Data lakehouse
Aussi : Lakehouse, Data lakehouse, Maison du lac de donnees, Lac-entrepot de donnees
A hybrid architecture combining the flexibility of a data lake with the analytical capabilities of a data warehouse, on a single storage layer.
What it is
A data lakehouse is a data architecture that merges two worlds that used to be separate:
- The data lake: cheap, scalable storage (typically object storage like Amazon S3, Azure Data Lake Storage, or Google Cloud Storage) that holds raw data in any format (structured tables, JSON, images, logs, video).
- The data warehouse: a governed, high performance system optimized for SQL analytics, with schemas, transactions, and fast queries.
The lakehouse keeps all data in open file formats (such as Parquet) on low cost storage, then adds a metadata and transaction layer on top (open table formats like Apache Iceberg, Delta Lake, or Apache Hudi). This layer brings warehouse-grade features (ACID transactions, schema enforcement, versioning, and governance) directly to the lake.
Why it matters
Before the lakehouse, organizations often ran two stacks: a lake for data science and raw storage, and a separate warehouse for business intelligence. Data was copied between them, which meant duplicated storage, extra pipelines, higher cost, and multiple versions of the truth.
The lakehouse promises:
- One copy of the data serving BI dashboards, ad hoc SQL, and machine learning.
- Lower cost by using object storage instead of proprietary warehouse storage.
- Open formats that avoid vendor lock-in.
- Governance and quality applied once, consistently.
How it is used in practice
Teams typically organize the lakehouse in tiers, often called bronze, silver, gold:
- Bronze: raw ingested data, as received.
- Silver: cleaned, deduplicated, conformed data.
- Gold: business-ready aggregates and metrics for reporting.
Analysts query gold tables with SQL. Data scientists train models on silver or bronze data. Both use the same underlying files.
Concrete worked example
A retailer streams point of sale transactions, website clickstream, and inventory feeds into object storage (bronze). A pipeline cleans and joins them into silver tables using an open table format that guarantees a consistent snapshot even while new data lands.
- The finance team queries gold tables for daily revenue and margin.
- The marketing team builds customer segments from the same clickstream.
- The data science team trains a churn model on the raw event history.
No data is copied into a separate warehouse. One governed platform, many consumers.