Apache Hive

Distributed data warehouse system built to run SQL analytics at massive scale. Enables reading, writing, and managing petabytes of data stored in distributed storage through a familiar SQL interface, without requiring teams to learn new languages or build custom batch pipelines.

Apache Hive has powered enterprise data warehouses for over 18 years and is used in thousands of production deployments worldwide. Stands out as the metastore layer for many data lakes: its Hive Metastore provides a central metadata repository that Spark, Presto, Impala, and hundreds of other tools rely on for table and partition discovery.

Key features:

• Hive Metastore as a shared metadata catalog for data lake ecosystems
• HiveServer2 with JDBC and ODBC for BI tools and multi-client concurrency
• ACID transactions for ORC tables and insert-only support for other formats
• Low Latency Analytics (LLAP) for interactive, sub-second queries
• Built-in Iceberg support for cloud-native table formats
• Native integration with S3, Azure Data Lake, and Google Cloud Storage

Data teams use Hive for batch ETL and reporting over lakehouse data, ad-hoc exploration with SQL, and as the metastore backbone when running Spark or Presto on shared datasets. Integrates with standard JDBC and ODBC clients, BI platforms, and orchestration tools like Airflow.