I am hearing a lot about the 'Lakehouse' concept recently. From a perspective, why would a company choose a Lakehouse over a proven Data Warehouse like Snowflake? I'm interested in understanding the trade-offs regarding cost, performance for BI tools, and the complexity of managing unstructured data alongside structured tables in a unified platform.
3 answers
The primary difference lies in the storage-compute decoupling and the support for diverse data types. A Lakehouse, like Databricks, uses open formats like Delta or Iceberg on top of cheap cloud storage (S3/ADLS), making it more cost-effective for massive datasets. Traditional warehouses are often optimized for SQL and BI performance but can be expensive and struggle with machine learning workloads. The Lakehouse attempts to bridge this gap by adding a metadata layer that provides ACID transactions and schema enforcement on top of the data lake, allowing both BI and AI to run on a single copy of data.
Doesn't the performance of BI tools suffer when querying a Lakehouse compared to the highly optimized proprietary engines of a standard Data Warehouse?
The biggest win for Lakehouse is avoiding data silos. You don't have to move data back and forth between a lake for ML and a warehouse for reporting.
Exactly! Reducing "data gravity" issues and eliminating redundant ETL jobs saves a massive amount of engineering hours and significantly reduces the chance of data discrepancies.
It used to, but with technologies like Photon in Databricks or Starburst Presto, the gap is closing fast. These engines are designed to provide SQL performance that rivals traditional warehouses by using vectorized execution. While a dedicated warehouse might still win on extremely low-latency dashboarding, the cost savings and flexibility for data science often make the Lakehouse a more attractive long-term investment for modern enterprises.