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DirectoryData & AnalyticsData Warehouse & LakehouseApache Hive

Apache Hive

Open Source

About Apache Hive

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. It facilitates reading, writing, and managing petabytes of data residing in distributed storage using SQL. It is built on top of Apache Hadoop with support for S3, ADLS, GS and more. The Hive Metastore provides a central repository of metadata. It is backed by the Apache Software Foundation.

Products

  • HiveServer2 (HS2): Supports multi-client concurrency and authentication with support for open API clients like JDBC and ODBC.
  • Hive Metastore Server (HMS): Serves as the central repository of metadata for Hive tables and partitions, providing access to clients including Hive, Impala, and Spark.
  • LLAP (Low Latency Analytics): Provides interactive and sub-second SQL queries through persistent query infrastructure and data caching.

Used for

  • Reading, writing, and managing petabytes of data using SQL syntax.
  • Replicating data for backup and disaster recovery.
  • Compacting data to optimize storage and query performance.
  • Integrating with Apache Iceberg tables for data lake architectures.
  • Analyzing metadata to make data driven decisions.

Who it is for

  • Used by data analysts and engineers.

From Apache Hive's own website, read 2026-10-23. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Apache Hive. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

Cloud Data Warehouse
25%
Query Performance & Concurrency
TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards
20%
Data Ingestion & Integration
Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace
20%
Governance & Security
Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications
15%
AI/ML Integration
Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving
10%
Cost Management
Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing
10%
Ecosystem & Tooling
BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace
Data Lakehouse Platforms
25%
Open Format & Catalog Interoperability
Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it
20%
Query Engine Performance & Concurrency
Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks
20%
Data Engineering, Streaming & AI/ML
Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities
15%
Governance, Security & Lineage
A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data
10%
Operational Simplicity & Table Maintenance
Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it
10%
Cost Model & Consumption Control
Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

hive.apache.org
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse
DeploymentCloud storage integration

We publish a detail only when we can point at the page it came from. Know when Apache Hive was founded or where it is based? Send it to us, free.

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