Data Engineering

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics. Remarkable advances in deep learning, combined with the exponential increase in computing power and the explosion of available data, have catalyzed the emergence of generative artificial intelligence (GenAI). Consequently, huge milestones have propelled this technology to greater potential, such as the introduction of the Transformer architecture in 2017 and the launch of GPT-2 in 2019. The arrival of GPT-3 in 2020 then demonstrated astounding capabilities in text generation, translation, and question answering, marking a decisive turning point in the field of AI. In 2024, organizations are devoting more resources to their AI strategy, seeking not only to optimize their decision-making processes, but also to generate new products and services while saving precious time to create more value. In this article, we plan to assess strategic practices for building a foundation of data intelligence systems. The emphasis will center around transparency, governance, and the ethical and responsible exploitation of cutting-edge technologies, particularly GenAI. An Introduction to Identifying and Extracting Data for AI Systems Identifying and extracting data are fundamental steps for training AI systems. As data is the primary resource for these systems, it makes it a priority to identify the best sources and use effective extraction methods and tools. Here are some common sources: Legacy systems contain valuable historical data that can be difficult to extract. These systems are often critical to day-to-day operations. They require specific approaches to extract data without disrupting their functioning.Data warehouses (DWHs) facilitate the search and analysis of structured data. They are designed to store large quantities of historical data and are optimized for complex queries and in-depth analysis.Data lakes store raw structured and unstructured data. Their flexibility means they can store a wide variety of data, providing fertile ground for exploration and the discovery of new insights.Data lakehouses cleverly combine the structure of DWHs with the flexibility of data lakes. They offer a hybrid approach that allows them to benefit from the advantages of both worlds, providing performance and flexibility. Other important sources include NoSQL databases, IoT devices, social media, and APIs, which broaden the spectrum of resources available to AI systems. Importance of Data Quality Data quality is indispensable for training accurate AI models. Poor data quality can distort the learning process and lead to biased or unreliable results. Data validation is, therefore, a crucial step, ensuring that input data meets quality standards such as completeness, consistency, and accuracy. Similarly, data versioning enables engineers to understand the impact of data changes on the performance of AI models. This practice facilitates the reproducibility of experiments and helps to identify sources of improvement or degradation in model performance. Finally, data tracking ensures visibility of the flow of data through the various processing stages. This traceability lets us understand where data comes from, how it is transformed, and how it is used, thereby contributing to transparency and regulatory compliance. Advanced Data Transformation Techniques Advanced data transformation techniques prepare raw data for AI models. These techniques include: Feature scaling and normalization. These methods ensure that all input variables have a similar amplitude. They are crucial for many machine learning algorithms that are sensitive to the scale of the data.Handling missing data. Using imputation techniques to estimate missing values, this step is fundamental to maintaining the integrity and representativeness of datasets.Detection and processing of outliers. This technique is used to identify and manage data that deviate significantly from the other observations, thus preventing these outliers from biasing the models.Dimensionality reduction. This method helps reduce the number of features used by the AI model, which can improve performance and reduce overlearning.Data augmentation. This technique artificially increases the size of the dataset by creating modified versions of existing data, which is particularly useful when training data is limited. These techniques are proving important because of their ability to enhance data quality, manage missing values effectively, and improve predictive accuracy in AI models. Imputation methods, such as those found in libraries like Fancyimpute and MissForest, can fill in missing data with statistically derived values. This is particularly useful in areas where outcomes are often predicted on the basis of historical and incomplete data. Key Considerations for Building AI-Driven Data Environments Data management practices are evolving under the influence of AI and the increasing integration of open-source technologies within companies. GenAI is now playing a central role in the way companies are reconsidering their data and applications, profoundly transforming traditional approaches. Let's take a look at the most critical considerations for building AI-driven data systems. Leveraging Open-Source Databases for AI-Driven Data Engineering The use of open-source databases for AI-driven data engineering has become a common practice in modern data ecosystems. In particular, vector databases are increasingly used in large language model (LLM) optimization. The synergy between vector databases and LLMs makes it possible to create powerful and efficient AI systems. In Table 1, we explore common open-source databases for AI-driven data engineering so that you can better leverage your own data when building intelligent systems: Table 1. Open-source databases for AI-driven data engineering categorycapabilitytechnologyRelational and NoSQLRobust functionality for transactional workloadsPostgreSQL and MySQLLarge-scale unstructured data managementMongoDB, CassandraReal-time performance and cachingRedisSupport for big data projects on Hadoop; large-scale storage and analysis capabilitiesApache HBase, Apache HiveVector databases and LLMsRapid search and processing of vectorsMilvus, PineconeSupport for search optimizationFaiss, Annoy, VespaEmerging technologiesHomomorphic databasesSEAL, TFHEDifferential privacy solutionsOpenDP, differential privacySensitive data protection via isolated execution environmentsIntel SGX, ARM TrustZone Emerging Technologies New database technologies, such as distributed, unified, and multi-model databases, offer developers greater flexibility in managing complex datasets. Data-intensive AI applications need these capabilities to bring greater flexibility in data management. Additionally, privacy-oriented databases enable computations on encrypted data. This enhances security and compliance with regulations such as GDPR. These advances enable developers to build more scalable and secure AI solutions. Industries handling sensitive data need these capabilities to ensure flexibility, security, and regulatory compliance. As shown in Table 1, homomorphic encryption and differential privacy solutions will prove impactful for advanced applications, particularly in industries that deal with sensitive data. For example, homomorphic encryption lets developers operate computations on encrypted data without ever decrypting it. Ethical Considerations Ethical considerations related to training models on large datasets raise important questions about bias, fairness, and transparency of algorithms and applications that use them. Therefore, in order to create AI systems that are more transparent, explainable AI is becoming a major requirement for businesses because the complexity of LLM models often makes it difficult, sometimes even impossible, to understand the decisions or recommendations produced by these systems. For developers, the consequence is that they not only have to work on performance, but also ensure that their models can be interpreted and validated by non-technical stakeholders, which requires extra time and effort when designing models. For example, developers need to install built-in transparency mechanisms, such as attention maps or interpretable results, so that decisions can be traced back to the specific data. Building a Scalable AI Infrastructure Building a scalable AI infrastructure is based on three main components: Storage. Flexible solutions, such as data lakes or data lakehouses, enable massive volumes of data to be managed efficiently. These solutions offer the scalability needed to adapt to the exponential growth in data generated and consumed by AI systems.Computing. GPU or TPU clusters provide new processing power required by deep neural networks and LLMs. These specialized computing units speed up the training and inference of AI models.Orchestration. Orchestration tools (e.g., Apache Airflow, Dagster, Kubernetes, Luigi, Prefect) optimize the management of large-scale AI tasks. They automate workflows, manage dependencies between tasks, and optimize resource use. Figure 1. Scalable AI architecture layers Hybrid Cloud Solutions Hybrid cloud solutions offer flexibility, resilience, and redundancy by combining public cloud resources with on-premises infrastructure. They enable the public cloud to be used for one-off requirements such as massive data processing or complex model training. At the same time, they combine the ability to maintain sensitive data on local servers. This approach offers a good balance between performance, security, and costs because hybrid cloud solutions enable organizations to make the most of both environments. Ensuring Future-Proof AI Systems To ensure the future proofing of AI systems, it is essential to: Design flexible and modular systems. This makes it easy to adapt systems to new technologies and changing business needs.Adopt data-centric approaches. Organizations must ensure that their AI systems remain relevant and effective. To achieve that, they have to place data at the heart of strategy.Integrate AI into a long-term vision. AI should not be seen as an isolated project since technology for technology's sake is of little interest. Instead, it should be seen as an integral component of a company's digital strategy.Focus on process automation. Automation optimizes operational efficiency and frees up resources for innovation.Consider data governance. Solid governance is essential to guarantee the quality, security, and compliance of the data used by AI systems.Prioritize ethics and transparency. These aspects are crucial for maintaining user confidence and complying with emerging regulations. Collaboration Between Data Teams and AI/ML Engineers Collaboration between data engineers, AI/ML engineers, and data scientists is critical to the success of AI projects. Data engineers manage the infrastructure and pipelines that allow data scientists and AI/ML engineers to focus on developing and refining models, while AI/ML engineers operationalize these models to deliver business value. To promote effective collaboration, organizations need to implement several key strategies: Clearly define the roles and responsibilities of each team; everyone must understand their part in the project.Use shared tools and platforms to facilitate seamless interaction and data sharing among team members.Encourage regular communication and knowledge sharing through frequent meetings and the use of shared documentation platforms. These practices help create a cohesive work environment where information flows freely, leading to more efficient and successful AI projects. For example, in a recommendation engine used by an e-commerce platform, data engineers collect and process large volumes of customer data. This includes historical browsing data and purchasing behavior. AI/ML engineers then develop algorithms that predict product preferences, and developers integrate the algorithms into the website or application. When an update to the recommendation model is ready, MLOps pipelines then automate testing and deployment. Conclusion Beyond tool implementation, strategic considerations must be accounted for in the same way as purely technical ones: Projects based on AI technologies must be built on a foundation of high-quality, well-managed data. The quality of AI systems depends in particular on the diversity and richness of their data sources, whether these are existing systems or data lakes.Ensuring AI models are interpretable and ethically compliant is essential to nurture trust and compliance with regulatory frameworks.The success of all AI initiatives is also directly dependent on the level of collaboration between data engineers, AI/ML specialists, and DevOps teams.AI applications, generative models, and hardware infrastructures are evolving rapidly to meet market demands, which require companies to adopt scalable infrastructures that can support these advancements. As organizations move forward, they need to focus on data engineering automation, cross-functional collaboration, and alignment with ethical and regulatory standards in order to maximize the value of their AI investments. This is an excerpt from DZone's 2024 Trend Report,Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics.Read the Free Report

