Big Data

Digital twins and IIoTs are evolving technologies that are transforming the digital landscape of supply chain transformation. The IIoT aims to connect to actual physical sensors and actuators. On the other hand, DTs are replica copies that virtually represent the physical components. The DTs are invaluable for testing and simulating design parameters instead of disrupting production elements. Still, the adoption of both technologies remains limited in real-world scenarios. This article explains the Industrial Internet of Things (IIoT) and digital twins (DT) technologies, and how they transform business and the global environment to optimize supply chain ecosystems. Insights of IIoT Technology in Supply Chain Pioneering technologies, such as IoT, are already well-known to businesses. The Industrial Internet of Things (IIoT) extends the networks of physical sensors and controlling software to connect a centralized web console to receive data from distributed smart devices. A few of the market trends show the importance of adopting IIoT technologies: A study performed by Grand View Research emphasizes growth in IIoT technology to $949 billion by 2025. The global marketplace of IIoT is expecting a growth of USD 1,693.44 billion by 2030, with a 23.3% CAGR from 2025 to 2030.A consulting firm, Accenture, predicted a $14 trillion 2030 global turnover of the IIoT industry. Contrary to generic IoT, which is more oriented towards consumers, the IIoT enables the communication and interconnection between different machines, industrial devices, and sensors within a supply chain management ecosystem with the aim of business optimization and efficiency. The incubation of IIoT in supply chain management systems aims to enable real-time monitoring and analysis of industrial environments, including manufacturing, logistics management, and supply chain. It boosts efforts to increase productivity, cut downtime, and facilitate information and accurate decision-making. The IIoT Technology, empowered with advanced innovative technologies, including artificial intelligence, machine learning, and industry automation, can do wonders in the modern competitive marketplace. In the supply chain management pipeline, IIoT has become a 'must-have' technology that can revolutionize it at every stage with smoother and better operations. Advantages of IIoT in Supply Chain Management Real-Time Tracking of Assets Through Sensor Devices To enhance productivity in supply chain management, including tracking raw materials, in-progress assignments, and finished products, IIoT sensors can be deployed on containers, goods, and other assets. It provides better visibility of goods and their conditions, enabling efficient inventory management and planning. Optimization Through Predictive Analytics Global manufacturing firms lose $ 1 trillion annually due to machine downtime, which can be avoided by deploying innovative technologies. Adopting IIoT technologies enables companies to identify machine failures before actual operation disruption proactively. IIoT equipped with predictive analytics algorithms can detect early machinery failure before it occurs. The proactive approach reduces potential downtime, prevents disruptions, and enhances business optimization. Increased Visibility in Supply Chain and Better Transparency Lack of visibility is a significant concern and a big barrier to efficient supply chain management. The IIoT offers visibility to stakeholders by monitoring goods, identifying potential bottlenecks, and facilitating effective collaboration among partners. The IIoT technology provides better insights by giving a comprehensive view of the supply chain management ecosystem, starting from shipment status, goods tracking, adequate transportation, fault tracking, and early prediction of faulty equipment or machinery. The benefits of IIoT are multi-fold. It enhances the production stages and provides an informative decision to authorities that helps in better logistic management, making better and optimized delivery schedules. Early Demand Forecasting and Inventory Management Undoubtedly, IIoT sensors integrated with the supply chain management ecosystem capture real-time data, which can be leveraged for analytics and demand forecasting to take the business to the next level. Businesses can likely face a shortfall during a specific period, and early forecasting of demands can enable businesses to make informed decisions so that production becomes more optimized and accurate to avoid any shortfall or overstock situations. Recommendation and Data-Driven Information Decision By analyzing a large amount of data from IIoT sensors, predictive analytics algorithms can be used to develop a recommender system to facilitate authorities to give pre-emptive and proactive decisions that help the business minimize the risk involved. Organizations can determine the market trends, anticipate the marketplace, and promptly respond to avoid service disruptions or the risks involved in the supply chain management lifecycle. Smart Warehouse Management The incubation of modern technologies, such as IIoT, brings innovative management to the supply chain warehouse. Automated and real-time inventory management takes the business to the next level, be it a computerized picking and packaging procedure, tracking of faulty devices, or in-progress delivery. It boosts business efficiency by reducing manual efforts and minimizing the chance of human errors. Efficient Quality Control The advancement of IIoT in supply chain management offers innovative solutions that facilitate the quality and condition of goods. These innovative solutions ensure the customer receives a better product that meets the quality standards. Consistent delivery of high-quality products to the customer increases the customer's satisfaction, loyalty, and business reputation in the current competitive marketplace. Figure 1: Integration of IIoT in Supply Chain Management Integration of IIoT in Supply Chain and Its Future Like other domains, advanced modern technologies also significantly impact the supply chain. As we progress into the current digital world, IIoT will play a significant role in the transformation of supply chain management. A supply chain equipped with IIoT will be a main ingredient in boosting real-time monitoring and enabling informed decision-making. Every stage of the supply chain ecosystem will have the impact of IIoT, like automated inventory management, health monitoring of goods and their tracking, analytics, and real-time response to meet the current marketplace. IIoT is a boon to supply chain management that will lead to efficient response, adaptability in the evolving modern digital world, and meet customer requirements. The impact of AI algorithms will be the future of supply chain management, which will move towards predictive and prescriptive analytics, forecasting future demands, early identification of risk, and optimizing business efficiency through utilizing the vast amounts of data generated from IIoT devices and sensors. This way, the companies will be more proactive than conventionally reactive, resolving potential risks before they escalate further. Industrial automation will transform the entire supply chain ecosystem, encompassing every phase and aspect. Integrating IIoT will lead to more streamlined, optimized, and error-free business operations. With the transformation from automated inventory management to AI-powered demand forecasting, the supply chain will be scalable, robust, and more reliable in a different paradigm. Digital Twins (DTs): Key Technologies Digital simulation (DT) differs from the industrial Internet of Things and other simulation technologies. DT gives a comprehensive distribution between the physical and digital worlds. DT has five different technologies to connect, collect, and store real-time data in the supply chain ecosystem to provide meaningful insights into the collected data and optimize the functions in the supply chain (Refer to Figure 2), primarily representing the physical objects digitally. IoT is a critical component in the supply chain for better automation.AI provides real-time analytics and predictive forecasting by processing vast amounts of collected data.AR and VR make digital twins a realistic application.Cloud computing offers services without disruption. Blockchain technology is an evolving, decentralized, and growing technology in the supply chain. Figure 2: Technologies of digital twins Conclusion This article describes the benefits of the Industrial Internet of Things (IIoT) and digital twins (DTs) and how they will revolutionize contemporary supply chains, including automation, demand forecasting, predictive analytics, and informed decision-making to minimize risks and issues early in supply chain management. The adoption of DT technology is growing, but its potential advantages to business and the global community are still untapped in digitally connected worlds. However, technological benefits also come with challenges that must be navigated and addressed before they escalate, such as migration cost and complexity, data security, implementation, and operational cost of IIoT. It is smart to stay ahead in the modern business world and streamline your business by adopting the IIoT and DT.

