Are you at the start of migrating existing workloads to Databricks Unity Catalog (UC)? In the middle of a migration? Not even on your radar?

As we increasingly recognize the benefits of Unity Catalog, and as Databricks rapidly innovates on features that are either a part of or dependent on UC, organizations are finding themselves with a lot to strategize and execute on. Many organizations have massive Databricks-driven platforms, both leveraging Hive and not, often spanning varied data and AI-related problems across differing implementation patterns and use cases. To complicate matters further, these solutions are maintained by many different teams, leveraged by many different business users, and owned by many different stakeholders with different priorities, ways of working, and skills.

We are working with organizations at various stages in their adoption of Unity Catalog. Some organizations are planning Unity Catalog migrations, and others are in the midst of a rollout needing to simultaneously enable new builds while limiting operational impacts of “legacy” solutions being migrated. Many groups are at a crossroads – but even that underplays the number of intersecting decisions that must be navigated in one’s Unity Catalog migration path.

Join us on October 9th for our webinar where we aim to share our experience in planning, designing, and executing Unity Catalog migrations. We’ll provide a perspective on key challenges organizations are facing and considerations on how best to juggle many variables in enabling (and maximizing) UC.

What to Expect:

• Summary of the value proposition of Unity Catalog and why we even need to migrate (and how to get the most out of migrated workloads)
• Considerations, approaches, and strategies for a successful migration to Unity Catalog
• A perspective for organizations at different stages of their journey

Is this for you?

• Do you want to learn more about Unity Catalog and what it can bring to your organization?
• Are you planning a migration to Unity Catalog from your existing Databricks’ workloads? And need to define best practices for new workloads?