Databricks Hive Metastore


           Databricks Hive Metastore

Here’s a breakdown of the Databricks Hive metastore, including how it works with Unity Catalog:

What is the Hive Metastore?

    • Centralized Repository: The Hive metastore serves as a core component in Databricks (and Apache Hive-based systems) for storing metadata about your tables. Metadata includes: Table names
    • Column names and data types
    • Table locations (where the data is stored)
    • Partitions
    • Other table properties.
  • Enabling SQL-like Queries: This metadata is crucial for allowing SQL-like queries on data stored in various file formats within Databricks.

Databricks and the Hive Metastore: Two Options

Databricks offers two types of megastores:

  1. Legacy Hive Metastore (Workspace-Level):
    • Each Databricks workspace has its own Hive metastore.
    • This is the traditional meta store familiar to experienced Databricks users.
    • Access is primarily workspace-bound.
  2. Unity Catalog (Global-Level):
    • A more recent meta-store option provides greater governance and control.
    • Unity Catalog offers centralized metadata management across multiple Databricks workspaces.
    • It has enhanced security features, fine-grained access controls, and lineage tracking.

Working with both Metastores

  • Coexistence: Both the Hive metastore and Unity Catalog can be used simultaneously in Databricks.
  • Accessing Hive Metastore Data from Unity Catalog: The legacy Hive metastore appears as a top-level catalog named hive_metastore within the Unity Catalog. You can query tables in it using a three-level namespace (e.g., hive_metastore.database_name.table_name)

Key Considerations and Best Practices

  • Use Cases: The legacy Hive metastore is suitable for existing workspaces, and migrations might be necessary to leverage Unity Catalog’s advantages.
  • Migration to Unity Catalog: Databricks recommends migrating tables managed by the legacy Hive metastore to Unity Catalog for better governance and security.
  • New Projects: Using Unity Catalog for new Databricks projects is generally preferable to benefit from its enhanced features.

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