Beyond the Lake and the Warehouse
For years, data teams chose between a cheap-but-messy data lake and an expensive-but-reliable data warehouse. The lakehouse pattern merges both, storing raw data cheaply in object storage while layering a structured, ACID-compliant query engine on top.
Core Components
- Object Storage: AWS S3, Azure Data Lake Storage, or Google Cloud Storage for cheap, scalable raw storage.
- Open Table Format: Delta Lake, Apache Iceberg, or Apache Hudi for ACID transactions, time travel, and schema evolution on files.
- Query Engine: Apache Spark, Trino, or Databricks SQL for high-performance analytical queries.
- Metadata Catalogue: AWS Glue or Apache Atlas for discovering and governing datasets.