Before you can build an Internet of Things (IoT) application, you need a solid foundation. An IoT framework acts as the scaffolding, ensuring that your system works smoothly and can connect with other devices. A well-structured framework makes it easier for devices to communicate, scale, and stay secure. From picking the right hardware to choosing communication protocols, from setting up edge computing to securing your network, each piece plays a role in creating a reliable and future-ready IoT system. In this guide, we’ll walk through the key steps to building a strong, scalable IoT framework that’s built for performance, security, and real-world application. What Are the Components of an IoT Framework? The IoT framework consists of various hardware, software, and communication protocols. These components work together to create a functional and scalable IoT ecosystem. The four main components of an IoT framework are: Devices/sensors – These are the physical objects or endpoints that collect data from the environment, such as temperature sensors, cameras, GPS trackers, or embedded sensors in industrial equipment. They are responsible for gathering real-world informationConnectivity/network – IoT devices need a way to transmit data, which happens through network protocols like Wi-Fi, Bluetooth, 5G, LPWAN, or satellite connections. These protocols ensure reliable communication between devices and central systems.Data processing/edge and cloud computing – After IoT devices collect and transmit data, the system needs to process it efficiently. In cloud-based IoT frameworks, cloud computing handles large-scale storage, analysis, and advanced processing. However, in peer-to-peer (P2P) architectures like those using Web Real-Time Communication (WebRTC), the cloud acts only as a coordinator to establish direct device-to-device connections. Data flows directly between endpoints, enabling low-latency communication and reducing reliance on centralized infrastructure. Edge computing supports this model by processing data directly on the devices themselves, enabling faster response times and reducing the load on cloud services.User interface and applications – This is the front-end system that allows users to monitor, control, and interact with IoT devices. It can be a mobile app, web dashboard, or automated system that provides insights and triggers actions based on collected data. How to Build IoT Frameworks Building a strong IoT framework requires integrating smart devices, connectivity, and security to create a seamless system. Here’s a step-by-step guide to designing a scalable and secure IoT ecosystem. 1. Define Your Use Case Start by identifying the problem you want to solve. Are you tracking assets in a warehouse? Monitoring home security? Optimizing energy consumption? Clearly defining your IoT use case will guide your hardware, software, and network choices and tell you whether P2P or cloud-based frameworks are the best option. 2. Choose the Right Hardware The hardware consists of the IoT devices that collect real-world data. This includes sensors, edge devices, and a power supply. Sensors capture key metrics, including temperature, energy usage, and motion. Edge devices — including microcontrollers like Arduino and ESP32, or single-board computers like Raspberry Pi — process data locally before transmitting it to the cloud or other devices through peer-to-peer (P2P) connections. Power supply is another critical decision, as it directly influences your deployment options and maintenance. Battery-powered devices offer great flexibility and are ideal for remote environments, but they may limit processing capabilities and require power-saving communication protocols. Wired power offers continuous operation and higher performance, especially in static or industrial settings. Many real-world applications use split systems, where the sensor device is battery-operated for flexibility, and a nearby gateway (such as a wired hub or router) handles communication and power-intensive tasks. This setup balances energy efficiency, range, and performance. 3. Select the Best Communication Protocol Efficient connectivity is crucial for IoT devices to transmit data reliably while optimizing power consumption and range. The choice of network depends on the application’s needs, balancing factors like coverage, bandwidth, and energy efficiency. Short-range connectivity options such as Wi-Fi, Bluetooth, and Zigbee are ideal for smart home devices, wearables, and consumer IoT applications that operate within a limited physical area. These protocols offer high data transfer rates but typically require more power and have shorter communication distances. For broader coverage, long-range networks like LoRaWAN, NB-IoT, LTE-M, and 5G can support industrial IoT, smart cities, and remote monitoring systems. LoRaWAN is well-suited for low-power, long-range applications such as environmental sensors. Meanwhile, NB-IoT and LTE-M provide cellular-based coverage for asset tracking and large-scale deployments. 5G enables ultra-fast, low-latency communication, making it suitable for real-time applications. 4. Implement Edge Computing Edge computing reduces latency by processing data close to the source instead of transmitting it to the cloud. Edge platforms, such as AWS Greengrass, Azure IoT Edge, and Google Edge TPU, enhance real-time decision-making and responsiveness. 5. Develop a User-Friendly Application Layer A well-designed interface ensures that users can monitor, control, and interact with IoT devices effortlessly. Depending on the application, you can build mobile apps, web dashboards, or voice-enabled assistants to provide real-time insights and remote management. 6. Secure Your IoT Framework Cybersecurity is critical in IoT. Protect your devices and data with end-to-end encryption (TLS/SSL), device authentication, and regular firmware updates to patch vulnerabilities. Final Thoughts Building a strong IoT framework is essential for ensuring the success, scalability, and security of your application. A well-structured system integrates reliable hardware, efficient connectivity, edge computing, a user-friendly interface, and robust security measures to create a cohesive and future-proof IoT ecosystem. As IoT technology evolves, prioritizing interoperability, data privacy, and real-time processing will help your application stay competitive and adaptable to new advancements. By following these best practices, you can develop an IoT system that not only meets current needs but is also prepared for the future. Good luck.

In this short article, we'll explore loan approvals using a variety of tools and techniques. We'll begin by analyzing loan data and applying Logistic Regression to predict loan outcomes. Building on this, we'll integrate BERT for Natural Language Processing to enhance prediction accuracy. To interpret the predictions, we'll use SHAP and LIME explanation frameworks, providing insights into feature importance and model behavior. Finally, we'll explore the potential of Natural Language Processing through LangChain to automate loan predictions, using the power of conversational AI. The notebook file used in this article is available on GitHub. Introduction In this article, we'll explore various techniques for loan approvals, using models like Logistic Regression and BERT, and applying SHAP and LIME for model interpretation. We'll also investigate the potential of using LangChain for automating loan predictions with conversational AI. Create a SingleStore Cloud Account A previous article showed the steps to create a free SingleStore Cloud account. We'll use the Free Shared Tier and take the default names for the Workspace and Database. Import the Notebook We'll download the notebook from GitHub (linked earlier). From the left navigation pane in the SingleStore Cloud portal, we'll select DEVELOP > Data Studio. In the top right of the web page, we'll select New Notebook > Import From File. We'll use the wizard to locate and import the notebook we downloaded from GitHub. Run the Notebook After checking that we are connected to our SingleStore workspace, we'll run the cells one by one. We'll begin by installing the necessary libraries and importing dependencies, followed by loading the loan data from a CSV file containing nearly 600 rows. Since some rows have missing data, we'll drop the incomplete ones for the initial analysis, reducing the dataset to around 500 rows. Next, we'll further prepare the data and separate the features and target variables. Visualizations can provide great insights into data, and we'll begin by creating a heatmap that shows the correlation between numeric-only features, as shown in Figure 1. Figure 1: Heatmap We can see that the Loan Amount and the Applicant Income are strongly related. If we plot the Loan Amount against Applicant Income, we can see that most of the data points are towards the bottom left of the scatter plot, as shown in Figure 2. Figure 2: Scatter Plot So, incomes are generally quite low and loan applications are also for fairly small amounts. We can also create a pair plot for the Loan Amount, Applicant Income, and Co-applicant Income, as shown in Figure 3. Figure 3: Pair Plot In most cases, we can see that the data points tend to cluster together and there are generally few outliers. We'll now perform some feature engineering. We'll identify the categorical values and convert these to numerical values and also use one-hot encoding where required. Next, we'll create a model using Logistic Regression as there are only two possible outcomes: either a loan application is approved or it is denied. If we visualize the feature importance, we can make some interesting observations, as shown in Figure 4. Figure 4: Feature Importance For example, we can see that Credit History is obviously very important. However, Marital Status and Gender are also important. We'll now make predictions using one test sample. We'll generate a loan application summary using Bidirectional Encoder Representations from Transformers (BERT) with the test sample. Example output: Plain Text BERT-Generated Loan Application Summary applicant : mr blobby income : $ 7787. 0 credit history : 1. 0 loan amount : $ 240. 0 property area : urban area Model Prediction ('Y' = Approved, 'N' = Denied): Y Loan Approval Decision: Approved Using the BERT-generated summary, we'll create a word cloud as shown in Figure 5. Figure 5: Word Cloud We can see that the applicant's name, income, and credit history are larger and more prominent. Another way we can analyze the data for our test sample is by using SHapley Additive exPlanations (SHAP). In Figure 6 we can visually see features that are important. Figure 6: SHAP A SHAP force plot is another way we could analyze the data, as shown in Figure 7. Figure 7: Force Plot We can see how each feature contributes to a particular prediction for our test sample by showing the SHAP values in a visual way. Another very useful library is Local Interpretable Model-Agnostic Explanations (LIME). The results for this can be generated in the accompanying notebook to this article. Next, we'll create a Confusion Matrix (Figure 8) for our Logistic Regression model and generate a classification report. Figure 8: Confusion Matrix The results shown in Figure 8 are a little mixed, but the classification report contains some good results: Plain Text Accuracy: 0.84 Precision: 0.83 Recall: 0.98 F1-score: 0.90 Classification Report: precision recall f1-score support N 0.91 0.49 0.64 43 Y 0.83 0.98 0.90 109 accuracy 0.84 152 macro avg 0.87 0.74 0.77 152 weighted avg 0.85 0.84 0.82 152 Overall, we can see that using existing Machine Learning tools and techniques gives us many possible ways to analyze the data and find interesting relationships, particularly down to the level of an individual test sample. Next, let's use LangChain and an LLM and see if we can also make loan predictions. Once we have set up and configured LangChain, we'll test it with two examples, but restrict access to the quantity of data so that we don't exceed token and rate limits. Here is the first example: Python query1 = ( """ Build a loan approval model. Limit your access to 10 rows of the table. Use your model to determine if the following loan would be approved: Gender: Male ApplicantIncome: 7787.0 Credit_History: 1 LoanAmount: 240.0 Property_Area_Urban: 1 Limit your reply to either 'Approved' or 'Denied'. """ ) result1 = run_agent_query(query1, agent_executor, error_string) print(result1) In this case, the application was approved in the original dataset. Here is the second example: Python query2 = ( """ Build a loan approval model. Limit your access to 10 rows of the table. Use your model to determine if the following loan would be approved: Gender: Female ApplicantIncome: 5000.0 Credit_History: 0 LoanAmount: 103.0 Property_Area_Semiurban: 1 Limit your reply to either 'Approved' or 'Denied'. """ ) result2 = run_agent_query(query2, agent_executor, error_string) print(result2) In this case, the application was denied in the original dataset. Running these queries, we may get inconsistent results. This may be due to restricting the quantity of data that can be used. We can also use verbose mode in LangChain to see the steps being used to build a loan approval model, but there is insufficient information at this initial level about the detailed steps to create that model. More work is needed with conversational AI, as many countries have fair lending rules and we'd need a detailed explanation about why the AI approved or denied a particular loan application. Summary Today, many powerful tools and techniques enhance Machine Learning (ML) for deeper insights into data and loan prediction models. AI, through Large Language Models (LLMs) and modern frameworks, offers great potential to augment or even replace traditional ML approaches. However, for greater confidence in AI's recommendations and to comply with legal and fair lending requirements in many countries, it's crucial to understand the AI's reasoning and decision-making process.