By Frederic Jacquet CORE

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics. Data engineering and software engineering have long been at odds, each with their own unique tools and best practices. A key differentiator has been the need for dedicated orchestration when building data products. In this article, we'll explore the role data orchestrators play and how recent trends in the industry may be bringing these two disciplines closer together than ever before. The State of Data Orchestration One of the primary goals of investing in data capabilities is to unify knowledge and understanding across the business. The value of doing so can be immense, but it involves integrating a growing number of systems with often increasing complexity. Data orchestration serves to provide a principled approach to composing these systems, with complexity coming from: Many distinct sources of data, each with their own semantics and limitationsMany destinations, stakeholders, and use cases for data productsHeterogeneous tools and processes involved with creating the end product There are several components in a typical data stack that help organize these common scenarios. The Components The prevailing industry pattern for data engineering is known as extract, load, and transform, or ELT. Data is (E) extracted from upstream sources, (L) loaded directly into the data warehouse, and only then (T) transformed into various domain-specific representations. Variations exist, such as ETL, which performs transformations before loading into the warehouse. What all approaches have in common are three high-level capabilities: ingestion, transformation, and serving. Orchestration is required to coordinate between these three stages, but also within each one as well. Ingestion Ingestion is the process that moves data from a source system (e.g., database), into a storage system that allows transformation stages to more easily access it. Orchestration at this stage typically involves scheduling tasks to run when new data is expected upstream or actively listening for notifications from those systems when it becomes available. Transformation Common examples of transformations include unpacking and cleaning data from its original structure as well as splitting or joining it into a model more closely aligned with the business domain. SQL and Python are the most common ways to express these transformations, and modern data warehouses provide excellent support for them. The role of orchestration in this stage is to sequence the transformations in order to efficiently produce the models used by stakeholders. Serving Serving can refer to a very broad range of activities. In some cases, where the end user can interact directly with the warehouse, this may only involve data curation and access control. More often, downstream applications need access to the data, which, in turn, requires synchronization with the warehouse's models. Loading and synchronization is where orchestrators play a role in the serving stage. Figure 1. Typical flow of data from sources, through the data warehouse, out to end-user apps Ingestion brings data in, transformation occurs in the warehouse, and data is served to downstream apps. These three stages comprise a useful mental model for analyzing systems, but what's important to the business is the capabilities they enable. Data orchestration helps coordinate the processes needed to take data from source systems, which are likely part of the core business, and turn it into data products. These processes are often heterogeneous and were not necessarily built to work together. This can put a lot of responsibility on the orchestrator, tasking it with making copies, converting formats, and other ad hoc activities to bring these capabilities together. The Tools At their core, most data systems rely on some scheduling capabilities. When only a limited number of services need to be managed on a predictable basis, a common approach is to use a simple scheduler such as cron. Tasks coordinated in this way can be very loosely coupled. In the case of task dependencies, it is straightforward to schedule one to start some time after the other is expected to finish, but the result can be sensitive to unexpected delays and hidden dependencies. As processes grow in complexity, it becomes valuable to make dependencies between them explicit. This is what workflow engines such as Apache Airflow provide. Airflow and similar systems are also often referred to as "orchestrators," but as we'll see, they are not the only approach to orchestration. Workflow engines enable data engineers to specify explicit orderings between tasks. They support running scheduled tasks much like cron and can also watch for external events that should trigger a run. In addition to making pipelines more robust, the bird's-eye view of dependencies they offer can improve visibility and enable more governance controls. Sometimes the notion of a "task" itself can be limiting. Tasks will inherently operate on batches of data, but the world of streaming relies on units of data that flow continuously. Many modern streaming frameworks are built around the dataflow model — Apache Flink being a popular example. This approach forgoes the sequencing of independent tasks in favor of composing fine-grained computations that can operate on chunks of any size. From Orchestration to Composition The common thread between these systems is that they capture dependencies, be it implicit or explicit, batch or streaming. Many systems will require a combination of these techniques, so a consistent model of data orchestration should take them all into account. This is offered by the broader concept of composition that captures much of what data orchestrators do today and also expands the horizons for how these systems can be built in the future. Composable Data Systems The future of data orchestration is moving toward composable data systems. Orchestrators have been carrying the heavy burden of connecting a growing number of systems that were never designed to interact with one another. Organizations have built an incredible amount of "glue" to hold these processes together. By rethinking the assumptions of how data systems fit together, new approaches can greatly simplify their design. Open Standards Open standards for data formats are at the center of the composable data movement. Apache Parquet has become the de facto file format for columnar data, and Apache Arrow is its in-memory counterpart. The standardization around these formats is important because it reduces or even eliminates the costly copy, convert, and transfer steps that plague many data pipelines. Integrating with systems that support these formats natively enables native "data sharing" without all the glue code. For example, an ingestion process might write Parquet files to object storage and then simply share the path to those files. Downstream services can then access those files without needing to make their own internal copies. If a workload needs to share data with a local process or a remote server, it can use Arrow IPC or Arrow Flight with close to zero overhead. Standardization is happening at all levels of the stack. Apache Iceberg and other open table formats are building upon the success of Parquet by defining a layout for organizing files so that they can be interpreted as tables. This adds subtle but important semantics to file access that can turn a collection of files into a principled data lakehouse. Coupled with a catalog, such as the recently incubating Apache Polaris, organizations have the governance controls to build an authoritative source of truth while benefiting from the zero-copy sharing that the underlying formats enable. The power of this combination cannot be overstated. When the business' source of truth is zero-copy compatible with the rest of the ecosystem, much orchestration can be achieved simply by sharing data instead of building cumbersome connector processes. Figure 2. A data system composed of open standards Once data is written to object storage as Parquet, it can be shared without any conversions. The Deconstructed Stack Data systems have always needed to make assumptions about file, memory, and table formats, but in most cases, they've been hidden deep within their implementations. A narrow API for interacting with a data warehouse or data service vendor makes for clean product design, but it does not maximize the choices available to end users. Consider Figure 1 and Figure 2, which depict data systems aiming to support similar business capabilities. In a closed system, the data warehouse maintains its own table structure and query engine internally. This is a one-size-fits-all approach that makes it easy to get started but can be difficult to scale to new business requirements. Lock-in can be hard to avoid, especially when it comes to capabilities like governance and other services that access the data. Cloud providers offer seamless and efficient integrations within their ecosystems because their internal data format is consistent, but this may close the door on adopting better offerings outside that environment. Exporting to an external provider instead requires maintaining connectors purpose-built for the warehouse's proprietary APIs, and it can lead to data sprawl across systems. An open, deconstructed system standardizes its lowest-level details. This allows businesses to pick and choose the best vendor for a service while having the seamless experience that was previously only possible in a closed ecosystem. In practice, the chief concern of an open data system is to first copy, convert, and land source data into an open table format. Once that is done, much orchestration can be achieved by sharing references to data that has only been written once to the organization's source of truth. It is this move toward data sharing at all levels that is leading organizations to rethink the way that data is orchestrated and build the data products of the future. Conclusion Orchestration is the backbone of modern data systems. In many businesses, it is the core technology tasked with untangling their complex and interconnected processes, but new trends in open standards are offering a fresh take on how these dependencies can be coordinated. Instead of pushing greater complexity into the orchestration layer, systems are being built from the ground up to share data collaboratively. Cloud providers have been adding compatibility with these standards, which is helping pave the way for the best-of-breed solutions of tomorrow. By embracing composability, organizations can position themselves to simplify governance and benefit from the greatest advances happening in our industry. This is an excerpt from DZone's 2024 Trend Report,Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics.Read the Free Report

Data is a critical component to all aspects of the world in 2024. It is more valuable than most commodities, and there is an exponentially increasing need to more safely and accurately share, use, store, and organize this data. Data architecture is just that: the rules and guidelines that users must follow when storing and using data. There is significant benefit to housing and conglomerating this data management into a single unified platform, but there are also emerging challenges such as data complexities and security considerations that will make this streamlining ever more complicated. The popularity of generative AI (commonly know as GenAI) that is steamrolling the technology industry will mean that data architecture will be completely changed in this revolutionary, modern era. Unsurprisingly, since this modernization is taking the world by storm in a very quick and competitive fashion, there are heightening stressors and pressures to adhere to it quickly. While there are projections that 80% of enterprises will incorporate GenAI APIs or GenAI-enabled applications, less than 25% of banking institutions have implemented their critical data into the target architecture; this is only one industry. There is a need to move away from data silos and onto the newer and modern data fabric and data mesh. Data Silos Are Old News — It's About Data Fabrics and Data Meshes In the automotive industry, among others, there has been a noticed need to move away from the outdated data silos. With data silos, information is inaccessible. It is gridlocked for one organization only. This hinders any communication or development and pigeonholes data into a single use without considering the transformation and evolution that can occur if it is viewed as a shared asset. A data fabric is an approach to unite data management. As mentioned, data is often gridlocked away and data fabrics aim to unlock it at the macro level and made available to multiple entities for numerous, differentiated purposes. A data mesh separates data into products and delivers them to all parties as decentralized and with their own individualized governances. This transition to modern data architecture is also altered by the adoption of artificial intelligence (AI). AI can help to locate sophisticated patterns, generate predictions, and even automate many processes. This can improve accuracy and largely benefit scalability and flexibility. However, there are also challenges of data quality, transparency, ethical and legal factors, and integration hiccups. This leads to many strategies and insights that can help to guide and smooth out the progression from traditional to modern data architecture. Key Strategies Build a Minimal Viable Product First Accelerating results in data architecture initiatives can be achieved in a much quicker fashion if you start with the minimum needed and build from there for your data storage. Begin by considering all use cases and finding the one component needed to develop so a data product can be delivered. Expansion can happen over time with use and feedback, which will actually create a more tailored and desirable product. Educate, Educate, Educate Educating your key personnel on the importance of being able and ready to make the shift from previously familiar legacy data systems to modern architectures like data lakehouses or hybrid cloud platforms. Migration to a unified, hybrid, or cloud-based data management system may seem challenging initially, but it is essential for enabling comprehensive data lifecycle management and AI-readiness. By investing in continuous education and training, organizations can enhance data literacy, simplify processes, and improve long-term data governance, positioning themselves for scalable and secure analytics practices. Anticipate the Challenges of AI By being prepared for the typical challenges of AI, problems can be predicted and anticipated which can help to reduce downtime and frustration in the modernization of data architecture. Some of the primary ones are: data quality, data volume, data privacy, and bias and fairness. Data cleaning, profiling, and labeling, bias mitigation, validation and testing, monitoring, edge computing, multimodal learning, federated learning, anomaly detection, and data protection regulations can all assist minimize the obstacles caused by AI. Key Insights Unifying Data Is Beneficial for Competition It is almost a unanimous decision that unifying data is useful for businesses. It helps with simplifying processes, gaining flexibility, enhancing data governance and security, enabling easier integration with new tools and models for AI, and improving scalability. Data fabric brings value for business and can increase competitive advantage by understanding the five competitive forces: new entrants, supplier bargaining, buyer bargaining, competitor rivalries, and substitute product/service threats. Data Is a Product There is a view that data should be domain-driven, viewed and handled as an asset, self-served on a platform, and undergo federated, computational governance. This is achieved through separation of data by domain and type; the incorporation of metadata for data to exist and be explained in its own, isolated format; the ability to search and locate data independently; and a supportive and organized housing structure. Handling Multiple Sources of Data Is Challenging It is critical to remember that combinations of data from numerous sources is difficult. Real-time capabilities for some processes like fraud detection, online shopping, and healthcare just simply are not ready yet. Standards and policies need to be adopted. There will be inevitable trouble with managing all clouds and data sources, potential security breaches and governance struggles, and the necessity for continuous development and customization. Modern Data Architecture Will Forge Ahead With the Advent of AI Despite the difficulties and complexities of updating the existing and traditional data architecture methods, there is no doubt that modern data architecture will also include AI. AI will continue to grow and help organizations use data in a prescriptive way, instead of a descriptive way. Although many people are wary of AI, there is still the overwhelming hope and vision that it will create opportunity, maximize output, and power innovation in all markets, including data structure and management. Those in the wake of AI and modern data architecture will know the benefits of more productivity and operational efficiency, enhanced customer experience, and risk management.

