Page Comparison

Chapter 3: Communication and Data Contracts

• Event-Driven Data Contracts

  • Intro

    • data contract = data definition + triggering logic

  • Using Explicit Schemas as Contracts

  • Schema Definition Comments

  • Full-Featured Schema Evolution

  • Code Generator Support

  • Breaking Schema Changes

• Selecting an Event Format

• Designing Events

  • Tell the Truth, the Whole Truth, and Nothing but the Truth

  • Use a Singular Event Definition per Stream

  • Use the Narrowest Data Types

  • Keep Events Single-Purpose

  • Minimize the Size of Events

  • Involve Prospective Consumers in the Event Design

  • Avoid Events as Semaphores or Signals

• Summary

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