This article explains data pipeline architecture, the framework that defines how data is collected, processed, stored, and delivered to users, applications, and AI systems.Rather than focusing on a single technology, pipeline architecture describes the overall blueprint governing data movement and transformation.

The article distinguishes between logical architecture, which defines pipeline stages and functions, and physical architecture, which specifies the technologies used to implement them.Modern pipelines typically consist of four core layers: ingestion, transformation, storage, and serving.

Data can be ingested in batches or as real-time streams, transformed through cleaning and enrichment processes, stored in data lakes, warehouses, or lakehouses, and finally delivered to analysts, business users, machine learning models, or operational applications.

The article reviews common architectural patterns including batch, streaming, Lambda, Kappa, and Medallion architectures, highlighting the strengths and trade-offs of each.

It also explains the evolution from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform), noting that modern cloud platforms make it practical to store raw data first and transform it later.

Best practices include separating ingestion from transformation, designing idempotent processes, implementing data quality checks, handling schema changes, using open storage formats, maintaining governance controls, and monitoring pipelines end-to-end.The article emphasizes that selecting an architecture should depend on business requirements such as latency, cost, scalability, and reliability.

Databricks presents its own platform approach, which combines ingestion, orchestration, storage, governance, and both batch and streaming processing into a unified lakehouse environment.