By Suri Nuthalapati CORE

Reactive Programming Reactive programming is a programming paradigm that manages asynchronous data streams and automatically propagates changes, enabling systems to react to events in real time. It’s useful for creating responsive APIs and event-driven applications, often applied in UI updates, data streams, and real-time systems. WebFlux WebFlux is designed for applications with high concurrency needs. It leverages Project Reactor and Reactive Streams, enabling it to handle a large number of requests concurrently with minimal resource usage. Key Features Reactive programming uses reactive types like Mono, which manages data 0..1, and Flux, which manages data 0..N, to process data streams asynchronously.Non-blocking I/O is built on non-blocking servers like Netty, reducing overhead and allowing for high-throughput processing.Functional and annotation-based models support both functional routing and traditional annotation-based controllers. R2DBC (Reactive Database Connectivity) R2DBC (Reactive Relational Database Connectivity) is a non-blocking API for interacting with relational databases in a fully reactive, asynchronous manner. It’s designed for reactive applications, enabling efficient handling of database operations in real-time data streams, especially with frameworks like Spring WebFlux. Overview of the Solution This approach is ideal for applications that require scalable, real-time interactions with data sources. Spring WebFlux allows for a non-blocking, asynchronous API setup, while R2DBC provides reactive connections to relational databases, like PostgreSQL or MySQL, which traditionally require blocking I/O with JDBC. This combination allows for a seamless, event-driven system, leveraging reactive streams to handle data flow from the client to the database without waiting for blocking operations. Key Components Java 17 or later will execute this solution because of the used Java record.Spring Boot library is used as the portable configuration of the services.Spring APO declarative approach means concerns are added dynamically at runtime, not hard coded into the application, making adjustments easier without altering existing code.Spring WebFlux: A reactive web framework that provides fully non-blocking APIs, optimized for reactive applications and real-time web functionalityR2DBC: A reactive, non-blocking API for database connectivity, which replaces the blocking behavior of JDBC with a fully reactive stackMapStruct: Used to transform requests into the data access layer WebFlux Servlet WebFlux supports a variety of servers like Netty, Tomcat, Jetty, Undertow, and Servlet containers. Let's define Netty and servlet container in this example. Project Structure Configuration application.yaml YAML logging: level: org: springframework: r2dbc: DEBUG server: error: include-stacktrace: never spring: application: name: async-api-async-db jackson: serialization: FAIL_ON_EMPTY_BEANS: false main: allow-bean-definition-overriding: true r2dbc: password: 123456 pool: enabled: true initial-size: 5 max-idle-time: 30m max-size: 20 validation-query: SELECT 1 url: r2dbc:mysql://localhost:3306/test username: root Web Flux Netty: Functional Process Spring's functional web framework (ProductRoute.java) exposes routing functionality, such as creating a RouterFunction using a discoverable builder-style API, to create a RouterFunction given a RequestPredicate and HandlerFunction, and to do further subrouting on an existing routing function. Additionally, this class can transform a RouterFunction into an HttpHandle. Java package com.amran.async.controller.functional; import com.amran.async.constant.ProductAPI; import com.amran.async.handler.ProductHandler; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; import org.springframework.web.reactive.function.server.RouterFunction; import org.springframework.web.reactive.function.server.RouterFunctions; import org.springframework.web.reactive.function.server.ServerResponse; import static org.springframework.web.reactive.function.server.RequestPredicates.DELETE; import static org.springframework.web.reactive.function.server.RequestPredicates.GET; import static org.springframework.web.reactive.function.server.RequestPredicates.POST; import static org.springframework.web.reactive.function.server.RequestPredicates.PUT; /** * @author Md Amran Hossain on 28/10/2024 AD * @Project async-api-async-db */ @Configuration(proxyBeanMethods = false) public class ProductRoute { @Bean public RouterFunction<ServerResponse> routerFunction(ProductHandler productHandler) { return RouterFunctions .route(GET(ProductAPI.GET_PRODUCTS).and(ProductAPI.ACCEPT_JSON), productHandler::getAllProducts) .andRoute(GET(ProductAPI.GET_PRODUCT_BY_ID).and(ProductAPI.ACCEPT_JSON), productHandler::getProductById) .andRoute(POST(ProductAPI.ADD_PRODUCT).and(ProductAPI.ACCEPT_JSON), productHandler::handleRequest) .andRoute(DELETE(ProductAPI.DELETE_PRODUCT).and(ProductAPI.ACCEPT_JSON), productHandler::deleteProduct) .andRoute(PUT(ProductAPI.UPDATE_PRODUCT).and(ProductAPI.ACCEPT_JSON), productHandler::handleRequest); } } WebFlux Non-Functional Request Process Spring Detected Rest API (ProductController.java): Java package com.amran.async.controller; import com.amran.async.model.Product; import com.amran.async.service.ProductService; import org.springframework.http.HttpStatus; import org.springframework.web.bind.annotation.DeleteMapping; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.PathVariable; import org.springframework.web.bind.annotation.PostMapping; import org.springframework.web.bind.annotation.PutMapping; import org.springframework.web.bind.annotation.RequestBody; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.ResponseStatus; import org.springframework.web.bind.annotation.RestController; import reactor.core.publisher.Flux; import reactor.core.publisher.Mono; /** * @author Md Amran Hossain on 28/10/2024 AD * @Project async-api-async-db */ @RestController @RequestMapping(path = "/api/v2") public class ProductController { private final ProductService productService; public ProductController(ProductService productService) { this.productService = productService; } @GetMapping("/product") @ResponseStatus(HttpStatus.OK) public Flux<Product> getAllProducts() { return productService.getAllProducts(); } @GetMapping("/product/{id}") @ResponseStatus(HttpStatus.OK) public Mono<Product> getProductById(@PathVariable("id") Long id) { return productService.getProductById(id); } @PostMapping("/product") @ResponseStatus(HttpStatus.CREATED) public Mono<Product> createProduct(@RequestBody Product product) { return productService.addProduct(product); } @PutMapping("/product/{id}") @ResponseStatus(HttpStatus.OK) public Mono<Product> updateProduct(@PathVariable("id") Long id, @RequestBody Product product) { return productService.updateProduct(product, id); } @DeleteMapping("/product/{id}") @ResponseStatus(HttpStatus.NO_CONTENT) public Mono<Void> deleteProduct(@PathVariable("id") Long id) { return productService.deleteProduct(id); } } Next, let's turn to see the R2DBC implementation. This repository follows reactive paradigms and uses Project Reactor types which are built on top of Reactive Streams. Save and delete operations with entities that have a version attribute trigger an onError with an OptimisticLockingFailureException when they encounter a different version value in the persistence store than in the entity passed as an argument. Other delete operations that only receive IDs or entities without version attributes do not trigger an error when no matching data is found in the persistence store. Optimistic Lock Optimistic locking and a concurrency conflict are detected while updating data. Optimistic locking is a mechanism for handling concurrent modifications in a way that ensures data integrity without requiring exclusive database locks. This is commonly applied in systems where multiple transactions might read and update the same record concurrently. Java package com.amran.async.repository; import com.amran.async.domain.ProductEntity; import org.springframework.data.repository.reactive.ReactiveCrudRepository; import org.springframework.stereotype.Repository; /** * @author Md Amran Hossain on 28/10/2024 AD * @Project async-api-async-db */ @Repository public interface ProductRepository extends ReactiveCrudRepository<ProductEntity, Long> { // @Query("SELECT * FROM product WHERE product_name = :productName") // Flux<ProductEntity> findByProductName(String productName); } Summary In summary, using Java Spring Boot WebFlux with R2DBC allows developers to build high-performance, reactive REST APIs with non-blocking database connections. This combination supports scalable, low-latency applications optimized for handling large volumes of concurrent requests, making it ideal for real-time, cloud-native environments. Also, you guys can find a full source code example here.

