Google iconGoogleAug 19, 2026 ~1 min source read

How to modernize Apache Hive using Google Cloud’s Lakehouse runtime catalog

For over a decade, the Apache Hive Metastore (HMS) has served as the de facto metadata authority for big data analytics. Built on the open Apache Iceberg REST catalog specification, it is a runnable, zero-data-copy migration solution to help you transition your production Hive tables in minutes.

How to modernize Apache Hive using Google Cloud’s Lakehouse runtime catalog
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For over a decade, the Apache Hive Metastore (HMS) has served as the de facto metadata authority for big data analytics.

Built on the open Apache Iceberg REST catalog specification, it is a runnable, zero-data-copy migration solution to help you transition your production Hive tables in minutes.

Architectural and scaling bottlenecks Standalone HMS deployments rely on relational database backends (such as MySQL or Postgres) to track table schemas, partitions, and storage locations.

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The useful part

For over a decade, the Apache Hive Metastore (HMS) has served as the de facto metadata authority for big data analytics. Built on the open Apache Iceberg REST catalog specification, it is a runnable, zero-data-copy migration solution to help you transition your production Hive tables in minutes. Architectural and scaling bottlenecks Standalone HMS deployments rely on relational database backends (such as MySQL or Postgres) to track table schemas, partitions, and storage locations.

How it works

  • As data lakes grow to hundreds of thousands of partitioned tables, partition pruning and bulk listing operations lead to key performance bottlenecks on the relational database.
  • A complex Spark job requesting partition metadata can spike metastore CP...
  • However, as enterprise data architectures scale to petabytes and span multiple query engines (such as Google Cloud Managed Service for Apache Spark, BigQuery, and Trino), legacy Hive Metastores often become...

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