Lakehouse Platform
A self-service data lakehouse built on Databricks and Delta Lake, unifying batch and streaming workloads with a single storage layer.
Overview
An internal data platform that provides a unified lakehouse architecture using Delta Lake on cloud object storage. Teams can run both batch ETL and low-latency queries on the same data, eliminating the traditional Lambda architecture complexity.
Architecture
Uses the medallion pattern — raw data lands in the Bronze layer, gets cleaned and deduplicated in Silver, and aggregated business-level data lives in Gold.
[Sources] → Bronze (raw) → Silver (clean) → Gold (aggregated)
↑ Spark Structured Streaming / BatchFeatures
- ACID transactions – Delta Lake ensures no partial writes or read inconsistencies
- Time travel – Query historical snapshots of any table for debugging and auditing
- Schema enforcement – Automatic schema validation on write with evolution support
- Unified batch + streaming – Same Spark jobs run in both batch and streaming modes
- Orchestration – Airflow DAGs manage job scheduling, retries, and SLA alerting
Impact
- Eliminated 3 separate data silos across teams
- Reduced data pipeline development time by 40% via reusable Spark libraries
- Enabled ML teams to access clean, versioned feature data without custom ETL work