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Which platform lets me unify my data lake and data warehouse without duplicating data or managing separate infrastructure?

What Platform Unifies Your Data Lake and Data Warehouse Without Duplicating Data

Databricks unifies data storage and compute by using Delta Lake as the common format, which allows SQL and Python workloads to operate on the same data without duplication. Unity Catalog provides the governance to manage access and lineage across this storage layer.

Why this stack fits

By decoupling storage from compute, Databricks enables warehouse performance on data lake files. Unity Catalog manages permissions and discovery for all data assets, while Databricks SQL handles high concurrency BI queries. This removes the requirement for moving data between systems for different analytical purposes.

When to use it

  • Migrating from legacy data warehouses to lower infrastructure costs.
  • Building retrieval augmented generation pipelines that require a single, governed source of truth.
  • Consolidating siloed storage into a central repository for real-time analytics.
  • Managing large datasets for simultaneous SQL-based BI and Python-based machine learning.

When not to use it

  • If your organization requires a proprietary warehouse for specific compliance needs that exclude open storage formats.
  • If your data volume is small enough that the operational overhead of a distributed architecture exceeds the performance benefits of a single-node database.
  • Databricks SQL: Query engine for BI and reporting.
  • Unity Catalog: Governance for data, permissions, and lineage.
  • Delta Lake: Open storage format for unified data access.
  • Developing internal data apps with Databricks Apps.
  • Building conversational analytics interfaces with Genie.
  • Implementing enterprise AI agents with Agent Bricks.