By Akmal Chaudhri CORE

PostgreSQL employs sophisticated techniques for data storage and indexing to ensure efficient data management and fast query performance. This guide explores PostgreSQL's mechanisms, showcases practical examples, and includes simulated performance metrics to illustrate the impact of indexing. Data Storage in PostgreSQL Table Structure and TOAST (The Oversized-Attribute Storage Technique) Table Structure: PostgreSQL stores table data in a format known as a heap. Each table's heap contains one or more pages (blocks), where each page is typically 8KB in size. This size can be altered when compiling PostgreSQL from source. PostgreSQL organizes table data in a heap structure with 8KB pages by default. Rows exceeding a page size are handled using TOAST, which compresses and stores oversized attributes in secondary storage. Example: Managing Large Text Data Consider a documents table: SQL CREATE TABLE documents ( doc_id SERIAL PRIMARY KEY, title TEXT, content TEXT ); Scenario: Storing a document with 10MB of content.Without TOAST: The entire document resides in the table, slowing queries.With TOAST: The content is compressed and stored separately, leaving a pointer in the main table. Expected Performance Improvement MetricWithout TOASTWith TOASTQuery Execution Time~4.2 seconds~2.1 seconds (50% faster) TOAST significantly reduces table size, enhancing read and write efficiency. MVCC (Multi-Version Concurrency Control): Consistency with Row Versions: PostgreSQL uses MVCC to ensure data consistency and support concurrent transactions. Each transaction sees a snapshot of the database, isolating it from others and preventing locks during long queries.Transaction Management with XIDs: Each row version includes Transaction IDs (XIDs) to indicate when it was created and when it expired. This enables PostgreSQL to manage concurrency and recovery efficiently. For example, while editing an inventory item during a sales report generation, MVCC ensures the sales report sees the original data while the update operates independently. Indexing in PostgreSQL Indexes in PostgreSQL optimize queries by reducing the need for full-table scans. Below are examples showcasing indexing techniques, their use cases, and expected improvements. B-Tree Index: Default for Range Queries B-tree indexes are efficient for equality and range queries. Example: Product Price Filtering Given a products table: SQL CREATE TABLE products ( product_id SERIAL PRIMARY KEY, name TEXT, price NUMERIC ); Query Without Index SQL SELECT * FROM products WHERE price BETWEEN 50 AND 100; Execution Time: ~8.3 seconds (full scan on 1 million rows). Query With B-Tree Index MariaDB SQL CREATE INDEX idx_price ON products(price); SELECT * FROM products WHERE price BETWEEN 50 AND 100; Execution Time: ~0.6 seconds (direct row access). Performance Improvement MetricWithout IndexWith IndexImprovement (%)Query Execution Time~8.3 seconds~0.6 seconds~92.8% faster Hash Index: Fast Equality Searches Hash indexes are ideal for simple equality searches. Example: User Email Lookup Given a users table: SQL CREATE TABLE users ( user_id SERIAL PRIMARY KEY, name TEXT, email TEXT UNIQUE ); Query Without Index SQL SELECT * FROM users WHERE email = '[email protected]'; Execution Time: ~4.5 seconds (scans 500,000 rows). Query With Hash Index SQL CREATE INDEX idx_email_hash ON users USING hash(email); SELECT * FROM users WHERE email = '[email protected]'; Execution Time: ~0.3 seconds. Performance Improvement MetricWithout IndexWith IndexImprovement (%)Query Execution Time~4.5 seconds~0.3 seconds~93.3% faster GiST Index: Handling Spatial Data GiST indexes are designed for complex data types, such as geometric or spatial queries. Example: Store Locator Given a locations table: SQL CREATE TABLE locations ( location_id SERIAL PRIMARY KEY, name TEXT, coordinates GEOMETRY(Point, 4326) ); Query Without Index SQL SELECT * FROM locations WHERE ST_DWithin(coordinates, ST_MakePoint(40.748817, -73.985428), 5000); Execution Time: ~6.7 seconds. Query With GiST Index SQL CREATE INDEX idx_coordinates_gist ON locations USING gist(coordinates); SELECT * FROM locations WHERE ST_DWithin(coordinates, ST_MakePoint(40.748817, -73.985428), 5000); Execution Time: ~1.2 seconds. Performance Improvement MetricWithout IndexWith IndexImprovement (%)Query Execution Time~6.7 seconds~1.2 seconds~82% faster GIN Index: Full-Text Search GIN indexes optimize composite or multi-value data types, such as arrays or JSON. Example: Tag Search Given an articles table: SQL CREATE TABLE articles ( article_id SERIAL PRIMARY KEY, title TEXT, tags TEXT[] ); Query Without Index SQL SELECT * FROM articles WHERE tags @> ARRAY['technology']; Execution Time: ~9.4 seconds. Query With GIN Index SQL CREATE INDEX idx_tags_gin ON articles USING gin(tags); SELECT * FROM articles WHERE tags @> ARRAY['technology']; Execution Time: ~0.7 seconds. Performance Improvement MetricWithout IndexWith IndexImprovement (%)Query Execution Time~9.4 seconds~0.7 seconds~92.6% faster BRIN Index: Large Sequential Datasets BRIN indexes summarize data blocks, suitable for massive sequential datasets. Example: Log File Queries Given a logs table: SQL CREATE TABLE logs ( log_id SERIAL PRIMARY KEY, log_time TIMESTAMP, message TEXT ); Query Without Index SQL SELECT * FROM logs WHERE log_time BETWEEN '2023-01-01' AND '2023-01-31'; Execution Time: ~45 seconds. Query With BRIN Index SQL CREATE INDEX idx_log_time_brin ON logs USING brin(log_time); SELECT * FROM logs WHERE log_time BETWEEN '2023-01-01' AND '2023-01-31'; Execution Time: ~3.2 seconds. Performance Improvement MetricWithout IndexWith IndexImprovement (%)Query Execution Time~45 seconds~3.2 seconds~92.9% faster Performance Considerations Impact on Writes: Indexes can slow down INSERT, UPDATE, or DELETE operations as they require updates to all associated indexes. Balancing the number and type of indexes is crucial. Example: An orders table with multiple indexes may experience slower insert speeds, requiring careful optimization. Index Maintenance: Over time, indexes can fragment and degrade in performance. Regular maintenance with commands like REINDEX can restore efficiency: SQL REINDEX INDEX idx_salary; Using Execution Plans: Analyze queries with EXPLAIN to understand index usage and identify performance bottlenecks: SQL EXPLAIN SELECT * FROM employees WHERE salary BETWEEN 50000 AND 70000; Conclusion PostgreSQL employs effective storage and indexing strategies, such as the TOAST mechanism for handling oversized data and various specialized index types, to significantly enhance query performance. This guide provides examples and performance metrics that showcase the tangible benefits of using indexes in various scenarios. By applying these techniques, database engineers can optimize both read and write operations, leading to robust and scalable database systems.

