Data Lakehouse vs Data Lake vs Data Warehouse

These three architectures solve different versions of the same problem. This page cuts through the noise: here is what each actually does, where it falls short, and which workloads it is genuinely the right fit for.

Quick Definitions

Data warehouse: A centralized database optimized for structured analytical queries. Data is loaded through ETL, stored in proprietary columnar format, and queried through a proprietary SQL engine.

Data lake: A storage repository for raw data in open file formats on cheap object storage. No enforced schema, no transactions, no consistent query interface.

Data lakehouse: Object storage with open files plus a table format layer (Apache Iceberg) that adds ACID transactions, schema enforcement, and query optimization on top.

How They Are Built Differently

graph TD subgraph WAREHOUSE["Data Warehouse"] W1["ETL into proprietary storage"] W2["Proprietary SQL engine only"] W3["Examples: Redshift, Snowflake (non-Iceberg), Synapse"] end subgraph LAKE["Data Lake"] L1["Raw files in object storage (S3, GCS, ADLS)"] L2["No table layer — schema defined at query time"] L3["Examples: S3 + raw Parquet, HDFS + raw ORC"] end subgraph LAKEHOUSE["Data Lakehouse"] LH1["Open files in object storage"] LH2["Table format: Apache Iceberg (metadata + manifests)"] LH3["Catalog: Apache Polaris / Glue / Nessie"] LH4["Any engine: Spark, Trino, Dremio, Athena, BigQuery"] LH1 --> LH2 --> LH3 --> LH4 end

Full Comparison

Dimension	Data Lake	Data Warehouse	Data Lakehouse
Storage format	Open (raw files)	Proprietary columnar	Open (Parquet + table format metadata)
Storage cost	Very low	High	Very low (same object storage)
ACID transactions	No	Yes	Yes (Iceberg)
Schema enforcement	Read-time only	Write-time strict	Write-time + safe evolution
SQL query performance	Slow (full scans)	Very fast	Fast (metadata pruning + compaction)
Time travel	No	Limited	Yes (Iceberg snapshot history)
Multi-engine access	Yes (raw files, limited)	No (proprietary API)	Yes (REST Catalog standard)
ML / data science	Good (raw format access)	Difficult (format conversion)	Good (PyIceberg, Spark, DuckDB)
AI agent access	No (no governed interface)	Possible (JDBC/SQL)	Yes (governed + semantic layer)
Streaming writes	Yes (raw files)	Limited / expensive	Yes (Flink + Iceberg, exactly-once)
Governance / RBAC	S3 bucket-level only	Yes (table/column level)	Yes (catalog-level RBAC + masking)
Vendor lock-in	Low	High	Low (open formats + open catalog)

When to Use Each

flowchart TD A["What does your workload look like?"] A -->|"Purely SQL analytics, stable schema, moderate volume, one vendor is fine"| B["Data Warehouse"] A -->|"Raw ML / data science files only, no SQL or consistency requirements"| C["Data Lake"] A -->|"SQL + ML + streaming + AI agents OR multi-engine OR large scale OR vendor independence"| D["Data Lakehouse (Apache Iceberg + open catalog)"] D --> E["Choose catalog: Apache Polaris, AWS Glue, Project Nessie"] D --> F["Choose engine: Dremio, Trino, Spark, Athena"]

Migration Triggers

Teams typically move from a warehouse to a lakehouse when storage costs become unsustainable, ML teams need direct access to the same data BI uses, or the team wants to add a second query engine without copying data.

Teams move from a raw data lake to a lakehouse when data reliability problems start causing production incidents, SQL analytics on the lake become a requirement, or governance requirements (RBAC, audit, masking) can no longer be satisfied with S3 bucket policies.