The data lakehouse combines the flexibility of data lakes with the reliability and performance of data warehouses, creating a unified platform for all your data needs.
The Problem with Separate Systems
Traditional architectures force you to choose between:
- Data Lakes: Cheap storage, any format, but poor query performance and no ACID transactions
- Data Warehouses: Fast queries, strong consistency, but expensive and rigid schemas
What is a Data Lakehouse?
A lakehouse unifies both paradigms on a single platform:
- Store raw data in open formats (Parquet, Delta, Iceberg)
- Run SQL analytics directly on the lake
- Support ACID transactions and schema enforcement
- Enable ML/AI workloads on the same data
Key Technologies
Apache Iceberg
Open table format that brings warehouse-like features to data lakes. Supports schema evolution, time travel, and partition evolution.
Delta Lake
Created by Databricks, Delta Lake adds reliability to data lakes with ACID transactions, scalable metadata handling, and unified batch/streaming processing.
Apache Hudi
Hadoop Upserts Deletes and Incrementals, optimized for incremental data processing and near-real-time analytics.
Architecture Patterns
Bronze-Silver-Gold (Medallion Architecture)
- Bronze: Raw ingested data, append-only
- Silver: Cleaned, validated, deduplicated data
- Gold: Business-level aggregates, ready for analytics
Benefits for AI/ML
- Train models directly on lakehouse data without ETL to a separate ML platform
- Feature stores built on lakehouse tables
- Model versioning alongside data versioning
- Unified governance across analytics and ML
Conclusion
The data lakehouse isn't just a trend; it's the natural evolution of data architecture. If you're building a new data platform or modernizing an existing one, the lakehouse pattern should be your starting point.
