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Open-source alternatives to Google BigQuery

Compare community-driven replacements for Google BigQuery in data warehouse & olap databases workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.

Google BigQuery logo

Google BigQuery

BigQuery is a managed analytics warehouse with ANSI SQL, separation of storage/compute, and built‑in ML and federation for large‑scale analysis.Read more
Data Warehouse & OLAP Databases
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Key stats

  • 12Alternatives
  • 2Support self-hosting

    Run on infrastructure you control

  • 12Active development

    Recent commits in the last 6 months

  • 9Permissive licenses

    MIT, Apache, and similar licenses

Counts reflect projects currently indexed as alternatives to Google BigQuery.

All open-source alternatives

Apache Doris logo

Apache Doris

High-performance real-time analytical database with MPP architecture

Active developmentPermissive licenseIntegration-friendlyJava

Why teams choose it

  • Sub-second query response times on massive datasets with MPP architecture
  • MySQL protocol compatibility with standard SQL and seamless BI tool integration
  • Storage-compute integrated architecture with horizontal scalability to petabyte scale

Watch for

Storage-compute integrated architecture may limit independent scaling flexibility

Migration highlight

Real-Time Business Dashboards

Deliver sub-second reporting and decision-making dashboards with real-time data ingestion from transactional databases, enabling automated business processes and instant insights.

Apache Gravitino logo

Apache Gravitino

Geo-distributed federated metadata lake for unified data governance

Self-host friendlyActive developmentPermissive licenseJava

Why teams choose it

  • Unified API for managing metadata across Hive, MySQL, HDFS, S3, and more
  • Geo-distributed architecture for multi-region and multi-cloud metadata sharing
  • Direct connector integration with immediate reflection of upstream changes

Watch for

Windows builds are not currently supported

Migration highlight

Multi-Cloud Data Lake Federation

Unified metadata access across AWS S3, Azure Data Lake, and on-premises HDFS, enabling cross-cloud analytics without data migration.

BemiDB logo

BemiDB

Postgres-compatible analytical database with built-in data sync connectors

Active developmentFast to deployIntegration-friendlyGo

Why teams choose it

  • Analytical query engine 2000x faster than regular Postgres for complex queries
  • Built-in connectors for Postgres, Amplitude, and Attio with table-level filtering
  • Compressed columnar storage in S3 with 4x compression using open table format

Watch for

Requires external Postgres database for catalog metadata management

Migration highlight

Centralized analytics without ETL complexity

Query data from multiple Postgres databases and SaaS platforms through a single endpoint without building custom pipelines

ByConity logo

ByConity

Cloud-native data warehouse with compute-storage separation for large-scale analytics

Active developmentPermissive licenseFast to deployC++

Why teams choose it

  • Compute-storage separation architecture for independent resource scaling
  • Advanced query optimizer delivering fast analytics on large-scale datasets
  • Unified ingestion for both batch-loaded and streaming data sources

Watch for

Requires FoundationDB client library dependency for operation

Migration highlight

Real-Time Analytics on Streaming Data

Ingest and query streaming events alongside historical batch data without maintaining separate systems, enabling unified analytics across all data sources.

chDB logo

chDB

In-process SQL OLAP engine powered by ClickHouse

Active developmentPermissive licenseIntegration-friendlyPython

Why teams choose it

  • Zero-installation embedded ClickHouse engine with no separate server required
  • Native support for 60+ formats including Parquet, Arrow, ORC, CSV, and JSON
  • Zero-copy data transfer between C++ and Python via memoryview for maximum performance

Watch for

Limited to single-process execution without distributed query capabilities

Migration highlight

Ad-hoc Parquet Analysis

Query multi-gigabyte Parquet files directly from disk with SQL, returning results as Pandas DataFrames without ETL pipelines or database imports.

CrateDB logo

CrateDB

Distributed SQL database for real-time analytics at scale

Active developmentPermissive licenseIntegration-friendlyJava

Why teams choose it

  • Standard SQL with PostgreSQL wire protocol and HTTP API support
  • Horizontal scalability with auto-sharding, auto-replication, and self-healing
  • Native time-series, full-text search, and geospatial capabilities

Watch for

Requires understanding of distributed database concepts for optimal deployment

Migration highlight

IoT Sensor Data Analytics

Ingest thousands of sensor readings per second and run real-time SQL queries for anomaly detection and trend analysis across distributed clusters.

