-related tools will gradually upgrade to EtLT tools, or add real-time EtLT tools to compensate for the lack of real-time data support in ELT architectures. EtLT Globally, companies like JPMorgan, Shein, Shoppe, etc., are embedding themselves in the EtLT architecture. More companies will integrate their internal data integration tools into the EtLT architecture, combined with batch-stream integrated scheduling systems to meet enterprise DataOps-related requirements. Automated Governance With the increase in data sources and real-time data, traditional governance processes cannot meet the timeliness requirements for real-time analysis. Automated governance will gradually rise within enterprises in the next few years. Big Model Support As large models penetrate enterprise applications, providing data to large models becomes a necessary skill for data integration. Traditional ETL and ELT architectures are relatively difficult to adapt to real-time, large batch data scenarios, so the EtLT architecture will deepen its penetration into most enterprises along with the popularization of large models. ZeroETL This is a concept proposed by Amazon, suggesting that data stored on S3 can be accessed directly by various engines without the need for ETL between different engines. In a sense, if the data scenario is not complex, and the data volume is small, a small number of engines can meet the OLAP and OLTP requirements. However, due to limited scenario support and poor performance, it will take some time for more companies to recognize this approach. DataFabric Currently, many companies propose using DataFabric metadata to manage all data, eliminating the need for ETL/ELT during queries and directly accessing underlying data. This technology is still in the experimental stage, with significant challenges in query response and scenario adaptation. It can meet the needs of simple scenarios with small data queries, but for complex big data scenarios, the EtLT architecture will still be necessary for the foreseeable future. Data Virtualization The basic idea is similar to the execution layer of DataFabric. Data does not need to be moved; instead, it is queried directly through ad-hoc query interfaces and compute engines (e.g., Presto, TrinoDB) to translate data stored in underlying data storage or data engines. However, in the case of large amounts of data, engine query efficiency and memory consumption often fail to meet expectations, so it is only used in scenarios with small amounts of data. Conclusion From an overall trend perspective, with the explosive growth of global data, the emergence of large models, and the proliferation of data engines for various scenarios, the rise of real-time data has brought data integration back to the forefront of the data field. If data is considered a new energy source, then data integration is like the pipeline of this new energy. The more data engines there are, the higher the efficiency, data source compatibility, and usability requirements of the pipeline will be. Although data integration will eventually face challenges from Zero ETL, data virtualization, and DataFabric, in the visible future, the performance, accuracy, and ROI of these technologies have always failed to reach the level of popularity of data integration. Otherwise, the most popular data engines in the United States should not be SnowFlake or DeltaLake but TrinoDB. Of course, I believe that in the next 10 years, under the circumstances of DataFabric x large models, virtualization + EtLT + data routing may be the ultimate solution for data integration. In short, as long as data volume grows, the pipelines between data will always exist. Chapter 4: How To Use the Data Integration Maturity Model Firstly, the maturity model provides a comprehensive view of current and potential future technologies that may be utilized in data integration over the next 10 years. It offers individuals insight into personal skill development and assists enterprises in designing and selecting appropriate technological architectures. Additionally, it guides key development areas within the data integration industry. For enterprises, technology maturity aids in assessing the level of investment in a particular technology. For a mature technology, it is likely to have been in use for many years, supporting business operations effectively. However, as technological advancements reach a plateau, consideration can be given to adopting newer, more promising technologies to achieve higher business value. Technologies in decline are likely to face increasing limitations and issues in supporting business operations, gradually being replaced by newer technologies within 3-5 years. When introducing such technologies, it's