Reduce costs, innovate faster and simplify your data platform by migrating to the Databricks Lakehouse from your enterprise data warehouse or legacy data lake. Now you can run all your data, analytics and AI workloads on a modern unified platform, built on open standards and secured with a common governance approach.
Why migrate to Databricks?
Simplify your data platform
Get a single modern platform for all your data, analytics and AI use cases. Unify governance and the user experience across clouds and data teams.
Stop managing servers, and scale on demand with serverless. Run data warehousing at scale with up to 12x better price/performance.
Build AI, ML and real-time analytics capabilities faster with collaborative, self-service tools and open source technologies such as MLflow and Apache Spark™.
We’ve helped hundreds of customers migrate from legacy data platforms. Using a phased end-to-end migration process provides a predictable model to understand costs both during and post-migration. And a lakehouse-first approach for migrating all workloads ensures support for existing as well as new use cases. The result is lower risk, faster value realization and increased ROI.
The 5 phases of data migration
Use profilers to automate discovery. Get insights on legacy platform workloads and estimate Databricks platform consumption costs.
Use analyzers for a detailed assessment of code complexity and to estimate migration project costs.
Finalize technology mapping and build optimal pathways for each source platform migration, with expert guidance from Databricks.
Run a pilot with your use cases and use code converters where applicable to transpile legacy code to Databricks-compatible code. Develop a migration implementation plan and road map.
Rinse and repeat for all workloads. Get help with migration execution and support from certified partners or Databricks Professional Services.
Customers who have successfully migrated to Databricks