About This Architecture

Databricks Lakehouse architecture ingests streaming and batch data from Kafka, APIs, databases, and files through Auto Loader, Spark Streaming, and Partner Connect connectors. Data flows through Bronze (raw), Silver (curated), and Gold (aggregated) Delta Lake layers, transformed by Spark clusters, Delta Live Tables, and Photon engines with integrated data quality checks. The platform serves analytics dashboards, predictive models, and ML pipelines while Unity Catalog provides governance, lineage, and security across the entire pipeline. Fork this diagram to customize ingestion sources, transformation logic, or serving endpoints for your specific lakehouse use case.

People also ask

How do you build an end-to-end data platform on Databricks with ingestion, transformation, and serving layers?

This diagram shows a Databricks lakehouse with streaming (Kafka, APIs) and batch (SQL, files) ingestion flowing through Auto Loader and Spark Streaming into Bronze raw layers, then curated through Silver and Gold Delta tables using Spark clusters and Delta Live Tables. Unity Catalog provides governance and lineage while Workflows orchestrate jobs and serve data to dashboards, ML models, and analyt