GoldenGate 23ai for Big Data & Streaming Platforms

Introduction Oracle GoldenGate 23ai for Big Data and Streaming Platforms is a real-time data integration solution. It enables smooth data movement between databases and modern platforms like Hadoop and Kafka. Moreover, it supports cloud-based streaming systems. With its microservices architecture, it ensures high performance and scalability. As a result, organizations can achieve low-latency data streaming. Therefore, it is ideal for real-time analytics and event-driven systems. Learner Prerequisites Basic understanding of databases such as Oracle or other RDBMS Familiarity with SQL and data integration concepts Knowledge of Hadoop or Kafka is helpful but not mandatory Basic understanding of Linux or Unix commands Awareness of cloud platforms like AWS, Azure, or OCI Interest in real-time data streaming and analytics Table of Contents 1. Introduction to GoldenGate 23ai for Big Data 1.1 Overview of GoldenGate Architecture 1.2 Evolution to Microservices Architecture 1.3 Key Features for Big Data Integration 1.4 Supported Big Data and Streaming Platforms 1.5 Use Cases for Real-Time Data Streaming 2. GoldenGate 23ai Microservices Architecture 2.1 Core Components and Services 2.2 Service Manager and Deployment Options 2.3 Administration Server and Distribution Server 2.4 Receiver Server and Performance Considerations 2.5 Microservices vs Classic Architecture 3. Big Data and Streaming Ecosystem Overview 3.1 Introduction to Big Data Concepts 3.2 Hadoop Ecosystem Overview 3.3 Apache Kafka Fundamentals 3.4 Cloud Streaming Platforms Overview 3.5 Data Lakes and Real-Time Analytics 4. Installing and Configuring GoldenGate for Big Data 4.1 Installation Prerequisites 4.2 Setting Up GoldenGate Microservices 4.3 Configuring Big Data Adapters 4.4 Environment Configuration for Streaming 4.5 Validation and Initial Setup Checks 5. Data Capture Techniques 5.1 Extract Process Overview 5.2 Log-Based Capture Mechanism 5.3 Initial Load vs Change Data Capture (CDC) 5.4 Filtering and Transformation Basics 5.5 Performance Optimization for Data Capture 6. Streaming Data to Kafka 6.1 Kafka Integration Architecture 6.2 Configuring Kafka Handlers 6.3 Topic Management and Partitioning 6.4 JSON and Avro Message Formats 6.5 Real-Time Streaming Use Cases 7. Integration with Hadoop Ecosystem 7.1 HDFS Integration 7.2 Hive and HBase Targets 7.3 File Formats such as JSON, Avro, and Parquet 7.4 Batch vs Real-Time Processing 7.5 Data Lake Integration Strategies 8. Cloud Streaming Integrations 8.1 GoldenGate with OCI Streaming 8.2 Integration with AWS Kinesis 8.3 Azure Event Hubs Connectivity 8.4 Hybrid and Multi-Cloud Architectures 8.5 Security Considerations in Cloud Streaming 9. Data Transformation and Mapping 9.1 Mapping Data Between Source and Target 9.2 Using Built-in Transformation Functions 9.3 Handling Schema Evolution 9.4 Data Enrichment Techniques 9.5 Error Handling and Data Validation 10. Monitoring and Troubleshooting 10.1 Monitoring Tools and Dashboards 10.2 Log Analysis and Error Detection 10.3 Performance Monitoring Metrics 10.4 Troubleshooting Data Lag Issues 10.5 Debugging Streaming Pipelines 11. Performance Tuning and Optimization 11.1 Throughput Optimization Techniques 11.2 Parallel Processing Configuration 11.3 Network and Resource Optimization 11.4 Scaling for High Volume Data 11.5 Latency Reduction Strategies 12. Security and Compliance 12.1 Data Encryption in Transit and At Rest 12.2 Authentication and Authorization 12.3 Secure Configuration Best Practices 12.4 Compliance with Data Regulations 12.5 Audit and Logging Mechanisms 13. Real-Time Analytics and Use Cases 13.1 Streaming Data for BI Tools 13.2 Event-Driven Architectures 13.3 Real-Time Fraud Detection 13.4 IoT Data Streaming 13.5 Operational Intelligence Use Cases 14. Advanced Features and Enhancements 14.1 Custom Handlers and Extensions 14.2 Integration with AI and ML Pipelines 14.3 Advanced Filtering Techniques 14.4 Multi-Target Data Distribution 14.5 Future Trends in Data Streaming Conclusion This training helps learners understand real-time data streaming using GoldenGate 23ai. It also builds practical skills for big data integration. Moreover, it covers cloud and streaming platforms in detail. As a result, learners can design scalable and secure data pipelines. Therefore, it supports modern analytics and business needs.