By Md Amran Hossain CORE

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics. This article explores the essential strategies for leveraging real-time data streaming to drive actionable insights while future proofing systems through AI automation and vector databases. It delves into the evolving architectures and tools that empower businesses to stay agile and competitive in a data-driven world. Real-Time Data Streaming: The Evolution and Key Considerations Real-time data streaming has evolved from traditional batch processing, where data was processed in intervals that introduced delays, to continuously handle data as it is generated, enabling instant responses to critical events. By integrating AI, automation, and vector databases, businesses can further enhance their capabilities, using real-time insights to predict outcomes, optimize operations, and efficiently manage large-scale, complex datasets. Necessity of Real-Time Streaming There is a need to act on data as soon as it is generated, particularly in scenarios like fraud detection, log analytics, or customer behavior tracking. Real-time streaming enables organizations to capture, process, and analyze data instantaneously, allowing them to react swiftly to dynamic events, optimize decision making, and enhance customer experiences in real time. Sources of Real-Time Data Real-time data originates from various systems and devices that continuously generate data, often in vast quantities and in formats that can be challenging to process. Sources of real-time data often include: IoT devices and sensorsServer logsApp activityOnline advertisingDatabase change eventsWebsite clickstreamsSocial media platformsTransactional databases Effectively managing and analyzing these data streams requires a robust infrastructure capable of handling unstructured and semi-structured data; this allows businesses to extract valuable insights and make real-time decisions. Critical Challenges in Modern Data Pipelines Modern data pipelines face several challenges, including maintaining data quality, ensuring accurate transformations, and minimizing pipeline downtime: Poor data quality can lead to flawed insights.Data transformations are complex and require precise scripting.Frequent downtime disrupts operations, making fault-tolerant systems essential. Additionally, data governance is crucial to ensure data consistency and reliability across processes. Scalability is another key issue as pipelines must handle fluctuating data volumes, and proper monitoring and alerting are vital for avoiding unexpected failures and ensuring smooth operation. Advanced Real-Time Data Streaming Architectures and Applications Scenarios This section demonstrates the capabilities of modern data systems to process and analyze data in motion, providing organizations with the tools to respond to dynamic events in milliseconds. Steps to Build a Real-Time Data Pipeline To create an effective real-time data pipeline, it's essential to follow a series of structured steps that ensure smooth data flow, processing, and scalability. Table 1, shared below, outlines the key steps involved in building a robust real-time data pipeline: Table 1. Steps to build a real-time data pipeline stepactivities performed1. Data ingestionSet up a system to capture data streams from various sources in real time2. Data processingCleanse, validate, and transform the data to ensure it is ready for analysis3. Stream processingConfigure consumers to pull, process, and analyze data continuously4. StorageStore the processed data in a suitable format for downstream use5. Monitoring and scalingImplement tools to monitor pipeline performance and ensure it can scale with increasing data demands Leading Open-Source Streaming Tools To build robust real-time data pipelines, several leading open-source tools are available for data ingestion, storage, processing, and analytics, each playing a critical role in efficiently managing and processing large-scale data streams. Open-source tools for data ingestion: Apache NiFi, with its latest 2.0.0-M3 version, offers enhanced scalability and real-time processing capabilities. Apache Airflow is used for orchestrating complex workflows.Apache StreamSets provides continuous data flow monitoring and processing. Airbyte simplifies data extraction and loading, making it a strong choice for managing diverse data ingestion needs. Open-source tools for data storage: Apache Kafka is widely used for building real-time pipelines and streaming applications due to its high scalability, fault tolerance, and speed. Apache Pulsar, a distributed messaging system, offers strong scalability and durability, making it ideal for handling large-scale messaging. NATS.io is a high-performance messaging system, commonly used in IoT and cloud-native applications, that is designed for microservices architectures and offers lightweight, fast communication for real-time data needs. Apache HBase, a distributed database built on top of HDFS, provides strong consistency and high throughput, making it ideal for storing large amounts of real-time data in a NoSQL environment. Open-source tools for data processing: Apache Spark stands out with its in-memory cluster computing, providing fast processing for both batch and streaming applications. Apache Flink is designed for high-performance distributed stream processing and supports batch jobs. Apache Storm is known for its ability to process more than a million records per second, making it extremely fast and scalable. Apache Apex offers unified stream and batch processing.Apache Beam provides a flexible model that works with multiple execution engines like Spark and Flink. Apache Samza, developed by LinkedIn, integrates well with Kafka and handles stream processing with a focus on scalability and fault tolerance. Heron, developed by Twitter, is a real-time analytics platform that is highly compatible with Storm but offers better performance and resource isolation, making it suitable for high-speed stream processing at scale. Open-source tools for data analytics: Apache Kafka allows high-throughput, low-latency processing of real-time data streams. Apache Flink offers powerful stream processing, ideal for applications requiring distributed, stateful computations. Apache Spark Streaming integrated with the broader Spark ecosystem handles real-time and batch data within the same platform. Apache Druid and Pinot serve as real-time analytical databases, offering OLAP capabilities that allow querying of large datasets in real time, making them particularly useful for dashboards and business intelligence applications. Implementation Use Cases Real-world implementations of real-time data pipelines showcase the diverse ways in which these architectures power critical applications across various industries, enhancing performance, decision making, and operational efficiency. Financial Market Data Streaming for High-Frequency Trading Systems In high-frequency trading systems, where milliseconds can make the difference between profit and loss, Apache Kafka or Apache Pulsar are used for high-throughput data ingestion. Apache Flink or Apache Storm handle low-latency processing to ensure trading decisions are made instantly. These pipelines must support extreme scalability and fault tolerance as any system downtime or processing delay can lead to missed trading opportunities or financial loss. IoT and Real-Time Sensor Data Processing Real-time data pipelines ingest data from IoT sensors, which capture information such as temperature, pressure, or motion, and then process the data with minimal latency. Apache Kafka is used to handle the ingestion of sensor data, while Apache Flink or Apache Spark Streaming enable real-time analytics and event detection. Figure 1 shared below shows the steps of stream processing for IoT from data sources to dashboarding: Figure 1. Stream processing for IoT Fraud Detection From Transaction Data Streaming Transaction data is ingested in real time using tools like Apache Kafka, which handles high volumes of streaming data from multiple sources, such as bank transactions or payment gateways. Stream processing frameworks like Apache Flink or Apache Spark Streaming are used to apply machine learning models or rule-based systems that detect anomalies in transaction patterns, such as unusual spending behavior or geographic discrepancies. How AI Automation Is Driving Intelligent Pipelines and Vector Databases Intelligent workflows leverage real-time data processing and vector databases to enhance decision making, optimize operations, and improve the efficiency of large-scale data environments. Data Pipeline Automation Data pipeline automation enables the efficient handling of large-scale data ingestion, transformation, and analysis tasks without manual intervention. Apache Airflow ensures that tasks are triggered in an automated way at the right time and in the correct sequence. Apache NiFi facilitates automated data flow management, enabling real-time data ingestion, transformation, and routing. Apache Kafka ensures that data is processed continuously and efficiently. Pipeline Orchestration Frameworks Pipeline orchestration frameworks are essential for automating and managing data workflows in a structured and efficient manner. Apache Airflow offers features like dependency management and monitoring. Luigi focuses on building complex pipelines of batch jobs. Dagster and Prefect provide dynamic pipeline management and enhanced error handling. Adaptive Pipelines Adaptive pipelines are designed to dynamically adjust to changing data environments, such as fluctuations in data volume, structure, or sources. Apache Airflow or Prefect allow for real-time responsiveness by automating task dependencies and scheduling based on current pipeline conditions. These pipelines can leverage frameworks like Apache Kafka for scalable data streaming and Apache Spark for adaptive data processing, ensuring efficient resource usage. Streaming Pipelines A streaming pipeline for populating a vector database for real-time retrieval-augmented generation (RAG) can be built entirely using tools like Apache Kafka and Apache Flink. The processed streaming data is then converted into embeddings and stored in a vector database, enabling efficient semantic search. This real-time architecture ensures that large language models (LLMs) have access to up-to-date, contextually relevant information, improving the accuracy and reliability of RAG-based applications such as chatbots or recommendation engines. Data Streaming as Data Fabric for Generative AI Real-time data streaming enables real-time ingestion, processing, and retrieval of vast amounts of data that LLMs require for generating accurate and up-to-date responses. While Kafka helps in streaming, Flink processes these streams in real time, ensuring that data is enriched and contextually relevant before being fed into vector databases. The Road Ahead: Future Proofing Data Pipelines The integration of real-time data streaming, AI automation, and vector databases offers transformative potential for businesses. For AI automation, integrating real-time data streams with frameworks like TensorFlow or PyTorch enable real-time decision making and continuous model updates. For real-time contextual data retrieval, leveraging databases like Faiss or Milvus help in fast semantic searches, which are crucial for applications like RAG. Conclusion Key takeaways include the critical role of tools like Apache Kafka and Apache Flink for scalable, low-latency data streaming, along with TensorFlow or PyTorch for real-time AI automation, and FAISS or Milvus for fast semantic search in applications like RAG. Ensuring data quality, automating workflows with tools like Apache Airflow, and implementing robust monitoring and fault-tolerance mechanisms will help businesses stay agile in a data-driven world and optimize their decision-making capabilities. Additional resources: AI Automation Essentials by Tuhin Chattopadhyay, DZone RefcardApache Kafka Essentials by Sudip Sengupta, DZone RefcardGetting Started With Large Language Models by Tuhin Chattopadhyay, DZone RefcardGetting Started With Vector Databases by Miguel Garcia, DZone Refcard This is an excerpt from DZone's 2024 Trend Report,Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics.Read the Free Report

