Pattern architecture scope
ETL, ELT, and CDC patterns serve different latency, transformation, and operational control requirements. Enterprise architecture should map these patterns to domain use cases rather than standardizing one method for all workloads.
Pipeline design and governance controls
Teams should define source extraction policy, schema evolution handling, replay strategy, and data quality gates per pipeline family. CDC pipelines require robust ordering and reconciliation controls, while ELT patterns need warehouse-level transformation governance and cost monitoring.
Related pathways: Data Integration Hub, Integration and API Hub, and modern data pattern library.
Operating model and performance outcomes
Well-governed pattern selection improves freshness SLAs, reduces reprocessing risk, and strengthens confidence in analytics consumption. Organizations should align pattern ownership to platform teams and domain product owners to keep change cadence manageable.
Cross-stack alignment: Event Streaming Kafka Event Hub Architecture and Lakehouse Implementation Patterns.
Hub pathways
Return to Data Integration taxonomy, continue to Integration and API taxonomy, or review BI and Analytics pathways.