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.