By Tuhin Chattopadhyay CORE

Editor's Note: The following is an article written for and published in DZone's 2024 Trend Report, Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics. Businesses today rely significantly on data to drive customer engagement, make well-informed decisions, and optimize operations in the fast-paced digital world. For this reason, real-time data and analytics are becoming increasingly more necessary as the volume of data continues to grow. Real-time data enables businesses to respond instantly to changing market conditions, providing a competitive edge in various industries. Because of their robust infrastructure, scalability, and flexibility, cloud data platforms have become the best option for managing and analyzing real-time data streams. This article explores the key aspects of real-time data streaming and analytics on cloud platforms, including architectures, integration strategies, benefits, challenges, and future trends. Cloud Data Platforms and Real-Time Data Streaming Cloud data platforms and real-time data streaming have changed the way organizations manage and process data. Real-time streaming processes data as it is generated from different sources, unlike batch processing, where data is stored and processed at scheduled intervals. Cloud data platforms provide the necessary scalable infrastructure and services to ingest, store, and process these real-time data streams. Some of the key features that make cloud platforms efficient in handling the complexities of real-time data streaming include: Scalability. Cloud platforms can automatically scale resources to handle fluctuating data volumes. This allows applications to perform consistently, even at peak loads.Low latency. Real-time analytics systems are designed to minimize latency, providing near-real-time insights and enabling businesses to react quickly to new data.Fault tolerance. Cloud platforms provide fault-tolerant systems to ensure continuous data processing without any disturbance, whether caused by hardware malfunctioning or network errors.Integration. These platforms are integrated with cloud services for storage, AI/ML tooling, and various data sources to create comprehensive data ecosystems.Security. Advanced security features, including encryption, access controls, and compliance certifications, ensure that real-time data remains secure and meets regulatory requirements.Monitoring and management tools. Cloud-based platforms offer dashboards, notifications, and additional monitoring instruments that enable enterprises to observe data flow and processing efficiency in real time. This table highlights key tools from AWS, Azure, and Google Cloud, focusing on their primary features and the importance of each in real-time data processing and cloud infrastructure management: Table 1 Cloud servicekey featuresimportance AWS Auto Scaling Automatic scaling of resources Predictive scalingFully managed Cost-efficient resource management Better fault tolerance and availability Amazon CloudWatch Monitoring and loggingCustomizable alerts and dashboards Provides insights into system performanceHelps with troubleshooting and optimization Google Pub/Sub Stream processing and data integrationSeamless integration with other GCP services Low latency and high availabilityAutomatic capacity management Azure Data Factory Data workflow orchestrationSupport for various data sourcesCustomizable data flows Automates data pipelinesIntegrates with diverse data sources Azure Key Vault Identity managementSecrets and key management Centralized security managementProtecting and managing sensitive data Cloud providers offer various features for real-time data streaming. When selecting a platform, consider factors like scalability, availability, and compatibility with data processing tools. Select a platform that fits your organization’s setup, security requirements, and data transfer needs. To support your cloud platform and real-time data streaming, here are some key open-source technologies and frameworks: Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and streaming applications.Apache Flink is a stream processing framework that supports complex event processing and stateful computations.Apache Spark Streaming is an extension of Apache Spark for handling real-time data.Kafka Connect is a framework that helps connect Kafka with different data sources and storage options. Connectors can be set up to transfer data between Kafka and outside systems. Real-Time Data Architectures on Cloud Data Platforms The implementation of real-time data analytics requires choosing the proper architecture that fits the special needs of an organization. Common Architectures Different data architectures offer various ways to manage real-time data. Here’s a comparison of the most popular real-time data architectures: Table 2. Data architecture patterns and use cases architecturedescriptionideal use casesLambdaHybrid approach that combines batch and real-time processing; uses a batch layer to process historical data and a real-time layer for real-time data, merging the results for comprehensive analyticsApplications that need historical and real-time dataKappaSimplifies the Lambda architecture, focuses purely on real-time data processing, and removes the need for batch processingInstances where only real-time data is requiredEvent drivenProcesses data based on events triggered by specific actions or conditions, enabling real-time response to changes in dataSituations when instant notifications on data changes are neededMicroservicesModular approach wherein the individual microservices handle specific tasks within the real-time data pipeline, lending scalability and flexibilityComplex systems that need to be modular and scalable These architectures offer adaptable solutions for different real-time data issues, whether the requirement is combining past data, concentrating on current data streams, responding to certain events, or handling complicated systems with modular services. Figure 1. Common data architectures for real-time streaming Integration of Real-Time Data in Cloud Platforms Integrating real-time data with cloud platforms is changing how companies handle and understand their data. It offers quick insights and enhances decision making by using up-to-date information. For the integration process to be successful, you must select the right infrastructure, protocols, and data processing tools for your use case. Key integration strategies include: Integration with on-premises systems. Many organizations combine cloud platforms with on-premises systems to operate in hybrid environments. To ensure data consistency and availability, it is necessary to have efficient real-time data transfer and synchronization between these systems.Integration with third-party APIs and software. The integration of real-time analytics solutions with third-party APIs — such as social media streams, financial data providers, or customer relationship management systems — can improve the quality of insights generated.Data transformation and enrichment. Before analysis, real-time data often needs to be transformed and enriched. Cloud platforms offer tools to make sure the data is in the right format and context for analysis.Ingestion and processing pipelines. Set up automated pipelines that manage data flow from the source to the target, improving real-time data handling without latency. These pipelines can be adjusted and tracked on the cloud platform, providing flexibility and control. Integration of real-time data in cloud platforms involves data ingestion from different data sources and processing in real time by using stream processing frameworks like Apache Flink or Spark Streaming. Data integration can also be used on cloud platforms that support scalable and reliable stream processing. Finally, results are archived in cloud-based data lakes or warehouses, enabling users to visualize and analyze streaming data in real time. Figure 2. Integration of real-time data streams Here are the steps to set up real-time data pipelines on cloud platforms: Select the cloud platform that fits your organization's needs best.Determine the best data ingestion tool for your goals and requirements. One of the most popular data ingestion tools is Apache Kafkadue to its scalability and fault tolerance. If you’re planning to use a managed Kafka service, setup might be minimal. For self-managed Kafka, follow these steps: Identify the data sources to connect, like IoT devices, web logs, app events, social media feeds, or external APIs.Create virtual machines or instances on your cloud provider to host Kafka brokers. Install Kafka and adjust the configuration files as per your requirement.Create Kafka topics for different data streams and set up the partitions to distribute the topics across Kafka brokers. Here is the sample command to create topics using command line interface (CLI). The below command creates a topic stream_data with 2 partitions and a replication factor of 2: Shell bash kafka-topics.sh --create --topic stream_data --bootstrap-server your-broker:9092 --partitions 2 --replication-factor 2 Configure Kafka producers to push real-time data to Kafka topics from various data sources: Utilize the Kafka Producer API to develop producer logic.Adjust batch settings for better performance (e.g., linger.ms, batch.size).Set a retry policy to manage temporary failures. Shell Sample Kafka Producer configuration properties bootstrap.servers=your-kafka-broker:9092 key.serializer=org.apache.kafka.common.serialization.StringSerializer value.serializer=org.apache.kafka.common.serialization.StringSerializer batch.size=15350 linger.ms=5 retries=2 acks=all batch.size sets the max size (bytes) of batch records, linger.ms controls the wait time, and the acks=all setting ensures that data is confirmed only after it has been replicated. Consume messages from Kafka topics by setting up Kafka consumers that subscribed to a topic and process the streaming messages. Once data is added to Kafka, you can use stream processing tools like Apache Flink, Apache Spark, or Kafka Streams to transform, aggregate, and enrich data in real time. These tools operate simultaneously and send the results to other systems. For data storage and retention, create a real-time data pipeline connecting your stream processing engine to analytics services like BigQuery, Redshift, or other cloud storage services. After you collect and save data, use tools such as Grafana, Tableau, or Power BI for analytics and visualization in near real time to enable data-driven decision making. Effective monitoring, scaling, and security are essential for a reliable real-time data pipeline. Use Kafka's metrics and monitoring tools or Prometheus with Grafana for visual displays.Set up autoscaling for Kafka or message brokers to handle sudden increases in load. Leverage Kafka's built-in features or integrate with cloud services to manage access. Enable TLS for data encryption in transit and use encrypted storage for data at rest. Combining Cloud Data Platforms With Real-Time Data Streaming: Benefits and Challenges The real-time data and analytics provided by cloud platforms provide several advantages, including: Improved decision making. Having instant access to data provides real-time insights, helping organizations to make proactive and informed decisions that can affect their business outcomes.Improved customer experience. Through personalized interactions, organizations can engage with customers in real time to improve customer satisfaction and loyalty.Operational efficiency. Automation and real-time monitoring help find and fix issues faster, reducing manual work and streamlining operations.Flexibility and scalability. Cloud platforms allow organizations to adjust their resources according to demand, so they only pay for the services they use while keeping their operations running smoothly.Cost effectiveness. Pay-as-you-go models help organizations use their resources more efficiently by lowering spending on infrastructure and hardware. Despite the advantages, there are many challenges in implementing real-time data and analytics on cloud platforms, including: Data latency and consistency. Applications need to find a balance between how fast they process data and how accurate and consistent that data is, which can be challenging in complex settings.Scalability concerns. Even though cloud platforms offer scalability, handling large-scale real-time processing in practice can be quite challenging in terms of planning and optimization.Integration complexity. Integration of real-time data streaming presses with legacy systems, on-prem infrastructure, or previously implemented solutions can be difficult, especially in hybrid environments; it may need a lot of customization.Data security and privacy. Data security must be maintained throughout the entire process, from collection to storage and analysis. It is important to ensure that real-time data complies with regulations like GDPR and to keep security strong across different systems.Cost management. Cloud platforms are cost effective; however, managing costs can become challenging when processing large volumes of data in real time. It’s important to regularly monitor and manage expenses. Future Trends in Real-Time Data and Analytics in Cloud Platforms The future of real-time data and analytics in cloud platforms is promising, with several trends set to shape the landscape. A few of these trends are outlined below: Innovations in AI and machine learning will have a significant impact on cloud data platforms and real-time data streaming. By integrating AI/ML models into data pipelines, decision-making processes can be automated, predictive insights can be obtained, and data-driven applications can be improved.More real-time data processing is needed closer to the source of data generation as a result of the growth of edge computing and IoT devices. In order to lower latency and minimize bandwidth usage, edge computing allows data to be processed on devices located at the network's edge.Serverless computing is streamlining the deployment and management of real-time data pipelines, reducing the operational burden on businesses. Because of its scalability and affordability, serverless computing models — where the cloud provider manages the infrastructure — are becoming increasingly more common for processing data in real time. In order to support the growing complexity of real-time data environments, these emerging technology trends will offer more flexible and decentralized approaches to data management. Conclusion Real-time data and analytics are changing how systems are built, and cloud data platforms offer the scalability tools and infrastructure needed to efficiently manage real-time data streams. Businesses that use real-time data and analytics on their cloud platforms will be better positioned to thrive in an increasingly data-driven world as technology continues to advance. Emerging trends like serverless architectures, AI integration, and edge computing will further enhance the value of real-time data analytics. These improvements will lead to new ideas in data processing and system performance, influencing the future of real-time data management. This is an excerpt from DZone's 2024 Trend Report,Data Engineering: Enriching Data Pipelines, Expanding AI, and Expediting Analytics.Read the Free Report