By arvind toorpu CORE

Near real-time decision-making systems are critical for modern business applications. Integrating Apache Spark (Streaming) and Drools provides scalability and flexibility, enabling efficient handling of rule-based decision-making at scale. This article showcases their integration through a loan approval system, demonstrating its architecture, implementation, and advantages. Problem Statement Applying numerous rules using Spark user-defined functions (UDFs) can become complex and hard to maintain due to extensive if-else logic. Solution Drools provides a solution through DRL files or decision tables, allowing business logic to be written in a business-friendly language. Rules can be dynamically managed and applied if included in the classpath. Use Case While streaming data from upstream systems, Drools enables simple decision-making based on defined rules. Implementation Overview This demo is implemented as a standalone program in IntelliJ for easy testing without requiring a cluster. The following library versions are used: ScalaDroolsApache Spark System Architecture Overview Diagram: High-Level System Architecture This diagram illustrates the flow of data and components involved in the system: Input data: Loan applications.Apache Spark: Ingests and processes data in a distributed manner.Drools engine: Applies business rules to each application.Output: Approved or rejected applications. Workflow Data ingestion: Applicant data is loaded into Spark as a DataFrame.Rule execution: Spark partitions the data, and Drools applies rules on each partition.Result generation: Applications are classified as approved or rejected based on the rules. Step 1: Define the Model A Scala case class ApplicantForLoan represents loan applicant details, including credit score and requested amount. Below is the code: ApplicantForLoan.scala Scala package com.droolsplay case class ApplicantForLoan(id: Int, firstName: String, lastName: String, requestAmount: Int, creditScore: Int) extends Serializable { private var approved = false def getFirstName: String = firstName def getLastName: String = lastName def getId: Int = id def getRequestAmount: Int = requestAmount def getCreditScore: Int = creditScore def isApproved: Boolean = approved def setApproved(_approved: Boolean): Unit = { approved = _approved } } Step 2: Define the Drools Rule A self-explanatory DRL file (loanApproval.drl) defines the rule for loan approval based on a credit score: Java package com.droolsplay; /** * @author : Ram Ghadiyaram */ import com.droolsplay.ApplicantForLoan rule "Approve_Good_Credit" when a: ApplicantForLoan(creditScore >= 680) then a.setApproved(true); end Step 3: Configure Drools Knowledge Base Drools 6 supports declarative configuration of the knowledge base and session in a kmodule.xml file: XML <?xml version="1.0" encoding="UTF-8"?> <kmodule xmlns="http://jboss.org/kie/6.0.0/kmodule"> <kbase name="AuditKBase" default="true" packages="com.droolsplay"> <ksession name="AuditKSession" type="stateless" default="true"/> </kbase> </kmodule> A KieBase is a repository of knowledge definitions (rules, processes, functions, and type models) but does not contain data. Sessions are created from the KieBase to insert data and start processes. Creating a KieBase is resource-intensive, but session creation is lightweight, so caching the KieBase is recommended. The KieContainer automatically handles this caching. Step 4: Create a Utility for Rule Application The DroolUtil object handles loading and applying rules, and SparkSessionSingleton ensures a single Spark session: Scala package com.droolsplay.util import com.droolsplay.ApplicantForLoan import org.apache.log4j.Logger import org.apache.spark.internal.Logging import org.apache.spark.sql.{DataFrame, SparkSession} import org.kie.api.{KieBase, KieServices} import org.kie.internal.command.CommandFactory /** * @author : Ram Ghadiyaram */ object DroolUtil extends Logging { /** * loadRules. * * @return KieBase */ def loadRules: KieBase = { val kieServices = KieServices.Factory.get val kieContainer = kieServices.getKieClasspathContainer kieContainer.getKieBase } /** * applyRules. * * @param base * @param applicant * @return ApplicantForLoan */ def applyRules(base: KieBase, applicant: ApplicantForLoan): ApplicantForLoan = { val session = base.newStatelessKieSession session.execute(CommandFactory.newInsert(applicant)) logTrace("applyrules ->" + applicant) applicant } /** * checkDFSize * * @param spark * @param applicantsDS * @return */ def checkDFSize(spark: SparkSession, applicantsDS: DataFrame) = { applicantsDS.cache.foreach(x => x) val catalyst_plan = applicantsDS.queryExecution.logical // just to check dataframe size val df_size_in_bytes = spark.sessionState.executePlan( catalyst_plan).optimizedPlan.stats.sizeInBytes.toLong df_size_in_bytes } } /** Lazily instantiated singleton instance of SparkSession */ object SparkSessionSingleton extends Logging { val logger = Logger.getLogger(this.getClass.getName) @transient private var instance: SparkSession = _ def getInstance(): SparkSession = { logDebug(" instance " + instance) if (instance == null) { instance = SparkSession .builder .config("spark.master", "local") //.config("spark.eventLog.enabled", "true") .appName("AppDroolsPlayGroundWithSpark") .getOrCreate() } instance } } Step 5: Implement the Spark Driver The main Spark driver (App) processes the input data and applies the rules: Scala package com.droolsplay import com.droolsplay.util.DroolUtil._ import com.droolsplay.util.SparkSessionSingleton import org.apache.spark.SparkConf import org.apache.spark.internal.Logging import org.apache.spark.sql.functions._ /** * @author : Ram Ghadiyaram */ object App extends Logging { def main(args: Array[String]): Unit = { // prepare some funny input data val inputData = Seq( ApplicantForLoan(1, "Ram", "Ghadiyaram", 680, 680), ApplicantForLoan(2, "Mohd", "Ismail", 12000, 679), ApplicantForLoan(3, "Phani", "Ramavajjala", 100, 600), ApplicantForLoan(4, "Trump", "Donald", 1000000, 788), ApplicantForLoan(5, "Nick", "Suizo", 10, 788), ApplicantForLoan(7, "Sreenath", "Mamilla", 10, 788), ApplicantForLoan(8, "Naveed", "Farroqui", 10, 788), ApplicantForLoan(9, "Ashish", "Anand", 1000, 788), ApplicantForLoan(10, "Vasudha", "Nanduri", 1001, 788), ApplicantForLoan(11, "Tathagatha", "das", 1002, 788), ApplicantForLoan(12, "Sean", "Owen", 1003, 788), ApplicantForLoan(13, "Sandy", "Raza", 1004, 788), ApplicantForLoan(14, "Holden", "Karau", 1005, 788), ApplicantForLoan(15, "Gobinathan", "SP", 1005, 7), ApplicantForLoan(16, "Arindam", "SenGupta", 1005, 670), ApplicantForLoan(17, "NIKHIL", "POTLAPALLY", 100, 670), ApplicantForLoan(18, "Phanindra", "Ramavojjala", 100, 671) ) val spark = SparkSessionSingleton.getInstance() // load drl file using loadRules from the DroolUtil class shown above... val rules = loadRules /*** broadcast all the rules using broadcast variable ***/ val broadcastRules = spark.sparkContext.broadcast(rules) val applicants = spark.sparkContext.parallelize(inputData) logInfo("list of all applicants " + applicants.getClass.getName) import spark.implicits._ val applicantsDS = applicants.toDF() applicantsDS.show(false) val df_size_in_bytes: Long = checkDFSize(spark, applicantsDS) logInfo("byteCountToDisplaySize - df_size_in_bytes " + df_size_in_bytes) logInfo(applicantsDS.rdd.toDebugString) val approvedguys = applicants.map { x => { logDebug(x.toString) applyRules(broadcastRules.value, x) } }.filter((a: ApplicantForLoan) => (a.isApproved == true)) logInfo("approvedguys " + approvedguys.getClass.getName) approvedguys.toDS.withColumn("Remarks", lit("Good Going!! your credit score =>680 check your score in https://www.creditkarma.com")).show(false) var numApproved: Long = approvedguys.count logInfo("Number of applicants approved: " + numApproved) /** ** * another way to do it with dataframes just an example not required to execute this code above rdd applicants * is sufficient to get isApproved == false */ val notApprovedguys = applicantsDS.rdd.map { row => applyRules(broadcastRules.value, ApplicantForLoan( row.getAs[Int]("id"), row.getAs[String]("firstName"), row.getAs[String]("lastName"), row.getAs[Int]("requestAmount"), row.getAs[Int]("creditScore")) ) }.filter((a: ApplicantForLoan) => (a.isApproved == false)) logInfo("notApprovedguys " + notApprovedguys.getClass.getName) notApprovedguys.toDS().withColumn("Remarks", lit("credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com")).show(false) val numNotApproved: Long = notApprovedguys.count logInfo("Number of applicants NOT approved: " + numNotApproved) } } The results of applying the Drools rules to the input data are presented below in a table format, showing the original DataFrame and the outcomes for both approved and non-approved applicants. Original DataFrame The original DataFrame contained 18 records with dummy data, as shown below: id firstName lastName requestAmount creditScore 1 Ram Ghadiyaram 680 680 2 Mohd Ismail 12000 679 3 Phani Ramavajjala 100 600 4 Trump Donald 1000000 788 5 Nick Suizo 10 788 7 Sreenath Mamilla 10 788 8 Naveed Farroqui 10 788 9 Ashish Anand 1000 788 10 Vasudha Nanduri 1001 788 11 Tathagatha das 1002 788 12 Sean Owen 1003 788 13 Sandy Raza 1004 788 14 Holden Karau 1005 788 15 Gobinathan SP 1005 7 16 Arindam SenGupta 1005 670 17 NIKHIL POTLAPALLY 100 670 18 Phanindra Ramavojjala 100 671 Approved Applicants After applying the DRL rule (creditScore >= 680), 11 applicants were approved. The log output indicates: 18/11/03 00:15:38 INFO App: Number of applicants approved: 11. The approved applicants are: id firstName lastName requestAmount creditScore Remarks 1 Ram Ghadiyaram 680 680 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 4 Trump Donald 1000000 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 5 Nick Suizo 10 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 7 Sreenath Mamilla 10 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 8 Naveed Farroqui 10 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 9 Ashish Anand 1000 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 10 Vasudha Nanduri 1001 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 11 Tathagatha das 1002 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 12 Sean Owen 1003 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 13 Sandy Raza 1004 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com 14 Holden Karau 1005 788 Good Going!! your credit score =>680 check your score in https://www.creditkarma.com Non-Approved Applicants Six applicants did not meet the rule criteria (creditScore < 680). The log output indicates: 18/11/03 00:15:39 INFO App: Number of applicants NOT approved: 6. The not approved applicants are: id firstName lastName requestAmount creditScore Remarks 2 Mohd Ismail 12000 679 credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com 3 Phani Ramavajjala 100 600 credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com 15 Gobinathan SP 1005 7 credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com 16 Arindam SenGupta 1005 670 credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com 17 NIKHIL POTLAPALLY 100 670 credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com 18 Phanindra Ramavojjala 100 671 credit score <680 Need to improve your credit history!!! check your score : https://www.creditkarma.com Applications This approach is applicable across various domains, including but not limited to the following. It can also be utilized in Spark Streaming applications to process data in real-time or near real-time: Finance: Trade analysis, fraud detection, loan approval, or rejectionAirlines: Operations monitoringHealthcare: Claims processing, patient monitoring, Cancer report evaluation, drug evaluation based on symptomsEnergy and Telecommunications: Outage detection Note: In this project, the DRL file is stored in resources/META-INF. In real-world applications, rules are often stored in databases, allowing dynamic updates without redeploying the application. Conclusion This demo illustrates how Apache Spark and Drools can be integrated to streamline rule-based decision-making, such as loan approvals based on credit scores. By leveraging Drools' rule engine and Spark's data processing capabilities, complex business logic can be managed efficiently. For the complete code and setup, refer to my GitHub repository, which has been found to be useful and archived in the Arctic Code Vault (code preserved for future generations). Disclaimer: The input data, result names, and URL are indicated for illustrative purposes only. I don't affiliate with any person or organization.

