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Commonly confused · Data & Analytics

Data Warehouse vs Data Lake vs Lakehouse: What's the Difference?

A data warehouse stores cleaned, structured data in a defined schema optimized for business intelligence queries. A data lake stores raw data of any type cheaply in its native format. A lakehouse adds warehouse features such as ACID transactions, schemas, and fast SQL on top of open files in lake storage.

Data Warehouse

Full definition
vsvs

Data Lakehouse

Full definition
Data WarehouseData LakeData Lakehouse
Data typesStructuredStructured, semi-structured, unstructuredAll types
SchemaSchema on writeSchema on readSchema enforced on open table formats
Data stateCleaned, transformed, curatedRawRaw through curated layers (bronze, silver, gold)
Primary usersBusiness analysts, BIData scientists, engineersBoth
Storage costHigherLow (object storage)Low (object storage)
ExamplesSnowflake, Amazon Redshift, BigQuery, Azure Synapse dedicated poolsAmazon S3, Azure Data Lake StorageDatabricks with Delta Lake, Microsoft Fabric, Apache Iceberg tables
Remember it

Warehouse is a tidy store, lake is a raw reservoir, lakehouse is a reservoir with plumbing.

Exam tip

Remember schema on write (warehouse) versus schema on read (lake). Without governance a lake can become a data swamp, which is a common distractor phrase.

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