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Slide Mastering Data Integration: Design, Development, and Best Practices with Modern Trends

1.1 What is data integration

1.2 Challenges, benefits and Business use cases for data integration

1.3 Introduction to data integration Architectures and Business Intelligence (BI)

2.1 Defining data integration requirements

2.2 The Source system analysis and profiling

2.3 Data quality assessment and cleansing techniques

2.4 Data volume analysis and target system mapping

2.5 Introduction to data integration modeling concepts

3.1 Macro design best practices: Source system selection and prioritization

3.2 Macro design best practices: Data transformation strategies

3.3 Macro design best practices: Target system design considerations

3.4 Micro design best practices: Component-based design principles

3.5 Micro design best practices: Physical data integration modeling techniques

3.6 Micro design best practices: Coding standards and documentation practices

3.7 Micro design best practices: Data security and access control considerations

4.1 Data extraction techniques (full vs. incremental loads)

4.2 Change data capture (CDC) methods

4.3 Error handling and data integrity checks

4.4 Data transformation and cleansing in development environments

4.5 Unit testing and integration testing strategies for data integration processes

5.1 Building and deploying data integration pipelines

5.2 Continuous integration and continuous delivery (CI/CD) for data integration

5.3 Data integration monitoring and performance optimization techniques

5.4 Production support considerations and troubleshooting procedures