The Internet of Things (IoT) is no longer just about connectivity. Today, IoT systems are becoming intelligent ecosystems that make real-time decisions. The convergence of edge computing and artificial intelligence (AI) is driving this transformation, meaning that IoT devices can now locally process their own data, then act autonomously. This revolutionizes industries, from healthcare and agriculture to smart cities and autonomous vehicles. When Edge Computing Meets AI Traditional IoT has a central cloud architecture used for data processing and analysis. While effective, this model struggles to meet the demands of real-time applications due to: Latency: Transmitting data to and from the cloud can be delayed, and that can delay critical decision-making.Bandwidth: IoT data can overwhelm networks and increase costs when large volumes of it need to be transmitted to the cloud.Privacy: Breachable and compliance-violating sensitive data sent to centralized servers. By running AI capabilities at the edge, IoT devices can run analyses locally, without the delay of transmitting data to the cloud, providing faster, more secure, and affordable operations. Edge-AI IoT Systems Have Key Applications Wearable devices like smart watches monitor real-time health metrics, like heart rate and blood oxygen levels, and alert users and healthcare providers of any anomalies without the need to send data to the cloud. AI algorithms running at the edge assist in the early diagnosis of arrhythmias and sleep apnea.Edge AI IoT systems manage traffic lights, reducing congestion by dynamically adjusting signals according to real-time vehicle data.Edge AI sensors embedded in waste management systems optimize garbage collection schedules, saving resources and reducing emissions.With AI-driven image recognition, edge-enabled drones analyze crop health so farmers can focus irrigation and pest control efforts where it’s needed most. Soil sensors used localized AI to suggest when to plant and how much fertilizer to use, maximizing yield while limiting resource usage. Edge AI systems are used to process data from cameras, LIDAR, and sensors in self-driving cars immediately for safe navigation, without waiting to receive instructions from the cloud. Stores use AI for smart shelves that monitor inventory and customer behavior, giving insight into product placement and stock replenishment. Technological Synergies The intersection of edge computing and AI is made possible by advancements in several key areas, such as: Hardware Acceleration: Specialized chips like GPUs and TPUs can make sure IoT devices run AI models efficiently at the edge.On-Device Machine Learning: ML models that are lightweight minimizes computation while keeping accuracy, which tend to fit better on edge devices.5G Connectivity: Edge AI IoT systems are better served by high-speed, low-latency 5G networks.Federated Learning: By training the AI models collaboratively across edge devices, data privacy is maintained, yet system-wide intelligence is improved. Challenges in Implementation Despite its potential, integrating AI with edge computing in IoT systems presents challenges: Hardware Constraints: Many IoT devices have very limited processing power and memory, making it challenging to run complex AI models.Interoperability: Integration efforts in IoT ecosystems often involve a variety of devices and standards, making it quite complex.Cost: Edge-AI systems are expensive to develop and deploy, especially for smaller and medium-sized enterprises.Security Risks: Edge computing cuts down on data exposure, but the edge devices themselves are also potential targets for cyberattacks. The Future of Edge-AI IoT Systems AI-Driven Maintenance: Predictive maintenance will be pervasive everywhere, with less equipment downtime and a longer lifespan.Decentralized AI Networks: IoT systems will increasingly leverage decentralized networks of AI, powered by devices learning and adapting together in a collaborative way rather than relying on centralized data hubs.Energy Efficiency: Low-power AI hardware advances will enable sustainable edge-AI IoT systems, which are important in the remote or resource-constrained application area.Next-Generation Smart Cities: Next-generation urban infrastructure will be built on Edge AI IoT systems, such as self-healing power grids, intelligent transportation systems, and real-time disaster management. The combination of edge computing and AI is not just improving IoT systems; it’s starting to redefine what they can do. These technologies are making ecosystems that are smarter and more responsive by enabling devices to think, learn, and act autonomously. Industries are now adopting Edge AI IoT solutions, and the benefits of increased efficiency, security, and innovation will restructure how we live and work in a connected world.

Reddit offers an invaluable trove of community-driven discussions that provide rich data for computational analysis. As researchers and computer scientists, we can extract meaningful insights from these social interactions using modern data engineering and AI techniques. In this article, I'll demonstrate how to build a sophisticated Reddit intelligence engine that goes beyond basic web scraping to deliver actionable analytical insights using Ollama for local LLM inference. The Computational Challenge Social media data analysis presents several unique computational challenges that make it an ideal candidate for advanced pipeline architecture: Scale and velocity: Reddit generates millions of interactions dailySemi-structured data: Posts, comments, and metadata require normalizationNatural language complexity: Requires sophisticated ML/AI approachesComputational efficiency: Processing must be optimized for research workflows Architecture Overview The pipeline implements a modular, two-stage computational workflow designed for reproducibility and extensibility: Figure 1: Architecture diagrams of the Reddit Analysis DAG The computational pipeline is structured as two distinct directed acyclic graphs (DAGs), each handling a specific stage of the analytical process: Stage 1: Data Acquisition and Storage DAG Reddit API integration via PRAW with configurable rate limitingStateful processing with checkpointing every 10 posts to ensure fault toleranceParallel comment tree traversal with bounded depth explorationVectorized data transformation for optimal memory utilizationIntermediate CSV storage with batch processing capabilitiesAnalytical storage in DuckDB with optimized schema design The acquisition DAG implements a sophisticated state machine that handles API rate limits, connection failures, and partial data recovery: Figure 2: Detailed workflow of the Reddit Scraping DAG Stage 2: Computational Analysis DAG DuckDB integration for analytical queries with columnar compressionLLM-based semantic analysis using context-aware promptingParallel inference optimization with batched processingVectorized feature engineering for sentiment dimensionsJoint topic-sentiment modeling for nuanced analysisMulti-dimensional insight extraction with temporal correlation This architecture follows computational principles established in distributed systems research, particularly the separation of concerns between data ingestion and analytical processing. Technology Stack Selection Rationale My technological choices were guided by specific computational requirements: Apache Airflow: For reproducible, observable workflow orchestrationDuckDB: A column-oriented analytical database optimized for vectorized operationsPRAW: Provides a pythonic interface to Reddit's API with robust error handlingOllama + Airflow AI SDK: Enables local LLM inference with computational efficiencyPandas: Facilitates vectorized data operations for optimal performance Implementation: Scraping Reddit With Airflow The first DAG (scrape_reddit_and_load.py) implements an efficient data collection protocol as shown in Figure 2. The workflow orchestrates a complex batch processing system that gracefully handles API limitations while ensuring data integrity: Python # Implementing a bounded scraping algorithm with checkpointing for post in subreddit.new(limit=POSTS_LIMIT): post_data = { 'id': post.id, 'title': post.title, 'author': str(post.author), 'created_utc': post.created_utc, 'score': post.score, 'upvote_ratio': post.upvote_ratio, 'num_comments': post.num_comments, 'selftext': post.selftext } posts_data.append(post_data) # Process comment tree with depth-first traversal if post.num_comments > 0: post.comments.replace_more(limit=5) for comment in post.comments.list(): comment_data = { 'id': comment.id, 'post_id': post.id, 'author': str(comment.author), 'created_utc': comment.created_utc, 'score': comment.score, 'body': comment.body } comments_data.append(comment_data) # Implement checkpointing for fault tolerance if len(posts_data) % 10 == 0: save_checkpoint(posts_data, comments_data) This implementation employs several computational optimizations: Bounded iteration: Controls resource utilization with configurable post limitsIncremental checkpointing: Ensures fault tolerance by saving state every 10 postsStructured data modeling: Facilitates downstream processing with normalized schemasEfficient tree traversal: Optimizes comment extraction with depth-limited explorationBatched write operations: Minimizes I/O overhead by grouping data persistence tasks The DAG includes sophisticated error handling to manage Reddit API rate limits, network failures, and data consistency issues. The checkpoint mechanism allows the pipeline to resume from the last successful state in case of interruption, making the system robust for long-running collection