By Sreenath Devineni CORE

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm, blending the strengths of information retrieval and natural language generation. By leveraging large datasets to retrieve relevant information and generate coherent and contextually appropriate responses, RAG systems have the potential to revolutionize applications ranging from customer support to content creation. Fundamentals of AI Agents Using RAG and LangChain | Enroll in Free Course* *Affiliate link. See Terms of Use. How Does RAG Work? Let us look at how RAG works. In a traditional setup, you will have a user prompt which is sent to the Large Language Model (LLM), and the LLM provides a completion: But the problem with this setup is that the LLM’s knowledge has a cutoff date, and it does not have insights into business-specific data. Importance of RAG for Accurate Information Retrieval RAG helps alleviate all the drawbacks that are listed above by allowing the LLM to access the knowledge base. Since the LLM now has context, the completions are more accurate and can now include business-specific data. The below diagram illustrates the value add RAG provides to content retrieval: As you can see, by vectorizing business-specific data, which the LLM would not have access to, instead of just sending the prompt to the LLM for retrieval, you send the prompt and context and enable the LLM to provide more effective completions. Challenges With RAG However, as powerful as RAG systems are, they face challenges, particularly in maintaining contextual accuracy and efficiently managing vast amounts of data. Other Challenges include: RAG systems will often find it very difficult to articulate complex relationships between information if it is distributed across a lot of documents.RAG solutions are very limited in their reasoning capabilities on the retrieved data.RAG solutions often tend to hallucinate when they are not able to retrieve desired information. Knowledge Graphs to the Rescue Knowledge graphs are sophisticated data structures that represent information in a graph format, where entities are nodes and relationships are edges. This structure plays a crucial role in overcoming the challenges faced by RAG systems, as it allows for a highly interconnected and semantically rich representation of data, enabling more effective organization and retrieval of information. Benefits of Using Knowledge Graphs for RAG Below are some key advantages for leveraging knowledge graphs: Knowledge graphs help RAG grasp complex information by providing rich context with the interconnected representation of information.With the help of knowledge graphs, RAG solutions can improve their reasoning capabilities when they traverse relationships in a better way. By linking information retrieved to specific aspects of the graph, knowledge graphs help increase factual accuracy. Impact of Knowledge Graphs on RAG Knowledge graphs fundamentally enhance RAG systems by providing a robust framework for understanding and navigating complex data relationships. They enable the AI not just to retrieve information based on keywords, but to also understand the context and interconnections between different pieces of information. This leads to more accurate, relevant, and contextually aware responses, significantly improving the performance of RAG applications. Now let us look at the importance of knowledge graphs in enhancing RAG application through a coding example. To showcase the importance, we will take the example of retrieving a player recommendation for an NFL Fantasy Football draft. We will ask the same question to the RAG application with and without knowledge graphs implemented, and we will see the improvement in the output. RAG Without Knowledge Graphs Let us look at the following code where we implement a RAG solution in its basic level for retrieving a football player of our choosing, which will be provided via a prompt. You can clearly see the output does not retrieve the accurate player based on our prompt. Python from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import numpy as np # Sample player descriptions players = [ "Patrick Mahomes is a quarterback for the Kansas City Chiefs, known for his strong arm and playmaking ability.", "Derrick Henry is a running back for the Tennessee Titans, famous for his power running and consistency.", "Davante Adams is a wide receiver for the Las Vegas Raiders, recognized for his excellent route running and catching ability.", "Tom Brady is a veteran quarterback known for his leadership and game management.", "Alvin Kamara is a running back for the New Orleans Saints, known for his agility and pass-catching ability." ] # Vectorize player descriptions vectorizer = TfidfVectorizer() player_vectors = vectorizer.fit_transform(players) # Function to retrieve the most relevant player def retrieve_player(query, player_vectors, players): query_vector = vectorizer.transform([query]) similarities = cosine_similarity(query_vector, player_vectors).flatten() most_similar_player_index = np.argmax(similarities) return players[most_similar_player_index] # Function to generate a recommendation def generate_recommendation(query, retrieved_player): response = f"Query: {query}\n\nRecommended Player: {retrieved_player}\n\nRecommendation: Based on the query, the recommended player is a good fit for your team." return response # Example query query = "I need a versatile player." retrieved_player = retrieve_player(query, player_vectors, players) response = generate_recommendation(query, retrieved_player) print(response) We have oversimplified the RAG case for ease of understanding. Below is what the above code does: Imports necessary libraries: TfidfVectorizer from sklearn, cosine_similarity from sklearn, and numpyDefines sample player descriptions with details about their positions and notable skillsPlayer descriptions are vectorized using TF-IDF to convert the text into numerical vectors for precise similarity comparison.Defines a function retrieve_player to find the most relevant player based on a query by calculating cosine similarity between the query vector and player vectorsDefines a function generate_recommendation to create a recommendation message incorporating the query and the retrieved player's description Provides an example query, "I need a versatile player.", which retrieves the most relevant player, generates a recommendation, and prints the recommendation message. Now let's look at the output: PowerShell python ragwithoutknowledgegraph.py Query: I need a versatile player. Recommended Player: Patrick Mahomes is a quarterback for the Kansas City Chiefs, known for his strong arm and playmaking ability. Recommendation: Based on the query, the recommended player is a good fit for your team. As you can see, when we were asked for a versatile player, the recommendation was Patrick Mahomes. RAG With Knowledge Graphs Now let us look at how knowledge graphs can help enhance RAG and give a better recommendation. As you see from the output below, the correct player is recommended based on the prompt. Python import rdflib from rdflib import Graph, Literal, RDF, URIRef, Namespace # Initialize the graph g = Graph() ex = Namespace("http://example.org/") # Define players as subjects patrick_mahomes = URIRef(ex.PatrickMahomes) derrick_henry = URIRef(ex.DerrickHenry) davante_adams = URIRef(ex.DavanteAdams) tom_brady = URIRef(ex.TomBrady) alvin_kamara = URIRef(ex.AlvinKamara) # Add player attributes to the graph g.add((patrick_mahomes, RDF.type, ex.Player)) g.add((patrick_mahomes, ex.team, Literal("Kansas City Chiefs"))) g.add((patrick_mahomes, ex.position, Literal("Quarterback"))) g.add((patrick_mahomes, ex.skills, Literal("strong arm, playmaking"))) g.add((derrick_henry, RDF.type, ex.Player)) g.add((derrick_henry, ex.team, Literal("Tennessee Titans"))) g.add((derrick_henry, ex.position, Literal("Running Back"))) g.add((derrick_henry, ex.skills, Literal("power running, consistency"))) g.add((davante_adams, RDF.type, ex.Player)) g.add((davante_adams, ex.team, Literal("Las Vegas Raiders"))) g.add((davante_adams, ex.position, Literal("Wide Receiver"))) g.add((davante_adams, ex.skills, Literal("route running, catching ability"))) g.add((tom_brady, RDF.type, ex.Player)) g.add((tom_brady, ex.team, Literal("Retired"))) g.add((tom_brady, ex.position, Literal("Quarterback"))) g.add((tom_brady, ex.skills, Literal("leadership, game management"))) g.add((alvin_kamara, RDF.type, ex.Player)) g.add((alvin_kamara, ex.team, Literal("New Orleans Saints"))) g.add((alvin_kamara, ex.position, Literal("Running Back"))) g.add((alvin_kamara, ex.skills, Literal("versatility, agility, pass-catching"))) # Function to retrieve the most relevant player using the knowledge graph def retrieve_player_kg(query, graph): # Define synonyms for key skills synonyms = { "versatile": ["versatile", "versatility"], "agility": ["agility"], "pass-catching": ["pass-catching"], "strong arm": ["strong arm"], "playmaking": ["playmaking"], "leadership": ["leadership"], "game management": ["game management"] } # Extract key terms from the query and match with synonyms key_terms = [] for term, syns in synonyms.items(): if any(syn in query.lower() for syn in syns): key_terms.extend(syns) filters = " || ".join([f"contains(lcase(str(?skills)), '{term}')" for term in key_terms]) query_string = f""" PREFIX ex: <http://example.org/> SELECT ?player ?team ?skills WHERE {{ ?player ex:skills ?skills . ?player ex:team ?team . FILTER ({filters}) } """ qres = graph.query(query_string) best_match = None best_score = -1 for row in qres: skill_set = row.skills.lower().split(', ') score = sum(term in skill_set for term in key_terms) if score > best_score: best_score = score best_match = row if best_match: return f"Player: {best_match.player.split('/')[-1]}, Team: {best_match.team}, Skills: {best_match.skills}" return "No relevant player found." # Function to generate a recommendation def generate_recommendation_kg(query, retrieved_player): response = f"Query: {query}\n\nRecommended Player: {retrieved_player}\n\nRecommendation: Based on the query, the recommended player is a good fit for your team." return response # Example query query = "I need a versatile player." retrieved_player = retrieve_player_kg(query, g) response = generate_recommendation_kg(query, retrieved_player) print(response) Let us look at what the above code does. The code: Imports necessary libraries: rdflib, Graph, Literal, RDF, URIRef, and NamespaceInitializes an RDF graph and a custom namespace ex for defining URIsDefines players as subjects using URIs within the custom namespaceAdds player attributes (team, position, skills) to the graph using triplesDefines a function retrieve_player_kg to find the most relevant player based on a query by matching key terms with skills in the knowledge graphUses SPARQL to query the graph, applying filters based on synonyms of key skills extracted from the queryEvaluates query results to find the best match based on the number of matching skillsDefines a function generate_recommendation_kg to create a recommendation message incorporating the query and the retrieved player's informationProvides an example query "I need a versatile player.", retrieves the most relevant player, generates a recommendation, and prints the recommendation message Now let us look at the output: PowerShell python ragwithknowledgegraph.py Query: I need a versatile player. Recommended Player: Player: AlvinKamara, Team: New Orleans Saints, Skills: versatility, agility, pass-catching Recommendation: Based on the query, the recommended player is a good fit for your team. Conclusion: Leveraging RAG for Enhanced Knowledge Graphs Incorporating knowledge graphs into RAG applications results in more accurate, relevant, and context-aware recommendations, showcasing their importance in improving AI capabilities. Here are a few key takeaways: ragwithoutknowledgegraph.py uses TF-IDF and cosine similarity for text-based retrieval, relying on keyword matching for player recommendations.ragwithknowledgegraph.py leverages a knowledge graph, using RDF data structure and SPARQL queries to match player attributes more contextually and semantically.Knowledge graphs significantly enhance retrieval accuracy by adeptly understanding the intricate relationships and context between data entities.They support more complex and flexible queries, improving the quality of recommendations.Knowledge graphs provide a structured and interconnected data representation, leading to better insights.The illustration demonstrates the limitations of traditional text-based retrieval methods.It highlights the superior performance and relevance of using knowledge graphs in RAG applications.The integration of knowledge graphs significantly enhances AI-driven recommendation systems. Additional Resources Below are some of the resources that help with learning knowledge graphs and their impact on RAG solutions. Courses to Learn More About RAG and Knowledge Graphs https://learn.deeplearning.ai/courses/knowledge-graphs-rag/lesson/1/introductionhttps://ieeexplore.ieee.org/document/10698122 Open-Source Tools and Applications https://neo4j.com/generativeai/

