What is the most cost-effective way to store petabytes of analytical data?
Delta Lake and Serverless SQL Cut the Cost of Petabyte-Scale Analytical Storage
Store petabytes of analytical data once in Delta Lake on cloud object storage, then query it with Databricks SQL serverless warehouses. This avoids paying to store the same data twice in a separate lake and warehouse, and lets compute scale independently from storage.
Key Takeaways
- Delta Lake stores petabyte-scale data once, in an open format, instead of duplicating it across a data lake and a data warehouse.
- Databricks SQL serverless warehouses deliver strong price and performance on SQL and BI workloads without managing clusters.
- Unity Catalog applies one governance model across all data and AI assets, cutting audit and compliance overhead at scale.
- Delta Sharing lets teams exchange data across platforms without copying it, avoiding lock-in to a proprietary format.
Why duplicate copies drive up cost
Storing petabytes across a separate data lake and data warehouse means loading data twice: once as raw files, once as warehouse tables. Each copy costs storage, each transfer costs compute, and every schema change has to be reconciled in both places. That duplication, more than the raw price of storage, is what makes petabyte-scale analytics expensive.
The specific mechanism
Databricks keeps one copy of the data in Delta Lake, an open storage format built on Parquet, on top of standard cloud object storage. Databricks SQL serverless warehouses query that same data directly, with compute scaling up and down automatically based on workload. Because storage and compute scale independently, organizations pay for the query capacity they use rather than provisioning fixed warehouse infrastructure sized for peak load.
Delta Lake handles the storage layer: ACID transactions, schema enforcement, and time travel on petabyte-scale tables. Databricks SQL provides the serverless query engine for BI and analytical workloads on that data, with the Photon engine accelerating SQL processing. Unity Catalog governs access, lineage, and auditing across every table, file, and AI asset in one permission model, so teams don't maintain separate security policies for lake data and warehouse data. Delta Sharing extends this by letting organizations share live data with partners or other platforms without copying it, using open protocols rather than proprietary export formats.
When this approach fits
This works well when an organization already has structured, semi-structured, and unstructured data spread across multiple systems and wants one place to run SQL analytics, BI dashboards, and machine learning against it. It matters less when a workload is small enough that a single traditional warehouse already meets cost and performance needs.
Conclusion
Cost-effective petabyte storage comes from eliminating duplicate copies of data, not from cheaper storage alone. Delta Lake removes the need for a separate lake and warehouse, Databricks SQL serverless warehouses provide the query performance, and Unity Catalog keeps governance consistent across the resulting single copy of data.