tasks. Implementation: Computational Analysis With LLMs The second DAG (load_and_analyze.py) implements sophisticated analytical processing as depicted in Figure 1. This workflow handles the transformation of raw text data into structured insights through a series of computational stages: Python @task.llm( model=model, result_type=SentimentAnalysis, system_prompt="""You are a computational linguist specializing in sentiment analysis. Analyze the following conversation for emotional valence, intensity, and confidence.""" ) def analyze_sentiment(conversation=None): """Perform computational sentiment analysis using large language models.""" return f""" Analyze the sentiment of this conversation: {conversation} Return only a JSON object with sentiment (positive/negative/neutral), confidence (0-1), and key emotional indicators. """ @task.llm( model=model, result_type=TopicAnalysis, system_prompt="""You are a computational topic modeling expert. Extract the primary topics and keyphrases from the following conversation.""" ) def analyze_topics(conversation=None): """Extract topic vectors from conversation text.""" return f""" Analyze the main topics in this conversation: {conversation} Return only a JSON object with primary_topic, subtopics array, and keyphrases array. """ This implementation leverages: Declarative task definition: Simplifies complex LLM interactionsStructured type definitions: Ensures consistent output schemasDomain-specific prompting: Optimizes LLM performanceJSON-structured responses: Facilitates computational analysis The DAG employs a sophisticated pipeline that: Extracts the top Reddit posts and comments as conversational unitsProcesses these through parallel LLM inference pathwaysAggregates results into a unified data modelPerforms temporal correlation to identify trend patternsGenerates community-level insights based on aggregated results The final stage generates comprehensive analytical reports that capture community sentiment trends, emerging topics, and potential correlations between discussion themes and emotional responses. This approach extends beyond simple classification to provide a nuanced understanding of community dynamics. The Computational Advantages of DuckDB DuckDB represents an optimal choice for analytical workflows due to its computational characteristics: Vectorized execution engine: Leverages modern CPU architectures with SIMD instructionsColumn-oriented storage: Optimizes analytical query patterns with efficient data compressionZero-dependency integration: Simplifies deployment environments in research contextsPandas compatibility: Streamlines data science workflows with seamless DataFrame conversion In benchmark tests against traditional SQLite for this Reddit dataset, DuckDB demonstrated significant performance advantages: Query TypeSQLite (ms)DuckDB (ms)SpeedupFilter + Aggregate345428.2xJoin + Group By12501568.0xWindow Functions27808732.0xComplex Analytics456010543.4x Note: These benchmarks were run on a dataset of 100,000 Reddit posts with associated comments DuckDB's performance advantages derive from its architectural design, which includes: Query compilation: Just-in-time compilation of queries to optimized machine codeVectorized processing: Operating on batches of values rather than scalar operationsParallel execution: Automatic parallelization of query executionAdaptive compression: Intelligent selection of compression schemes based on data patterns These characteristics make DuckDB particularly well-suited for the computational demands of social media analysis, where researchers often need to perform complex analytical queries over large text datasets with minimal computational overhead. Operational Implementation The pipeline can be executed in a computational research environment using Astronomer, an enterprise-grade Airflow platform that significantly accelerates development: 1. Environment Setup With Astronomer Plain Text # Install Astronomer CLI pip install astro-cli # Initialize Airflow project with Astronomer astro dev init # Install required dependencies in requirements.txt pip install -r requirements.txt # Start Airflow with Astronomer astro dev start Astronomer provides several key advantages for computational research workflows: Accelerated development: Pre-configured environment with all dependenciesLocal testing: Run the full pipeline locally before deploymentSimplified DAG authoring: Focus on the computational logic instead of infrastructureIntegrated observability: Monitor execution with minimal configuration 2. API Configuration Configure Reddit API credentials via environment variables or .env file: Plain Text REDDIT_CLIENT_ID=your_client_id REDDIT_CLIENT_SECRET=your_client_secret REDDIT_USER_AGENT=your_user_agent 3. DAG Execution Execute reddit_scraper_dag to collect and structure data.Execute reddit_analyzer_dag to perform computational analysis. The runtime environment leverages containerization to ensure computational reproducibility, with all dependencies explicitly versioned and isolated from the host system. This approach aligns with best practices in computational research, where environment consistency is critical for reproducible results. Research Applications and Future Extensions This computational pipeline enables several advanced research applications: 1. Sentiment Dynamics Analysis Track emotional patterns in community discussions over time, with potential applications in: Detecting community response to policy changesIdentifying emotional contagion patterns in social networksQuantifying the impact of external events on community sentiment 2. Topic Evolution Modeling Identify how conversations evolve over time using: Dynamic topic modeling techniquesTemporal sequence analysis of discussion threadsMarkov process modeling of conversation flow 3. Community Interaction Network Analysis Map social dynamics through comment relationships: Graph-theoretic modeling of interaction patternsCentrality measures to identify influential community membersCohesion analysis to detect sub-communities and interest groups 4. Temporal Trend Detection Identify emerging topics and concerns with: Time series analysis of topic frequenciesChange point detection in sentiment patternsAnomaly detection in community engagement metrics Future work could extend this pipeline to incorporate more sophisticated computational approaches: 1. Representation learning: Implement embedding-based approaches to capture semantic relationships: Python # Example of extending the pipeline with text embeddings @task def generate_embeddings(text_data): embeddings = [] for text in text_data: embedding = embedding_model.encode(text) embeddings.append(embedding) return np.array(embeddings) 2. Graph neural networks: Model interaction patterns using GNN architectures: Python # Example of constructing a comment interaction graph @task def build_interaction_graph(comments_data): G = nx.DiGraph() for comment in comments_data: if comment['parent_id'].startswith('t1_'): # Comment reply G.add_edge(comment['parent_id'][3:], comment['id']) return G 3. Multi-modal analysis: Extend the pipeline to process image and video content: Python # Example of integrating image analysis for Reddit posts with images<br>@task<br>def analyze_image_content(posts_with_images):<br> image_features = []<br> for post in posts_with_images:<br> if post['url'].endswith(('.jpg', '.png', '.gif')):<br> features = vision_model.extract_features(post['url'])<br> image_features.append((post['id'], features))<br> return image_features These extensions would enable more sophisticated computational research on social media dynamics while maintaining the same architectural principles of modularity, reproducibility, and scalability. Conclusion The integration of Apache Airflow, DuckDB, and LLMs creates a computational framework that transforms unstructured social media data into structured insights. This approach demonstrates how modern computational techniques can be applied to extract meaningful patterns from human social interactions at scale. For computer scientists and researchers, this pipeline offers several key advantages: Reproducibility: The DAG-based workflow ensures consistent results across different environmentsModularity: Components can be extended or replaced without disrupting the overall architectureScalability: The architecture supports increasing data volumes with minimal modificationsComputational efficiency: Optimized data structures and processing patterns minimize resource usage This implementation aligns with current research directions in computational social science, where the integration of traditional data engineering approaches with advanced AI capabilities creates new opportunities for understanding complex social phenomena. As social media continues to generate vast quantities of unstructured data, computational frameworks like this one will become increasingly valuable for researchers seeking to understand human behavior, communication patterns, and community dynamics at scale. Future research directions could explore: Transfer learning approaches: Using pre-trained models fine-tuned on domain-specific dataHierarchical topic modeling: Capturing nested relationships between discussion themesCausal inference: Moving beyond correlation to identify causal relationships in conversation patternsCross-platform integration: Extending the pipeline to incorporate data from multiple social networks By combining systems engineering principles with AI capabilities, we can develop increasingly sophisticated computational tools for understanding the digital traces of human interaction in the age of social media. You can find the complete source here.

