Data Lakehouse Architecture on Azure: Module 5: Integration Patterns

Module 5: Integration Patterns Learning Objectives By the end of this module, you will be able to: Select the right integration pattern (batch, streaming, event-driven) based on latency, throughput, and cost requirements Design Change Data Capture (CDC) pipelines using Debezium, Azure CDC, and Delta Lake CDF Architect API-first data products that expose curated datasets to consumers Implement Delta Sharing for cross-organization data exchange without data duplication Design reverse ETL pipelines to push lakehouse data back into operational systems Plan a data marketplace that enables self-service data discovery and consumption 1. Integration Pattern Selection 1.1 The Integration Spectrum Data integration is not a binary batch-or-streaming decision. Modern architectures place workloads along a latency continuum. INTEGRATION LATENCY SPECTRUM ◄─────────────────────────────────────────────────────────────────► BATCH MICRO-BATCH STREAMING EVENT-DRIVEN (hours-days) (seconds-minutes) (millise