BRIEF OVERVIEW: Databricks Metastore
The Databricks Metastore is a component of the Databricks Unified Data Analytics Platform that provides a centralized and scalable metadata management system. It acts as a catalog for storing and managing metadata information about various data assets, such as tables, databases, views, and functions.
With the Databricks Metastore, users can easily discover and access their data assets within the platform. It offers features like schema enforcement, versioning, access control policies, and integration with other tools in the Databricks ecosystem.
FAQs:
Q: Why is a metastore important?
A: A metastore is crucial for efficient data management in big data environments. It allows users to store metadata information about their datasets centrally so that it can be easily accessed by different services or applications. This eliminates redundancy and ensures consistency across multiple systems accessing the same data.
Q: How does Databricks Metastore work?
A: The Databricks Metastore leverages Apache Hive’s metastore service to provide compatibility with existing Hive-based workflows. It stores metadata in an underlying database (such as MySQL or PostgreSQL) which enables fast querying capabilities for discovering available datasets within the platform.
Q: Can I use my own external metastore with Databricks?
A: Yes! While Databricks provides its own managed metastore service out-of-the-box, you also have the flexibility to connect your existing external Hive metastores if desired. This allows you to leverage your current investments in metadata infrastructure while benefiting from the Databricks platform.
BOTTOM LINE:
The Databricks Metastore is a crucial component of the Databricks Unified Data Analytics Platform, offering centralized metadata management capabilities. It enables users to easily discover and access their data assets while providing features like schema enforcement, versioning, and access control policies. The flexibility to use external metastores further enhances its compatibility with existing workflows.