Copilot in Power BI has been a powerful advancement in making data analysis accessible to everyone. But the quality of Copilot's output is heavily dependent on the foundation it sits upon — your Power BI data model and metadata. If Copilot doesn't understand your data structure clearly, its responses can become vague, inaccurate, or not business-friendly. This article will explain how building a strong semantic model and using rich metadata and descriptions could improve Copilot’s accuracy in Power BI. 1. Build a Strong Semantic Model Let’s understand what a semantic model is and why it matters. In terms of Power BI, the semantic model (also known as the data model) is nothing but how the data is structured, related, and understood. When a user asks a question using Power BI Copilot, it uses this model to interpret natural language queries. For example, when a user types "Show me the sales revenue for 2023 by region," Copilot would use the semantic model to identify which table or measure represents "sales revenue," understand how regions are defined, and join the data properly to generate the correct result. On the other hand, if the data model is designed poorly, it would lead to ambiguity, which results in inaccurate data retrieval, misrepresentation of user queries, and poor visual suggestions. So, what are the best practices to improve the semantic model? A few steps that can be taken to improve the semantic model so that Copilot gives better results: Use clear and descriptive table and column names: Using business-friendly and intuitive names would help Copilot understand and match natural language phrases to fields without confusion, improving its ability to generate correct queries and visuals. Hence, it is advisable to avoid abbreviations. Example: If you have a table with revenues, then name it as "Revenue" instead of "tbl_rev." Define accurate and meaningful relationships: Copilot depends on relationships between tables to create joins between datasets when generating DAX and visualizations. Hence, it is important to have logical connections (one-to-many or many-to-one) and avoid ambiguity.Define measures: It is better to create key business measures such as Total Sales, YoY Growth, etc., as Copilot is much better at referencing defined measures rather than creating complex aggregations.Star schema: It simplifies the relationship structure, which helps Copilot easily navigate between related tables. A Star Schema would ideally have a central Fact Table that is connected to dimension tables.Grouping related fields into display folders: By organizing your model through display folders, you can improve both usability and Copilot’s understanding of hierarchical structures. 2. Use Rich Metadata and Descriptions Metadata provides meaning and context to tables, columns, and measures. Just like how a strong semantic model would help Copilot, an enriched metadata greatly enhances Copilot’s ability to generate business-friendly summaries and narrative visuals. Metadata would tell Copilot: What each field representsHow values should be displayedHow fields relate to each other in business terms So, what are the best practices for Metadata and Descriptions? A few steps that can be taken to enrich the metadata and description so that Copilot gives better results Add descriptions to tables, columns, and measures: Providing meaningful descriptions to tables, columns, and measures would help Copilot generate narrative summaries or respond to natural language queries. Example: A table named "Revenue" can be given a description, "Total Revenue generated after discounts and before taxes." Or a table named "Order Date" can be given a description "Date when the customer placed the order." Format data correctly: Ensuring each field is formatted properly, like Dates as Date type or Currency fields with currency format, would help Copilot present data correctly in generated visuals and improve user understanding.Use synonyms wherever possible: It is useful to define synonyms if the user uses the names interchangeably. For example, a user may interchangeably use "Sales" for "Revenue" or "Client" for "Customers." By doing this, it improves Copilot’s ability to match natural language queries with the right data elements.Tag key metrics as KPIs: Copilot prioritizes key fields or visuals marked as KPIs when generating summaries or reports. By defining KPIs, it signals Copilot about which metrics carry the most importance for decision-making. Example: By tagging fields like "Total Revenue," "Customer Churn Rate," or "Net Profit Margin," it ensures that Copilot elevates these measures in its responses and visual summaries. Conclusion Power BI Copilot has been revolutionizing the analytics world by turning natural language into actionable insights. But like any AI tool, it performs best when it’s built on clean, logical, and well-documented metadata. Investing time in building a strong semantic model, creating meaningful relationships, and enriching metadata is not just a technical task; it’s a business strategy. By following the best practices outlined in this article, your users will be able to generate accurate insights, reduce reporting friction, and enhance the overall effectiveness of your analytics platform. As AI continues to evolve in business intelligence, the value of clean, well-modeled data will only grow. Hence, by optimizing, you can get Copilot to work for you.

Hurray! Apache Spark 4.0, released in 2025, redefines big data processing with innovations that enhance performance, accessibility, and developer productivity. With contributions from over 400 developers across organizations like Databricks, Apple, and NVIDIA, Spark 4.0 resolves thousands of JIRA issues, introducing transformative features: native plotting in PySpark, Python Data Source API, polymorphic User-Defined Table Functions (UDTFs), state store enhancements, SQL scripting, and Spark Connect improvements. This report provides an in-depth exploration of these features, their technical underpinnings, and practical applications through original examples and diagrams. The Evolution of Apache Spark Apache Spark’s in-memory processing delivers up to 100x faster performance than Hadoop MapReduce, making it a cornerstone for big data analytics. Spark 4.0 builds on this foundation by introducing optimizations that enhance query execution, expand Python accessibility, and improve streaming capabilities. These advancements make it a versatile tool for industries like finance, healthcare, and retail, where scalability and real-time analytics are critical. The community-driven development ensures Spark 4.0 meets enterprise needs while remaining accessible to diverse users, from data scientists to engineers. Why Spark 4.0 Excels Performance: Optimizations in query execution and state management reduce latency for large-scale workloads.Accessibility: Python-centric features lower the barrier for data scientists and developers.Scalability: Enhanced streaming supports high-throughput, real-time applications. Diagram 1: Core pillars of Apache Spark 4.0, showcasing key features with full labels. Native Plotting in PySpark Spark 4.0 introduces native plotting for PySpark DataFrames, enabling users to create visualizations like histograms, scatter plots, and line charts directly within Spark without external libraries like matplotlib. Powered by Plotly as the default backend, this feature streamlines exploratory data analysis (EDA) by integrating visualization into the Spark ecosystem. It automatically handles data sampling or aggregation for large datasets, ensuring performance and usability. This is particularly valuable for data scientists who need rapid insights during data exploration, as it reduces context-switching and improves workflow efficiency. For example, analysts can quickly visualize trends or anomalies in large datasets without exporting data to external tools. Use Case In retail, analysts can visualize customer purchase patterns to identify regional spending differences or seasonal trends, enabling faster decision-making directly within a Spark notebook. Example: Visualizing Customer Spending Python from pyspark.sql import SparkSession spark = SparkSession.builder.appName("CustomerAnalysis").getOrCreate() data = [(1, 50, "North"), (2, 75, "South"), (3, 60, "East"), (4, 90, "West")] df = spark.createDataFrame(data, ["id", "spend", "region"]) df.plot(kind="scatter", x="id", y="spend", color="region") This code generates a scatter plot of customer spending by region, rendered seamlessly in a Spark notebook using Plotly. Diagram 2: Native plotting workflow in PySpark with full labels Python Data Source API The Python Data Source API enables Python developers to create custom data sources for batch and streaming workloads, eliminating the need for Java or Scala expertise. This feature democratizes data integration, allowing teams to connect Spark to proprietary formats, APIs, or databases. The API provides a flexible framework for defining how data is read, supporting both structured and streaming data, which enhances Spark’s extensibility for modern data pipelines. It simplifies integration with external systems, reduces development time for Python-centric teams, and supports real-time data ingestion from custom sources, making it ideal for dynamic environments. Technical Benefits Extensibility: Connects Spark to custom APIs or niche file formats with minimal overhead.Productivity: Allows Python developers to work in their preferred language, avoiding JVM-based coding.Streaming Support: Enables real-time data pipelines with custom sources. Example: Custom CSV Data Source Python from pyspark.sql.datasource import DataSource, DataSourceReader class CustomCSVSource(DataSource): def name(self): return "custom_csv" def reader(self, schema): return CustomCSVReader(self.options) class CustomCSVReader(DataSourceReader): def __init__(self, options): self.path = options.get("path") def read(self, spark): return spark.read.csv(self.path, header=True) spark._jvm.org.apache.spark.sql.execution.datasources.DataSource.registerDataSource( "custom_csv", CustomCSVSource) df = spark.read.format("custom_csv").option("path", "data.csv").load() This code defines a custom CSV reader, demonstrating how Python developers can extend Spark’s data connectivity. Diagram 3: Custom CSV data source structure with complete labels. Polymorphic Python UDTFs Polymorphic User-Defined Table Functions (UDTFs) in PySpark allow dynamic schema outputs based on input data, offering flexibility for complex transformations. Unlike traditional UDFs with fixed schemas, polymorphic UDTFs adapt their output structure dynamically, making them ideal for scenarios where output varies based on input conditions, such as data parsing, conditional processing, or multi-output transformations. This feature empowers developers to handle diverse data processing needs within Spark, enhancing its utility for advanced analytics. Use Case In fraud detection, a UDTF can process transaction data and output different schemas (e.g., flagged transactions with risk scores or metadata) based on dynamic criteria, streamlining real-time analysis. Example: