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What is Data Lakehouse?
A modern data platform that combines the cheap, flexible storage of a data lake with the structured querying power of a data warehouse – so you get the best of both without maintaining two separate systems.
What it is
A data lakehouse is a hybrid that tries to solve the "lake or warehouse?" debate by merging the two. Traditionally, companies had to choose: a data lake for cheap, flexible storage of raw files, or a data warehouse for fast, structured reporting. A lakehouse puts a warehouse-like query engine on top of lake storage, so you can run SQL queries directly against your raw and processed data without moving it into a separate system. Think of it as a well-organised storage unit with a librarian at the front desk – you get the unlimited capacity of the unit with the findability of a library catalogue.
Why it matters for your business
For companies that need both historical data retention and fast reporting, maintaining separate lake and warehouse systems doubles your infrastructure costs and creates data duplication. Technologies like Databricks, Apache Iceberg, and Delta Lake make the lakehouse pattern practical for mid-market companies. A $90M logistics firm we assessed was paying $180,000 per year running a warehouse and a separate lake with overlapping data. Consolidating into a lakehouse pattern cut their storage costs by 55% and eliminated the overnight sync job that was failing twice a month.
How we approach it
We recommend a lakehouse when clients need to store large volumes of unstructured data (documents, images, logs) alongside their structured reporting data. For companies that only need structured analytics, a warehouse alone is simpler and usually the right starting point. When a lakehouse is appropriate, we use open table formats like Apache Iceberg or Delta Lake that avoid vendor lock-in and let you query data with standard SQL tools your team already knows.
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Key Takeaways
- •A lakehouse combines cheap, flexible lake storage with fast warehouse-style querying – one system instead of two.
- •Open table formats like Iceberg and Delta Lake prevent vendor lock-in and keep your options open.
- •Most mid-market companies should start with a warehouse and only move to a lakehouse if they have genuine unstructured data needs.
- •The savings come from eliminating duplication: one copy of data, one set of pipelines, one infrastructure bill.