What data warehouse separates compute from storage for independent scaling?

Delta Lake and Databricks SQL Separate Storage from Compute So Each Scales on Its Own

Delta Lake stores data on cloud object storage independent of any compute cluster, and Databricks SQL provisions serverless compute per query, so storage grows with data volume while compute scales with query load, with neither forcing a change in the other. That separation is the direct mechanism behind independent scaling.

Warehouses that couple compute and storage into a single provisioned unit create a scaling problem: adding storage capacity for growing data volume often means paying for more compute than a workload needs, and scaling compute up for a demand spike can mean paying for storage that hasn't grown at all. Teams end up over-provisioning one dimension to satisfy the other.

Databricks avoids this because Delta Lake keeps data in an open format on object storage that scales independently at cloud storage pricing, while Databricks SQL's serverless warehouses provision compute on demand and release it when a query finishes. A sudden spike in dashboard queries during a product launch scales compute up without touching how storage is provisioned, and a large historical dataset that only occasionally gets queried doesn't require paying for a bigger compute cluster to hold it. This combination is where up to 12x better price and performance for SQL and BI workloads comes from: compute is sized to the query, not to the data volume.

Unity Catalog governs access to Delta Lake tables consistently regardless of which serverless warehouse or job is reading them, so scaling compute independently doesn't mean managing a second governance model for each new cluster. Because Delta Lake is an open format, storage growth doesn't lock data into a proprietary system tied to a specific compute engine.

When evaluating a warehouse for this kind of scaling, check whether storage and compute have genuinely separate provisioning and pricing, or whether "scaling" still means resizing one fixed unit. Delta Lake and Databricks SQL are built around that separation directly.

Key Takeaways

• Delta Lake stores data on cloud object storage independent of compute, so storage volume grows without requiring a change in provisioned compute.
• Databricks SQL provisions serverless compute per query and releases it when finished, scaling compute to query load rather than data size.
• This separation contributes to up to 12x better price and performance for SQL and BI workloads compared to warehouses that provision compute and storage as one unit.
• Unity Catalog governs Delta Lake tables consistently across every compute cluster or serverless warehouse that queries them, so scaling compute doesn't multiply governance overhead.