essential to consider their business value and the current state of the enterprise. Popular technologies, on the other hand, are prioritized by enterprises due to their widespread validation among early adopters, with the majority of businesses and technology companies endorsing them. Their business value has been verified, and they are expected to dominate the market in the next 1-2 years. Growing technologies require consideration based on their business value, having passed the early adoption phase, and having their technological and business values validated by early adopters. They have not yet been fully embraced in the market due to reasons such as branding and promotion but are likely to become popular technologies and future industry standards. Forward-looking technologies are generally cutting-edge and used by early adopters, offering some business value. However, their general applicability and ROI have not been fully validated. Enterprises can consider limited adoption in areas where they provide significant business value. For individuals, mature and declining technologies offer limited learning and research value, as they are already widely adopted. Focusing on popular technologies can be advantageous for employment prospects, as they are highly sought after in the industry. However, competition in this area is fierce, requiring a certain depth of understanding to stand out. Growing technologies are worth delving into as they are likely to become popular in the future, and early experience can lead to expertise when they reach their peak popularity. Forward-looking technologies, while potentially leading to groundbreaking innovations, may also fail. Individuals may choose to invest time and effort based on personal interests. While these technologies may be far from job requirements and practical application, forward-thinking companies may inquire about them during interviews to assess the candidate's foresight. Definitions of Technological Maturity Forward-looking: Technologies are still in the research and development stage, with the community exploring their practical applications and potential market value. Although the industry's understanding of these technologies is still shallow, high-value demands have been identified. Growing: Technologies begin to enter the practical application stage, with increasing competition in the market and parallel development of various technological paths. The community focuses on overcoming challenges in practical applications and maximizing their commercial value, although their value in business is not fully realized. Popular: Technology development reaches its peak, with the community striving to maximize technological performance. Industry attention peaks and the technology begins to demonstrate significant commercial value. Declining: Technology paths begin to show clear advantages and disadvantages, with the market demanding higher optimization and integration. The industry begins to recognize the limitations and boundaries of technology in enhancing business value. Mature: Technology paths tend to unify and standardize, with the community focusing on reducing costs and improving efficiency. The industry also focuses on cost-effectiveness analysis to evaluate the priority and breadth of technology applications. Definitions of Business Value 5 stars: The cost reduction/revenue contribution of relevant technologies/business units accounts for 50% or more of the department's total revenue, or is managed by senior directors or higher-level executives (e.g., VPs). 4 stars: The cost reduction/revenue contribution of relevant technologies/business units accounts for between 40% and 50% of the department's total revenue, or is managed by directors. 3 stars: The cost reduction/revenue contribution of relevant technologies/business units accounts for between 30% and 40% of the department's total revenue, or is managed by senior managers. 2 stars: The cost reduction/revenue contribution of relevant technologies/business units accounts for between 20% and 30% of the department's total revenue, or is managed by managers. 1 star: The cost reduction/revenue contribution of relevant technologies/business units accounts for between 5% and 20% of the department's total revenue, or is managed by supervisors. Definitions of Technological Difficulty 5 stars: Invested in top industry expert teams for over 12 months 4 stars: Invested in industry experts or senior architects for over 12 months 3 stars: Invested in architect teams for approximately 6 months 2 stars: Invested in senior programmer teams for 1-3 months 1 star: Invested in ordinary programmer teams for 1-3 months