The quantity of data generated per second is astonishing in today's digital world. Big data allows organizations and businesses to create new products and services, enabling them to make decisions and enhance customer experiences. However, processing and analyzing large volumes of data can be quite challenging. This is where cloud computing comes into play. Having worked as a cloud computing engineer, I have witnessed how much leeway the adoption of cloud technology has provided in terms of improving big data processing capabilities. This post discusses some advantages of cloud solutions for big data processing and how they ensure the success of organizations. 10 Reasons to Use Cloud for Big Data Processing 1. Scalability One of the major advantages of cloud computing is scalability. In most cases, traditional data processing systems require much money in hardware and software to bear increased loads. Since these services are cloud-based, you may scale up or down according to your needs. The scalability provides an additional advantage to businesses in managing resources efficiently as one pays only for what is required. Whether terabytes of data must be streamed in minutes for some short project or steady data streams over time, the cloud can take on your requirement with less onerous infrastructure change. 2. Cost-Effectiveness Big data solution implementations can be costly for any organization, especially small and medium-scale enterprises. Cloud platforms ensure a pay-per-use pricing model; an organization need not pay in advance for hardware and software. This will help them use their budget effectively to make more valuable investments. Even then, they can leverage fully loaded data-processing capabilities. Moreover, maintenance and updates are also usually within the scope of services provided by cloud service providers. This further reduces the overall costs for companies. 3. Advanced Tools and Technologies Various cloud service providers offer many advanced tools and technologies that simplify big data processing. Most of the time, these tools come equipped with the latest features and updates; this allows an organization to use recent technologies without actually managing them. These cloud platforms have an enormous list of services, from data storage and processing to machine learning, analytics, and more, enabling cloud computing engineers to build and deploy their solutions rapidly. Access to these advanced tools will enormously boost productivity and innovation. 4. Improved Collaboration Success today means collaboration in a work environment that is ever more remote and global. Cloud-based solutions help make this a reality: multiple users can access and analyze the same data in real time. That feature is particularly useful for big data projects, where insights might come from large, diverse teams with different areas of expertise. Moving to the cloud lets an organization ensure all team members have access to the same data and tools for better communication and collaboration. 5. Security and Compliance Data security is among the major big data business concerns. This element makes cloud providers invest much in security measures to protect infrastructures and client data. They offer such features as encryption, identity management, and regular security audits. Besides, many cloud services meet industry standards and regulations, making it easy for businesses to meet compliance requirements. The sensitive nature of the information an organization may handle calls for this added layer of security to provide a sense of assurance to clients and help reduce risk factors. 6. Speed and Performance Cloud computing enables organizations to process their data much faster and more efficiently. With high-performance access to computing resources, cloud platforms can process bulk volumes of data and complex computations much faster than any in-house solution. This speed is of the essence for big data applications, where real-time analysis leads to timely insights and informed decisions. Businesses can use such resources to improve performance and responsiveness to changing market conditions. 7. Simplified Data Management Data management can often become very cumbersome when volumes are large. Often, cloud solutions have embedded tools that make data management easier. These tools organize and store data so that its retrieval is also efficiently done, enabling the cloud computing engineer to analyze rather than wrestle with it. By offering automated backups, data replication, and highly flexible controls for ensuring access, cloud platforms make data management seamless to help an organization ensure data integrity and availability. 8. Disaster Recovery and Backup Solutions A reliable backup and disaster recovery plan will cater to circumstances where data is lost or a system fails. Cloud services offer some of the strongest backup solutions to ensure that data is well-secured and can be recovered quickly. Most cloud providers incorporate disaster recovery into their services, enabling an organization to limit data loss and reduce downtime. This becomes particularly crucial in big data processing, where large amounts lost can lead to significant changes in analysis and results. 9. Leveraging Global Resources Organizations can access global resources in the cloud, helping them reduce the friction and effort required to analyze and process data from different locations. This global reach is also one of the major drivers for businesses with a distributed workforce or those working in multiple regions. Based on cloud infrastructure, organizations may analyze data from different sources and gain a far more complete view of their market. This global perspective shall then enable better decision-making and strategic planning. 10. Continuous Innovation Finally, the cloud enables continuous innovation. Cloud service providers continuously update the services to make them more beneficial for organizations to keep up with the latest technologies and new features. This continuous improvement cycle takes place in such a way that lets businesses be competitive and agile in a fast-changing market. In big data processing solutions, cloud computing engineers can refine and enhance it regularly and utilize new advancements. Summary The advantages of cloud computing for big data processing are numerous and valid. Scalability and cost-effectiveness, among others, in improved collaboration and security, cloud solutions provide organizations with what they need to prosper in a data-driven environment. As big data gets bigger each day, the role of cloud architecture will also be seen to grow further and be more important, molding the future of data processing and analytics. However, for organizations that want to harness big data for their benefit, leveraging cloud technologies is no longer an option but an imperative. An investment in cloud solutions lets the organization unlock the full value of its data to drive meaningful insights that will lead to successful outcomes.

An Apache Kafka outage occurs when a Kafka cluster or some of its components fail, resulting in interruption or degradation of service. Kafka is designed to handle high-throughput, fault-tolerant data streaming and messaging, but it can fail for a variety of reasons, including infrastructure failures, misconfigurations, and operational issues. Why Kafka Outage Occurs Broker Failure Excessive data load or oversized hardware causes a broker to become unresponsive, hardware failure due to hard drive crash, memory exhaustion, or broker network issues. ZooKeeper Issues Kafka relies on Apache ZooKeeper to manage cluster metadata and leader election. ZooKeeper failures (due to network partitions, misconfiguration, or resource exhaustion) can disrupt Kafka operations. The ZooKeeper issues can be omitted if the cluster has been configured in KRaft mode with later version 3.5 of Apache Kafka. Topic Misconfiguration Insufficient replication factors or improper partition configuration can cause data loss or service outages when a broker fails. Network Partitions Communication failures between brokers, clients, or ZooKeeper can reduce availability or cause split-brain scenarios. Misconfiguration Misconfigured cluster settings (retention policies, replica allocation, etc.) can lead to unexpected behavior and failures. Overload A sudden increase in producer or consumer traffic can overload a cluster. Data Corruption Kafka log corruption (due to disk issues or abrupt shutdown) can cause startup or data retrieval issues. Inadequate Monitoring and Alerting If early warning signals (such as spikes in disk usage or long latency) go unrecognized and unaddressed, minor issues can lead to complete failures. Backups of Apache Kafka topics and configurations are important for disaster recovery because they allow us to restore our data and settings in the event of hardware failure, software issues, or human error. Kafka does not have built-in tools for topic backup, but we can achieve this using a couple of methods. How to Back Up Kafka Topics and Configurations There are multiple ways we can follow to back up topics and configurations. Kafka Consumers We can use Kafka consumers to read messages from the topic and store them in external storage like HDFS, S3, or local storage. Using reliable Kafka consumer tools like built-in kafka-console-consumer.sh or custom consumer scripts, all the messages from the topic can be consumed from the earliest offset. This procedure is simple and customizable but requires large storage for high-throughput topics and might lose metadata like timestamps or headers. Kafka Connect By streaming messages from topics to Object Storage using tools like Kafka Connect. We can set up Kafka Connect with a sink connector (e.g., S3 Sink Connector, JDBC Sink Connector, etc.), configure the connector to read from specific topics, and write to the backup destination. Of course, we need to have an additional setup for Kafka Connect. Cluster Replication Kafka's mirroring feature allows us to manage replicas of an existing Kafka cluster. It consumes messages from a source cluster using a Kafka consumer and republishes those messages to another Kafka cluster, which can serve as a backup using an embedded Kafka producer. We need to make sure that the backup cluster is in a separate physical or cloud region for redundancy. Can achieve seamless replication and support incremental backups but higher operational overhead to maintain the backup cluster. Filesystem-Level Copies Filesystem-level backups, such as copying Kafka log directories directly from the Kafka brokers, can be performed by identifying the Kafka log directory (log.dirs in server.properties). This method allows the preservation of offsets and partition data. However, it requires meticulous restoration processes to ensure consistency and avoid potential issues. Kafka Configurations and Metadata In terms of Kafka configuration, we can specify metadata about topics, access control (ACL), server.properties file from all brokers, and the ZooKeeper data directory (as defined by the dataDir parameter in ZooKeeper’s configuration). Subsequently, save the output to a file for reference. We need to ensure all custom settings (e.g., log.retention.ms, num.partitions) should be documented. Using the built-in script kafka-acls.sh, all the acl properties can be consolidated in a flat file. Takeaway The practices discussed above are mainly suitable for clusters deployed on-premises and limited to single-digit nodes configured in the cluster. However, managed service providers handle the operational best practices for running the platform, so we don't need to worry about detecting and fixing issues. By reading this article, I hope you'll gain practical insights and proven strategies to tackle Apache Kafka outages in on-premises deployments.

