Migrate to Databricks

Modernize your data platform by moving to the Databricks Lakehouse

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.

“Our data ingestion rates have increased significantly, improving processing times by 75% and resulting in 2x faster delivery of insights to the business, all while reducing compute costs by ~25% over static Hadoop clusters.”

— Kerby Johnson, Distinguished Software Engineer, Amgen

“Migrating from an on-premises infrastructure to Databricks on AWS was key to unlocking the possibilities of our data and enabling our data team to thrive.”

— R. Tyler Croy, Director of Platform Engineering, Scribd

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.

Scale cost-effectively

Stop managing servers, and scale on demand with serverless. Run data warehousing at scale with up to 12x better price/performance.

Accelerate innovation

Build AI, ML and real-time analytics capabilities faster with collaborative, self-service tools and open source technologies such as MLflow and Apache Spark™.

Migrate from Hadoop

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Migrate from a data warehouse

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Migrate with confidence

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

Phase 1:
Discovery

Use profilers to automate discovery. Get insights on legacy platform workloads and estimate Databricks platform consumption costs.

Phase 2:
Assessment

Use analyzers for a detailed assessment of code complexity and to estimate migration project costs.

Phase 3:
Strategy

Finalize technology mapping and build optimal pathways for each source platform migration, with expert guidance from Databricks.

Phase 4:
Production Pilot

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.