Lakehouse or Data Warehouse? A Databricks Comparison
Data lakehouse vs warehouse: how Databricks and Delta Lake compare with a traditional warehouse, when each fits, and how to plan the migration.
A data warehouse holds structured, modelled data for SQL analytics and reporting. A lakehouse, like Databricks with Delta Lake, keeps every kind of data — structured and unstructured — in open formats on cloud storage, while adding warehouse-grade reliability, governance and SQL performance. Pick a warehouse if your work is mostly BI on structured data; pick a lakehouse if you also need large-scale data engineering, streaming, machine learning or AI on one platform.
Data warehouse basics
A data warehouse is a database tuned for analytical queries. Data gets cleaned and modelled into tables before it is loaded, and business users query it through SQL and BI tools. Modern cloud warehouses decouple storage from compute and scale comfortably for reporting workloads.
Warehouses shine for consistent, governed metrics. They are a less natural fit for raw files, images, documents, event streams or the iterative work of data scientists.
Data lakehouse basics
A lakehouse stores data in an open table format on inexpensive object storage and layers on the capabilities that made warehouses dependable: ACID transactions, schema enforcement, time travel, fine-grained governance and fast SQL. Databricks builds this on Delta Lake, using Unity Catalog to govern data and AI assets.
Since a single copy of data serves SQL analysts, data engineers and ML teams alike, there is no need to shuttle data between a lake and a warehouse.
Lakehouse vs warehouse: the main differences
The practical distinctions that typically decide the choice:
When a lakehouse is the better fit
Go with a lakehouse when data volumes are large or growing quickly, when you take in logs, events, documents or images, when real-time or streaming pipelines are important, or when machine learning and forecasting must run on the same governed data as reporting.
When the need is mostly dashboards over moderate volumes of structured data, a cloud warehouse can be the simpler route. Many organisations operate both, with the lakehouse supplying curated tables to the warehouse or BI layer.
How lakehouse data is usually structured
The widely used medallion architecture has three layers: bronze holds raw ingested data, silver holds cleaned and conformed data, and gold holds business-ready aggregates and features. Delta Live Tables or scheduled jobs promote data from layer to layer with quality checks, while Unity Catalog governs who can see what.
Planning a low-risk lakehouse migration
Migrations work when they are incremental and measured:
- Data types: warehouses centre on structured tables; lakehouses cope with structured, semi-structured and unstructured data.
- Workloads: warehouses are strongest at BI; lakehouses bring together BI, large-scale ETL, streaming, ML and generative AI.
- Formats: lakehouses rely on open formats such as Delta or Iceberg, which reduces lock-in.
- Cost model: lakehouse storage is low-cost object storage, with compute provisioned per workload.
- Skills: warehouses are SQL-first; lakehouses add Spark, Python and notebook workflows next to SQL.
- Catalogue sources, pipelines, reports and the people who own them.
- Move one domain end to end first, running old and new side by side to reconcile figures.
- Put governance and access control in place before onboarding large numbers of users.
- Tune clusters, job schedules and storage layout from the first day to keep cost in check.
- Retire legacy pipelines only once outputs match.
- Our AI services include Databricks and data engineering work, covering lakehouse builds and migrations.