Databricks Unity Catalog migration
Seamlessly transition from Hive Metastore to Databricks Unity Catalog for unified data governance, enhanced security, and improved data accessibility across your organization.
Simplify data governance and enhance performance with Unity Catalog
Strengthen data governance, enhance security, and improve accessibility by moving beyond legacy systems. Enable efficient data organization, scalable integrations, and optimized performance for evolving business needs. Minimize complexity and ensure data integrity, empowering your teams to focus on insights and drive innovation.
What can we do for you?
Unified Visibility into Data and AI
Consolidate and map data from various platforms for accelerated data and AI initiatives, providing a single point of secure access for extracting insights.
Cutover from Legacy Catalog
Utilize SYNC commands to upgrade queries, jobs, and dashboards to Unity Catalog assets, ensuring comprehensive data layer coverage.
Unity Catalog Integration with Serverless SQL
Gain unified access to all data sources, eliminating manual reconciliation, and improving query performance for streamlined analytics workflows.
Seamless Migration to Unity Catalog
Ensure a smooth transition by creating Unity Catalog-enabled clusters, converting mount points to external locations, and migrating tables and views. Update code references to Unity Catalog tables using config and sync, initial load, and deep clone methods to maintain data integrity and optimize performance.
Streamline data management with effortless Unity Catalog migration
- Migrate to Unity Catalog to centralize data governance, ensuring better access control and compliance while improving security over legacy systems like Hive metastore.
- Easily scale connectors to accommodate growing data volumes and new source systems as your business evolves.
- Benefit from more efficient data management with Unity Catalog’s ability to organize, track, and manage data assets across various environments, improving accessibility.
- Spend more time on analysis and decision-making by reducing the complexity of data integration workflows and focusing on actionable insights.
- Minimize the risk of errors in analysis by utilizing custom connectors and robust data management frameworks to ensure accurate and reliable data.