By Gautam Goswami CORE

All big players in the market use PostgreSQL nowadays. Postgres is just the best and the most popular solution these days, no matter if it’s for startups or enterprise companies. But why is that? What makes PostgreSQL so great and why did companies abandon their enterprise-ready databases like Oracle, MS SQL, or DB2? Read on to find the answers. The world has changed significantly in the last few years. We work with cloud-native applications and microservices, and we rarely deploy monolithic ecosystems. Due to that, many companies (both start-ups and Fortune 500 enterprises) switch from their typical enterprise-ready databases (like Oracle, MS SQL, or IBM DB2) to PostgreSQL. This trend is visible globally and may come as a surprise to some people. In this blog post, we will address a couple of points. First, we analyze if PostgreSQL is indeed the most popular database according to companies and developers. Next, we’ll explore why it is being used by big companies. Finally, we are going to check if it is enterprise-ready and explain what makes it better than other SQL databases. Who Uses Postgres? Before explaining why PostgreSQL is everywhere, let us first check if that’s the case. We know that many companies promote the things they use and tend to focus only on the good sides, but the reality (especially on the enterprise level) quickly verifies these products. Enterprises may easily outscale them and show that they can’t be trusted in demanding environments. Companies Using Postgres Let’s explore some of the big companies that use PostgreSQL in their day-to-day operations. We won’t be going much into how they use it and we’ll just link to their materials. Feel free to explore more on their technical blogs and design documents. Instagram handles millions of photos every day. According to Statista, Instagram has two billion monthly active users as of early 2024. They keep their user data, friendships, media, and others in PostgreSQL, as shown in their tech blog. Reddit has over half a billion accounts as of 2024. To support that, they use PostgreSQL as a ThingDB (sort of key-value store) and as a regular SQL database. See more in their write-up.Skype, with over 300 million monthly active users, uses PostgreSQL for things like batch jobs or queueing. See their presentation.Spotify, with more than 600 million users each month, uses PostgreSQL for various storage needs.Robinhood, with more than 10 million users, uses PostgreSQL in its data lake.Twitch has most of its 125 OLTP databases running PostgreSQL.NASA and its International Space Station use PostgreSQL. We can see many big companies use PostgreSQL in their production systems. Saying that PostgreSQL is everywhere is not an overstatement. It is indeed powering both OLTP and OLAP workloads, with millions of users and transactions every day. Developers and Their Preferences Similarly, developers’ sentiment makes PostgreSQL the most popular database in the world. According to Stack Overflow Developer Survey 2023, PostgreSQL leads the way among professional developers and the overall population: Since developers like Postgres so much, they will encourage their teams and management to use it more. Therefore, we can expect PostgreSQL to grow even more popular in the upcoming years. Postgres Contributors Companies both use and develop PostgreSQL. According to the EDB diagram, AWS, Microsoft, and VMware significantly support the development of PostgreSQL. This shows that PostgreSQL is not a toy but gets strong support from the market and the whole industry. I Like My Oracle and DB2: Why Would I Switch? Let’s now consider why would we even switch to PostgreSQL from other enterprise-ready databases like Oracle, DB2, or Microsoft SQL Server. It is a valid question as PostgreSQL doesn’t have an enormous owner like other databases do and we might suspect that it’s behind other products. Let’s see if that’s the case. PostgreSQL Has Strong Foundations It may come as a surprise that PostgreSQL was actually one of the first Relational Database Management Systems in the world. In 1973, Michael Stonebraker from the University of California Berkeley decided to develop an implementation of the relational model suggested by Edgar F. Codd. It was first named Ingres and was implemented just to demonstrate that the relational model is viable and can compete with other database models of the era (mostly CODASYL and hierarchical). The project didn’t stop after that. In 1980, Stonebraker co-founded Relational Technology, Inc. to produce a commercial version of Ingres. This is how Postgres emerged. The database was used mostly for research and experimentation until Stonebraker co-founded Illustra Information Technologies in 1992 to create a database for all companies around the world. Finally, Postgres dropped QUEL language and switched to SQL in 1994, and was renamed to Postgres95 and PostgreSQL eventually. Can PostgreSQL Replace My Existing Databases? Postgres supports the essential features needed in the enterprise world. Let’s see some of them. Indexes: Postgres supports many types of indexes, including B-Tree, GIN, GiST, BRIN, vector, and others. Postgres can rebuild indexes online.Security: Postgres supports both the privilege system and row-level security policies.Replication and high availability: Postgres supports primary servers and standby replicas. It supports both streaming replication and logical replication. It supports log-shipping, streaming, and other solutions for continuous archiving.Columnar storage: PostgreSQL supports columnar storage thanks to many extensions, like Hydra or ParadeDB.Isolation levels: PostgreSQL supports both true serializable isolation levels and snapshots.Failover and load Balancing: Postgres supports failover and load balancing.Partitioning: PostgreSQL supports table partitioning with various policies.Encryption: Postgres supports encryption on various levels, including column encryption, data partition encryption, or client-side encryption.OLAP: Postgres can deal with OLTP and OLAP workloads.Cloud deployment: PostgreSQL can be deployed in the cloud with AWS RDS, Amazon Aurora, Azure Database for PostgreSQL, or Cloud SQL for PostgreSQL. Vendor lock-in: Postgres can be deployed on-premise or with other infrastructure providers like Tembo. As we can see, PostgreSQL supports everything that is needed for enterprise-ready database systems. However, there is more. Postgres supports many unique things that make it even better when compared to competitors. Let’s read on to see what. Things That Make PostgreSQL Better PostgreSQL is an open-source database. Therefore, anyone can extend it to provide more features. This makes it highly configurable. For instance, AWS extends PostgreSQL with postgresql-logfdw to let users easily read database logs stored in CloudWatch. Postgres can be easily adapted to build highly tailored solutions. For instance, Amazon Redshift can be considered a highly scalable fork of Postgres. It’s a distributed database focusing on OLAP workloads that you can deploy in AWS. Postgres make it easy to adopt new technologies and they can be put inside the database with little-to-no hassle. For example, you can run DuckDB directly inside Postgres thanks to pg_analytics. However, the biggest power of PostgreSQL is its extensions. Postgres has a very extensible extension mechanism that people use to build much more than just SQL features. Let’s see what else can be done with Postgres. Postgres Is Much More Than SQL PostgreSQL is not only an SQL database anymore. Thanks to its powerful extensions, it supports many other workloads and scenarios. Let’s see some of them. We cover them in detail in our other article about PostgreSQL Everywhere. PostgreSQL is capable of storing different types of data. In addition to standard numbers and text, you might need to store more complex data such as nested structures, spatial information, or mathematical formulas. Querying this kind of data can be much slower without the use of specialized data structures that are optimized to comprehend the content of the columns. Thankfully, PostgreSQL offers various extensions and technologies designed to handle non-relational data efficiently. It can deal with XML, JSON, spatial data, intervals, vectors, and much more. Full-text search (FTS) is a technique that involves analyzing every word in a document to find matches with the query. Instead of just locating documents containing the exact phrase, it also identifies similar phrases, accounts for typos, patterns, wildcards, synonyms, and more. This process is more challenging because each query is more complex, increasing the chance of false positives. Additionally, instead of directly scanning each document, the data set needs to be transformed to precompute aggregates, which are then utilized during the search process. PostgreSQL supports FTS with various extensions and can easily outperform Elasticsearch in many production scenarios. For analytical purposes, data is often gathered from various sources, such as SQL and NoSQL databases, e-commerce platforms, data warehouses, blob storage, log files, and clickstreams, among others. This data is typically collected during the ETL process, which involves loading information from different locations. PostgreSQL natively supports this through Foreign Data Wrappers. PostgreSQL can read data from S3, Azure, AWS, data lakes, and other databases. We can easily build a data lake with Postgres. Postgres supports many data storage formats. It can deal with regular tables, columnar storage, parquet files, time series, and much more. This way, we can improve analytical queries and turn Postgres into OLAP or even HTAP solutions. Postgres provides incrementally updated materialized views that are perfect for time series data. We can define aggregates that are constantly recalculated and kept up-to-date even when we modify the data. Postgres can serve as a vector database for AI-based solutions. With the rise of ChatGPT and other large language models, we want to empower our solutions with Retrieval-Augmented Generation. This can be easily implemented with PostgreSQL support for vector operations. Long story short, Postgres can deal with anything. It’s not just an SQL database for OLTP workloads. It can support any modern workload required in cloud-native distributed computing. The Future Is Now and Postgres Is Ready With its extensions mechanism, Postgres can support any type of workload. This makes it a very interesting platform for building solutions for new domains. Instead of building a new database from scratch, we can just extend PostgreSQL with the new capabilities and let it deal with the hard part of optimization, security, user management, and other elements that every production-grade system must have. No matter whether we are talking about custom data types, another in-memory process, or AI use cases, Postgres can be tuned to support new scenarios. When some new requirement comes into play, we don’t need to start from scratch but we just need to extend PostgreSQL with an extension. Postgres is ready for whatever the future brings. Enterprises Don’t Ignore Postgres and Nor Should You PostgreSQL is widely recognized as one of the most popular SQL databases, but it offers far more than just an SQL engine. With a variety of extensions, PostgreSQL can now manage non-relational data, full-text searches, analytical processes, time series, and more. The distinction between OLAP and OLTP is no longer necessary, as PostgreSQL enables the execution of HTAP workflows within a single database. PostgreSQL supports enterprise-level requirements around HA, scalability, permissions, or security. This versatility makes PostgreSQL an exceptionally adaptable database capable of meeting a wide range of requirements and explains why it’s now the most popular database in the world.

By Adam Furmanek CORE