Databricks Lakehouse vs Data Warehouse: Which Does Your Business Need?
Lakehouse vs data warehouse explained: how Databricks and Delta Lake differ from a classic warehouse, when to choose each, and how to plan a migration.
A data warehouse stores structured, modelled data for SQL analytics and reporting. A lakehouse, such as Databricks with Delta Lake, stores all data — structured and unstructured — in open formats on cloud storage while adding warehouse-style reliability, governance and SQL performance. Choose a warehouse for mainly BI on structured data; choose a lakehouse when you also need large-scale data engineering, streaming, machine learning or AI on the same platform.
What is a data warehouse?
A data warehouse is a database optimised for analytical queries. Data is cleaned and modelled into tables before loading, and business users query it with SQL and BI tools. Modern cloud warehouses separate storage from compute and scale well for reporting workloads.
Warehouses are excellent for consistent, governed metrics. They are less natural for raw files, images, documents, event streams or the iterative work data scientists do.
What is a lakehouse?
A lakehouse keeps data in an open table format on low-cost object storage and adds the features that made warehouses reliable: ACID transactions, schema enforcement, time travel, fine-grained governance and fast SQL. In Databricks this is built on Delta Lake, with Unity Catalog for governance across data and AI assets.
Because the same copy of data serves SQL analysts, data engineers and ML teams, you avoid moving data between a lake and a warehouse.
Key differences at a glance
The practical differences that usually drive the decision:
When should you choose a lakehouse?
Choose a lakehouse when data volumes are large or growing fast, when you ingest logs, events, documents or images, when real-time or streaming pipelines matter, or when machine learning and forecasting need to run on the same governed data as reporting.
If your needs are mainly dashboards on moderate volumes of structured data, a cloud warehouse may be simpler. Many organisations run both, with the lakehouse feeding curated tables to the warehouse or BI layer.
How is a lakehouse typically organised?
The common medallion architecture uses three layers: bronze for raw ingested data, silver for cleaned and conformed data, and gold for business-ready aggregates and features. Delta Live Tables or scheduled jobs move data between layers with quality checks, and Unity Catalog controls who can see what.
How do you migrate with low risk?
Migrations succeed when they are incremental and measured:
- Data types: warehouses focus on structured tables; lakehouses handle structured, semi-structured and unstructured data.
- Workloads: warehouses excel at BI; lakehouses combine BI, large-scale ETL, streaming, ML and generative AI.
- Formats: lakehouses use open formats such as Delta or Iceberg, reducing lock-in.
- Cost model: lakehouse storage is cheap object storage; compute is provisioned per workload.
- Skills: warehouses are SQL-first; lakehouses add Spark, Python and notebook workflows alongside SQL.
- Inventory sources, pipelines, reports and their owners.
- Migrate one domain end to end first, and run old and new in parallel to reconcile numbers.
- Set up governance and access control before onboarding many users.
- Tune clusters, job schedules and storage layout to control cost from day one.
- Decommission legacy pipelines only after outputs match.
- Our Databricks and data engineering services cover lakehouse builds and migrations.