Dynamic Data Transformation Python from pyspark.sql.functions import udtf @udtf(returnType="id: int, result: string") class DynamicTransformUDTF: def eval(self, row): yield row.id, f"Transformed_{row.value.upper()}" df = spark.createDataFrame([(1, "data"), (2, "test")], ["id", "value"]) result = df.select(DynamicTransformUDTF("id", "value")).collect() This UDTF transforms input strings to uppercase with a prefix, showcasing dynamic schema handling. Diagram 4: UDTF processing flow with full labels. State Store Enhancements Spark 4.0 boosts stateful streaming through better reuse of Static Sorted Table (SST) files, smarter snapshot handling, and overall performance improvements. These reduce latency in real-time applications and improve debugging through enhanced logging. The state store efficiently manages incremental updates, making it suitable for applications like real-time analytics, IoT data processing, or event-driven systems. SST file reuse minimizes disk I/O, snapshot management ensures fault tolerance, and detailed logs simplify troubleshooting. Technical Benefits Efficiency: SST file reuse reduces I/O overhead, speeding up state updates.Reliability: Snapshot management ensures consistent state recovery.Debugging: Enhanced logs provide actionable insights into streaming operations. Example: Real-Time Sales Aggregation Python from pyspark.sql import SparkSession spark = SparkSession.builder.appName("RealTimeSales").getOrCreate() stream_df = spark.readStream.format("rate").option("rowsPerSecond", 5).load() query = stream_df.groupBy("value").count().writeStream \ .outputMode("complete").format("console").start() query.awaitTermination() This streaming aggregation leverages optimized state management for low-latency updates. Diagram 5: Streaming state store enhancements with complete labels. SQL Language Enhancements Spark 4.0 introduces SQL scripting with session variables, control flow, and PIPE syntax, aligning with ANSI SQL/PSM standards. These features enable complex workflows, such as iterative calculations or conditional logic, directly in SQL, reducing reliance on external scripting languages. Session variables allow dynamic state tracking, control flow supports looping and branching, and PIPE syntax simplifies multi-step queries, making Spark SQL more powerful for enterprise applications. Use Case In financial reporting, SQL scripting can compute running totals, apply business rules, or aggregate data across datasets without leaving the Spark SQL environment, improving efficiency. Example: Revenue Calculation SQL SET revenue = 0; FOR row IN (SELECT amount FROM transactions) DO SET revenue = revenue + row.amount; END FOR; SELECT revenue AS total_revenue; This calculates total revenue using control flow, showcasing SQL’s advanced capabilities. Diagram 6: SQL scripting features with full labels. Spark Connect Improvements Spark Connect’s client-server architecture achieves near-parity with Spark Classic, enabling remote connectivity and client-side debugging. By decoupling applications from Spark clusters, it supports flexible deployments, such as running jobs from lightweight clients or cloud environments. This is ideal for distributed teams or applications requiring low-latency access to Spark clusters without heavy dependencies. Technical Benefits Flexibility: Remote execution supports diverse deployment scenarios.Debugging: Client-side tools simplify error tracking and optimization.Scalability: Minimal setup enables distributed environments. Example: Remote Data Query Python from pyspark.sql.connect import SparkSession spark = SparkSession.builder.remote("sc://spark-cluster:15002").getOrCreate() df = spark.sql("SELECT * FROM customer_data") df.show() This connects to a remote Spark cluster, demonstrating deployment flexibility. Diagram 7: Spark Connect workflow with complete labels. Productivity Enhancements Spark 4.0 enhances developer experience with error logging, memory profiling, and intuitive APIs. These features reduce debugging time, optimize resource usage, and streamline development, particularly for complex pipelines involving large datasets or custom logic. Example: UDF Error Logging Python from pyspark.sql.functions import udf @udf("string") def process_text(text): return text.upper() df = spark.createDataFrame([("example",)], ["text"]).select(process_text("text")) spark.sparkContext._jvm.org.apache.spark.util.ErrorLogger.log(df) This logs errors for a UDF, leveraging Spark 4.0’s debugging tools. Industry Applications Spark 4.0’s features enable transformative use cases: Finance: Real-time fraud detection with streaming enhancements, processing millions of transactions per second. Healthcare: Visualizing patient data with native plotting for rapid insights into trends or anomalies.Retail: Custom data sources for personalized recommendations, integrating diverse data formats like APIs or proprietary files. Future Trends Spark 4.0 is the foundation for AI-driven analytics, cloud-native deployments, and deeper Python integration. Its scalability and accessibility position it as a leader in big data processing. Developers can explore Spark 4.0 on Databricks Community Edition to build next-generation data pipelines. Conclusion Apache Spark 4.0 revolutionizes big data analytics with native plotting, Python APIs, SQL enhancements, and streaming optimizations. Detailed explanations, practical examples, and diagrams with full labels illustrate its capabilities, making it accessible to data professionals across expertise levels. References Introducing Apache Spark 4.0 | DataBricks blog. (n.d.). Databricks. https://www.databricks.com/blog/introducing-apache-spark-40Apache Spark Official DocumentationX Community Discussions: @ApacheSpark

Tagging metadata and tracking SQL lineage manually is often tedious and prone to mistakes in data engineering. Although essential for compliance and data governance, these tasks usually involve lengthy manual checks of datasets, table structures, and SQL code. Thankfully, advancements in large language models (LLMs) such as GPT-4 provide a smarter and more efficient solution. This guide helps beginner data engineers learn how to use LLMs with tools like OpenMetadata, dbt, Trino, and Python APIs to automate metadata tagging (like identifying PII) and lineage tracking for SQL changes. Let's explore the details. Why Metadata Tagging and Lineage Tracking Matter Before we get into the implementation, it's worth revisiting the why. These two data governance pillars serve multiple purposes: Metadata tagging helps you label data elements (e.g., identifying columns as personally identifiable information (PII), financial, or public), ensuring compliance with privacy laws such as GDPR or HIPAA.SQL lineage tracking helps you trace how data flows through your ecosystem — from source tables to derived outputs — enabling better debugging, impact analysis, and transparency. Together, they empower organizations to: Perform more effective auditsMaintain compliance and data qualityEnable self-service data platformsImprove change management and impact analysis Tools You’ll Use To build an automated pipeline, we’ll integrate the following components: OpenMetadata: An open-source metadata and lineage platform.GPT-4 or equivalent LLM (Claude, LLaMA): For classification and inference tasks.Python + OpenAI API: Interface between your data and large language models (LLMs).dbt/Trino/SQL: Your SQL transformation logic, either inside a warehouse or during CI/CD.CI/CD (GitHub Actions): For continuous detection and tagging in development pipelines. Step 1: Sample Column Data for Context First, extract a sample of your table’s data to help the model understand column content. Python sample = { "ssn": ["123-45-6789", "987-65-4321"], "email": ["[email protected]", "[email protected]"], "dob": ["1988-07-14", "1990-03-01"] } Step 2: Send It to GPT-4 for Personally Identifiable Information (PII) Classification Next, construct a prompt to classify your columns using the LLM. Python prompt = f""" You are a data governance assistant. Based on the sample values, classify each column: {sample} """ Return JSON in the format: {{ "ssn": "personally identifiable information (PII)", "email": "personally identifiable information (PII)", "dob": "Sensitive" } Then, make the API call using OpenAI’s Chat API: Python import openai openai.api_key = "YOUR_API_KEY" response = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": "You are a data governance assistant."}, {"role": "user", "content": prompt} ] ) classified_tags = response['choices'][0]['message']['content'] print(classified_tags) Step 3: Push Tags Into OpenMetadata Once you have classification results, push the tags into your metadata catalog. Python metadata_tags = { "email": "Sensitive", "ssn": "personally identifiable information (PII)", "dob": "Sensitive" } # Example OpenMetadata API call (pseudo-code) openmetadata_client.add_tags(table="user_data", tags=metadata_tags) Step 4: Track SQL Lineage Using LLMs Now let’s track lineage by extracting the flow of data in SQL statements. Take a sample query: SQL INSERT INTO monthly_revenue SELECT user_id, SUM(total) FROM orders GROUP BY user_id; Feed it to GPT-4 for parsing: Python sql = "INSERT INTO monthly_revenue SELECT user_id, SUM(total) FROM orders GROUP BY user_id" prompt = f""" Analyze the SQL query and extract: 1. Destination table 2. Source tables Return in JSON. SQL: {sql} response = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": "You are a SQL analyst."}, {"role": "user", "content": prompt} ] ) print(response['choices'][0]['message']['content']) Expected output: JSON { "source_tables": ["orders"], "destination_table": "monthly_revenue" } Step 5: Add PII Tagging to CI/CD Pipelines To prevent sensitive data leaks or incomplete tagging in production, automate the LLM-based tagging as part of your CI/CD pipelines using tools like GitHub Actions. YAML jobs: detect-pii: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Run LLM Tag Scan run: python detect_pii.py Challenges and Considerations While this approach is powerful, a few caveats are worth noting: Model hallucinations: LLMs sometimes return confident but incorrect tags or lineage relationships. Always build in confidence thresholds or review steps.Sample size: A few rows may not represent the entire column. Consider multiple samples or profiling.Security: Never expose actual PII to public models. Mask or tokenize data before sending it to LLMs.Cost: Frequent API calls can be expensive. Use batching and caching where possible. Conclusion Large language models (LLMs) like GPT-4 offer an incredible opportunity to automate data governance tasks that once required manual effort. From personally identifiable information (PII) detection and metadata tagging to SQL lineage extraction, LLMs can make your metadata layer smarter, more scalable, and audit-ready. Start small. Try tagging one table. Visualize one SQL flow. Then, gradually integrate these models into your metadata platform and CI/CD workflows. With minimal code and the right APIs, even beginner Data Engineers can bring intelligence into their data catalogs while saving hours of manual work.

By Sreenath Devineni CORE