In the digital age, where information is readily available and easily accessible, there is a need for techniques that can detect plagiarism (intentional or unintentional) from content duplication to enhancing natural language processing capabilities. What sets shingling's capabilities apart is the way it extends to various applications, including but not limited to, document clustering, information retrieval, and content recommendation systems. The article outlines the following: Understand the concept of shingling Explore the basics of the shingling technique Jaccard similarity: measuring textual similarity Advanced techniques and optimizations Conclusion and further reading Understanding the Concept of Shingling Shingling is a widely used technique in detecting and mitigating textual similarities. This article introduces you to the concept of shingling, the basics of shingling technique, Jaccard similarity, advanced techniques, and optimizations. The process of converting a string of text in documents into a set of overlapping sequences of words or letters is called Shingling. Programmatically, think of this as a list of substrings from a string value. Let's take a string: "Generative AI is evolving rapidly." Let's denote the length of the shingle as k and set the value of k to 5. The result is a set of five letters: {'i is ', ' evol', 'apidl', 'e ai ', 'ai is', 'erati', 've ai', 'rapid', 'idly.', 'ing r', ' ai i', 's evo', 'volvi', 'nerat', ' is e', 'ving ', 'tive ', 'enera', 'ng ra', 'is ev', 'gener', 'ative', 'evolv', 'pidly', ' rapi', 'olvin', 'rativ', 'lving', 'ive a', 'g rap'} This set of overlapping sequences are called "shingles" or "n-grams." Shingles consist of consecutive words or characters from the text, creating a series of overlapping segments. The length of a shingle denoted above as "k," varies depending on the specific requirements of the analysis, with a common practice involving the creation of shingles containing three to five words or characters. Explore the Basics of Shingling Technique Shingling is part of a three-step process. Tokenization If you are familiar with prompt engineering, you should have heard about Tokenization. It is the process of breaking up a sequence of text into smaller units called tokens. Tokens can be words, subwords, characters, or other meaningful units. This step prepares the text data for further processing by models. With word tokenization, the above example "Generative AI is evolving rapidly" will be tokenized into: ['Generative', 'AI', 'is', 'evolving', 'rapidly', '.'] For tokenization, you can use either a simple Python `split` method or Regex. There are libraries like NLTK (Natural Language ToolKit) and spaCy that provide advanced options like stopwords etc., Link to the code. Shingling As you know by now, Shingling, also known as n-gramming, is the process of creating sets of contiguous sequences of tokens (n-grams or shingles) from the tokenized text. For example, with k=3, the sentence "Generative AI is evolving rapidly." would produce shingles like [['Generative', 'AI', 'is'], ['AI', 'is', 'evolving'], ['is', 'evolving', 'rapidly.']] This is a list of shingles. Shingling helps capture local word order and context. Hashing Hashing simply means using special functions to turn any kind of data, like text or shingles, into fixed-size codes. Some popular hashing methods include MinHash, SimHash, and Locality Sensitive Hashing (LSH). Hashing enables efficient comparison, indexing, and retrieval of similar text segments. When you turn documents into sets of shingle codes, it's much simpler for you to compare them and spot similarities or possible plagiarism. Simple Shingling Let's consider two short text passages that are widely used to explain simple shingling Passage 1: "The quick brown fox jumps over the lazy dog." Passage 2: "The quick brown fox jumps over the sleeping cat." With a word size of 4, using the w-shingle Python above, the shingles for Passage 1 would be: Shell python w_shingle.py "The quick brown fox jumps over the lazy dog." -w 4 [['The', 'quick', 'brown', 'fox'], ['quick', 'brown', 'fox', 'jumps'], ['brown', 'fox', 'jumps', 'over'], ['fox', 'jumps', 'over', 'the'], ['jumps', 'over', 'the', 'lazy'], ['over', 'the', 'lazy', 'dog.']] For passage 2, the shingles would be: Shell python w_shingle.py "The quick brown fox jumps over the sleeping cat" -w 4 [['The', 'quick', 'brown', 'fox'], ['quick', 'brown', 'fox', 'jumps'], ['brown', 'fox', 'jumps', 'over'], ['fox', 'jumps', 'over', 'the'], ['jumps', 'over', 'the', 'sleeping'], ['over', 'the', 'sleeping', 'cat']] By comparing the sets of shingles, you can see that the first four shingles are identical, indicating a high degree of similarity between the two passages. Shingling sets the stage for more detailed analysis, like