Explainer Defines Data Pipeline Architecture and Stages

Databricks published a definitional explainer on data pipeline architecture -- the structured design that governs how data moves from source systems to its final destination. The article covers the stages, architectural patterns, and operational best practices used in modern data engineering.

Modern pipelines typically pass through layered stages: ingestion (extracting data from source systems), transformation (cleaning, enriching, aggregating), and serving (delivering processed data to consumers). The two dominant patterns are ETL (Extract, Transform, Load -- transform before loading) and ELT (Extract, Load, Transform -- load raw data first, transform inside the destination). ELT has become the default in cloud-native environments given the compute capacity of modern lakehouses and data warehouses. Streaming pipelines, which process data continuously rather than in discrete batches, are covered as a distinct pattern suited to real-time use cases.

The piece is vendor-published educational content from Databricks, a major data and AI platform. It positions Databricks tooling -- including Delta Lake and declarative pipeline capabilities -- as a recommended implementation layer. Practitioners should treat it as a useful conceptual reference alongside independent sources. It does not report a product launch, research finding, or industry development.