Databend logo

Databend

AI-native multimodal data warehouse with Snowflake-compatible SQL

Self-host friendlyActive developmentPrivacy-firstRust

Why teams choose it

  • Snowflake-compatible SQL with multimodal data support (structured, vector, geospatial)
  • Native AI functions: vector search, embeddings, and full-text search built-in
  • S3-native architecture with Rust-powered vectorized execution engine

Watch for

Dual licensing (Apache 2.0 + Elastic 2.0) may restrict certain commercial use cases

Migration highlight

Snowflake Migration with Cost Optimization

Maintain SQL compatibility while reducing cloud warehouse costs by up to 90% through S3-native storage and eliminating proprietary compute overhead

DuckDB logo

DuckDB

High-performance in-process analytical SQL database for fast queries

Active developmentPermissive licenseIntegration-friendlyC++

Why teams choose it

  • In-process architecture eliminates server management and network latency
  • Query CSV and Parquet files directly without import steps
  • Advanced SQL dialect with window functions, complex types, and nested queries

Watch for

Optimized for analytics, not transactional OLTP workloads

Migration highlight

Interactive Data Exploration

Analysts query multi-gigabyte Parquet datasets on laptops without loading data into separate databases, accelerating insight discovery.

LakeSoul logo

LakeSoul

Cloud-native lakehouse with ACID transactions and streaming upserts

Active developmentPermissive licenseIntegration-friendlyJava

Why teams choose it

  • LSM-Tree upserts with concurrent writes and automatic conflict resolution
  • CDC ingestion with auto DDL sync and exactly-once streaming guarantees
  • PostgreSQL-backed metadata for scalable ACID transactions and MVCC

Watch for

Requires PostgreSQL for metadata management, adding infrastructure dependency

Migration highlight

Real-Time MySQL Replication

Sync entire MySQL databases to cloud storage with auto table creation, DDL propagation, and exactly-once CDC guarantees for downstream analytics.

OceanBase logo

OceanBase

Distributed relational database delivering high‑availability, linear scalability, and vector search.

Active developmentFast to deployAI-powered workflowsC++

Why teams choose it

  • Native vector search for AI and semantic workloads
  • Linear scalability to 1,500 nodes and petabyte‑scale data
  • Zero data loss (RPO=0) with sub‑8‑second recovery (RTO<8s)

Watch for

All‑in‑one deployment is Linux‑only

Migration highlight

Real‑time fraud detection

Processes billions of transactions per day while instantly querying vector embeddings to flag anomalies.

StarRocks logo

StarRocks

Sub-second ad-hoc analytics across data lakes and warehouses

Active developmentPermissive licenseFast to deployJava

Why teams choose it

  • Native vectorized SQL engine for sub‑second query latency
  • Real‑time upsert/delete support with primary‑key tables
  • Direct querying of Hive, Iceberg, Delta Lake, and Hudi

Watch for

Optimally runs on Linux/Unix environments only

Migration highlight

Business intelligence dashboards with sub‑second refresh

Analysts receive instant query results across multi‑dimensional data, enabling real‑time decision making.

YTsaurus logo

YTsaurus

Scalable, fault-tolerant platform for big-data storage and processing

Active developmentPermissive licenseIntegration-friendlyC++

Why teams choose it

  • Multitenant ecosystem with MapReduce, SQL, job scheduler, and key‑value store
  • Fault‑tolerant architecture with automated replication and zero‑downtime updates
  • Massive scalability to millions of CPU cores, exabytes of data, and tens of thousands of nodes

Watch for

Complex deployment may require Kubernetes expertise

Migration highlight

Real‑time clickstream analytics

Process billions of events per day with low latency using MapReduce and CHYT for instant dashboards.

Choosing a data warehouse & olap databases alternative

Teams replacing Google BigQuery in data warehouse & olap databases workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.

  • 2 projects let you self-host and keep customer data on infrastructure you control.
  • 12 options are actively maintained with recent commits.

Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Google BigQuery.