measuring similarities using things like Jaccard similarity. Picking the right shingle size "k" is crucial. Smaller shingles can catch small language details, while larger ones might show bigger-picture connections. Jaccard Similarity: Measuring Textual Similarity In text analysis, Jaccard similarity is considered a key metric. It is the similarity between two text samples by calculating the ratio of the number of shared shingles to the total number of unique shingles across both samples. J(A,B) = (A ∩ B) / (A ∪ B) Jaccard similarity is defined as the size of the intersection divided by the size of the union of the shingle sets from each text. Though it sounds straightforward and simple, this technique is powerful as it provides a means to calculate textual similarity, offering insights into how closely related two pieces of text are based on their content. Using Jaccard similarity enables researchers and AI models to compare analyses of text data with precision. It is used in tasks like document clustering, similarity detection, and content categorization. Shingling can also be used to cluster similar documents together. By representing each document as a set of shingles and calculating the similarity between these sets (e.g., using the Jaccard coefficient or cosine similarity), you can group documents with high similarity scores into clusters. This approach is useful in various applications, such as search engine result clustering, topic modeling, and document categorization. While implementing Jaccard similarity in programming languages like Python, the choice of shingle size (k) and the conversion to lowercase ensures a consistent basis for comparison, showcasing the technique’s utility in discerning textual similarities. Let’s calculate the Jaccard similarity between two sentences: Python def create_shingles(text, k=5): """Generates a set of shingles for given text.""" return set(text[i : i + k] for i in range(len(text) - k + 1)) def compute_jaccard_similarity(text_a, text_b, k): """Calculates the Jaccard similarity between two shingle sets.""" shingles_a = create_shingles(text_a.lower(), k) print("Shingles for text_a is ", shingles_a) shingles_b = create_shingles(text_b.lower(), k) print("Shingles for text_b is ", shingles_b) intersection = len(shingles_a & shingles_b) union = len(shingles_a | shingles_b) print("Intersection - text_a ∩ text_b: ", intersection) print("Union - text_a ∪ text_b: ", union) return intersection / union Link to the code repository. Example text_a = "Generative AI is evolving rapidly." text_b = "The field of generative AI evolves swiftly." shingles_a = {'enera', 's evo', 'evolv', 'rativ', 'ving ', 'idly.', 'ative', 'nerat', ' is e', 'is ev', 'olvin', 'i is ', 'pidly', 'ing r', 'rapid', 'apidl', 've ai', ' rapi', 'tive ', 'gener', ' evol', 'volvi', 'erati', 'ive a', ' ai i', 'g rap', 'ng ra', 'e ai ', 'lving', 'ai is'} shingles_b = {'enera', 'e fie', 'evolv', 'volve', 'wiftl', 'olves', 'rativ', 'f gen', 'he fi', ' ai e', ' fiel', 'lves ', 'ield ', ' gene', 'ative', ' swif', 'nerat', 'es sw', ' of g', 'ftly.', 'ld of', 've ai', 'ves s', 'of ge', 'ai ev', 'tive ', 'gener', 'the f', ' evol', 'erati', 'iftly', 's swi', 'ive a', 'swift', 'd of ', 'e ai ', 'i evo', 'field', 'eld o'} J(A,B) = (A ∩ B) / (A ∪ B) = 12 / 57 = 0.2105 So, the Jaccard Similarity is 0.2105. The score signifies that the two sets are 21.05 % (0.2105 * 100) similar. Example Instead of passages, let’s consider two sets of numbers: A = { 1,3,6,9} B = {0,1,4,5,6,8} (A ∩ B) = Common numbers in both the sets = {1,6} = 2 (A ∪ B) = Total numbers in both the sets = {0,1,3,4,5,6,8,9} = 8 Calculate Jaccard similarity to see how similar these two sets of numbers are (A ∩ B) / (A ∪ B) = 2/8 = 0.25. To calculate dissimilarity, just subtract the score from 1. 1- 0.25 = 0.75 So both the sets are 25% similar and 75% dissimilar. Advanced Techniques and Optimizations Advanced shingling and hashing techniques and optimizations are crucial for efficient similarity detection and plagiarism detection in large datasets. Here are some advanced techniques and optimizations, along with examples and links to code implementations: Locality-Sensitive Hashing (LSH) Locality-Sensitive Hashing (LSH) is an advanced technique that improves the efficiency of shingling and hashing for similarity detection. It involves creating a signature matrix and using multiple hash functions to reduce the dimensionality of the data, making it efficient to find similar documents. The key idea behind LSH is to hash similar items into the same bucket with high probability, while dissimilar items are hashed into different buckets. This is achieved by using a family of locality-sensitive hash functions that hash similar items