Building Event-Driven Microservices Ch 4-5, 2021-10-20

Chapter 4: Integrating Event-Driven Architectures with Existing Systems

• What Is Data Liberation?

  • Compromises for Data Liberation

  • Converting Liberated Data to Events

• Data Liberation Patterns

• Data Liberation Frameworks

• Liberating Data by Query

  • Bulk Loading

  • Incremental Timestamp Loading

  • Autoincrementing ID Loading

  • Custom Querying

  • Incremental Updating

  • Benefits of Query-Based Updating

  • Drawbacks of Query-Based Updating

• Liberating Data Using Change-Data Capture Logs

  • Benefits of Using Data Store Logs

  • Drawbacks of Using Data Base Logs

• Liberating Data Using Outbox Tables

  • Performance Considerations

  • Isolating Internal Data Models

  • Ensuring Schema Compatibility

  • Capturing Change-Data Using Triggers

• Making Data Definition Changes to Data Sets Under Capture

  • Handling After-the-Fact Data Definition Changes for the Query and CDC Log Patterns

  • Handling Data Definition Changes for Change-Data Table Capture Patterns

• Sinking Event Data to Data Stores

• The Impacts of Sinking and Sourcing on a Business

• Summary

Chapter 5: Event-Driven Processing Basics

• Composing Stateless Topologies

  • Transformations

  • Branching and Merging Streams

• Repartitioning Event Streams

  • Example: Repartitioning an Event Stream

• Copartitioning Event Streams

  • Example: Copartitioning an Event Stream

• Assigning Partitions to a Consumer Instance

  • Assigning Partitions with the Partition Assignor

  • Assigning Copartitioned Partitions

  • Partition Assignment Strategies

    • Round-robin assignment

    • Static assignment

    • Custom assignment

• Recovering from Stateless Processing Instance Failures

• Summary