to the same value with higher probability than dissimilar items. Example Consider two documents, A and B, represented as sets of shingles: Document A: {"the quick brown", "quick brown fox", "brown fox jumps"} Document B: {"a fast brown", "fast brown fox", "brown fox leaps"} We can apply LSH by: Generating a signature matrix using multiple hash functions on the shingles. Hashing each shingle using the hash functions to obtain a signature vector. Banding the signature vectors into bands. Hashing each band to obtain a bucket key. Documents with the same bucket key are considered potential candidates for similarity. This process significantly reduces the number of document pairs that need to be compared, making similarity detection more efficient. For a detailed implementation of LSH in Python, refer to the GitHub repository. Minhashing Minhashing is a technique used to quickly estimate the similarity between two sets by using a set of hash functions. It's commonly applied in large-scale data processing tasks where calculating the exact similarity between sets is computationally expensive. Minhashing approximates the Jaccard similarity between sets, which measures the overlap between two sets. Here's how Minhashing works: Generate Signature Matrix Given a set of items, represent each item as a set of shingles. Construct a signature matrix where each row corresponds to a hash function, and each column corresponds to a shingle. Apply hash functions to each shingle in the set, and for each hash function, record the index of the first shingle hashed to 1 (the minimum hash value) in the corresponding row of the matrix. Estimate Similarity To estimate the similarity between the two sets, compare their respective signature matrices. Count the number of positions where the signatures agree (i.e., both sets have the same minimum hash value for that hash function). Divide the count of agreements by the total number of hash functions to estimate the Jaccard similarity. Minhashing allows for a significant reduction in the amount of data needed to represent sets while providing a good approximation of their similarity. Example: Consider Two Sets Set A = {1, 2, 3, 4, 5} Set B = {3, 4, 5, 6, 7} We can represent these sets as shingles: Set A shingles: {1, 2, 3}, {2, 3, 4}, {3, 4, 5}, {4, 5}, {5} Set B shingles: {3, 4}, {4, 5}, {5, 6}, {6, 7}, {3}, {4}, {5}, {6}, {7} Now, let's generate the signature matrix using Minhashing: Hash Function Shingle 1 Shingle 2 Shingle 3 Shingle 4 Shingle 5 Hash 1 0 0 1 0 1 Hash 2 1 1 1 0 0 Hash 3 0 0 1 0 1 Now, let's estimate the similarity between sets A and B: Number of agreements = 2 (for Shingle 3 and Shingle 5) Total number of hash functions = 3 Jaccard similarity ≈ 2/3 ≈ 0.67 Code Implementation: You can implement Minhashing in Python using libraries like NumPy and datasketch. Banding and Bucketing Banding and bucketing are advanced optimization techniques used in conjunction with Minhashing to efficiently identify similar sets within large datasets. These techniques are particularly valuable when dealing with massive collections of documents or data points. Banding Banding involves dividing the Minhash signature matrix into multiple bands, each containing several rows. By partitioning the matrix vertically into bands, we reduce the number of comparisons needed between sets. Instead of comparing every pair of rows across the entire matrix, we only compare rows within the same band. This significantly reduces the computational overhead, especially for large datasets, as we only need to consider a subset of rows at a time. Bucketing Bucketing complements banding by further narrowing down the comparison process within each band. Within each band, we hash the rows into a fixed number of buckets. Each bucket contains a subset of rows from the band. When comparing sets for similarity, we only need to compare pairs of sets that hash to the same bucket within each band. This drastically reduces the number of pairwise comparisons required, making the process more efficient. Example Let's say we have a Minhash signature matrix with 100 rows and 20 bands. Within each band, we hash the rows into 10 buckets. When comparing sets, instead of comparing all 100 rows, we only need to compare pairs of sets that hash to the same bucket within each band. This greatly reduces the number of comparisons needed, leading to significant performance gains, especially for large datasets. Benefits Efficiency: Banding and bucketing dramatically reduce the number of pairwise comparisons needed, making similarity analysis more computationally efficient. Scalability: These techniques enable the processing of large datasets that would otherwise be impractical due to computational constraints. Memory optimization: By reducing the number of comparisons, banding, and bucketing also lower memory requirements, making the process more memory-efficient. Several open-source implementations, such as the datasketch library in Python and the lsh library in Java, provide functionality for shingling, minhashing, and banded LSH with bucketing. Candidate Pairs Candidate pairs is an advanced technique used in conjunction with shingling and minhashing for efficient plagiarism detection and near-duplicate identification. In the context of shingling, candidate pairs work as follows: Shingling Documents are first converted into sets of k-shingles, which are contiguous sequences of k tokens (words or characters) extracted from the text. This step represents documents as sets of overlapping k-grams, enabling similarity comparisons. Minhashing The shingle sets are then converted into compact minhash signatures, which are vectors of fixed length, using the minhashing technique. Minhash signatures preserve similarity between documents, allowing efficient estimation of Jaccard similarity. Banding The minhash signatures are split into multiple bands, where each band is a smaller sub-vector of the original signature. Bucketing Within each band, the sub-vectors are hashed into buckets using a hash function. Documents with the same hash value for a particular band are placed in the same bucket. Candidate Pair Generation Two documents are considered a candidate pair for similarity comparison if they share at least one bucket across all bands. In other words, if their sub-vectors collide in at least one band, they are considered a candidate pair. The key advantage of using candidate pairs is that it significantly reduces the number of document pairs that need to be compared for similarity, as only candidate pairs are considered. This makes the plagiarism detection process much more efficient, especially for large datasets. By carefully choosing the number of bands and the band size, a trade-off can be made between the accuracy of similarity detection and the computational complexity. More bands generally lead to higher accuracy but also increase the computational cost. Document Similarity Conclusion In conclusion, the combination of shingling, minhashing, banding, and Locality Sensitive Hashing (LSH) provides a powerful and efficient approach for plagiarism detection and near-duplicate identification in large document collections. Shingling converts documents into sets of k-shingles, which are contiguous sequences of k tokens (words or characters), enabling similarity comparisons. Minhashing then compresses these shingle sets into compact signatures, preserving similarity between documents. To further improve efficiency, banding splits the minhash signatures into multiple bands, and bucketing hashes each band into buckets, grouping similar documents together. This process generates candidate pairs, which are pairs of documents that share at least one bucket across all bands, significantly reducing the number of document pairs that need to be compared for similarity. The actual similarity computation is then performed only on the candidate pairs, using the original minhash signatures to estimate the Jaccard similarity. Pairs with similarity above a specified threshold are considered potential plagiarism cases or near-duplicates. This approach offers several advantages: Scalability: By focusing on candidate pairs, the computational complexity is significantly reduced, making it feasible to handle large datasets. Accuracy: Shingling and minhashing can detect plagiarism even when content is paraphrased or reordered, as they rely on overlapping k-shingles. Flexibility: The choice of the number of bands and the band size allows for a trade-off between accuracy and computational complexity, enabling optimization for specific use cases. Several open-source implementations, such as the datasketch library in Python and the lsh library in Java, provide functionality for shingling, minhashing, and banded LSH with bucketing and candidate pair generation, making it easier to integrate these techniques into plagiarism detection systems or other applications requiring efficient similarity search. Overall, the combination of shingling, minhashing, banding, and LSH offers a powerful and efficient solution for plagiarism detection and near-duplicate identification, with applications across academia, publishing, and content management systems. Further Reading Plagiarism selection in text collections Minhash using datasketch chrisjmccormick/MinHash: Python implementation with tutorial MinHashing - Kaggle: Comprehensive exploration of minhashing Stack Overflow discussion: Suggestions for minhash implementations