Apache Gravitino
Best for self-hosting
Why teams pick it
Organizations with data spread across multiple clouds, regions, or on-premises systems
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
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
Key stats
- 12Alternatives
- 2Support self-hosting
Run on infrastructure you control
- 11Active 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.
Start with these picks
These projects match the most common migration paths for teams replacing Google BigQuery.
Databend
Privacy-first alternative
Why teams pick it
Unified deployment: local Python install, Docker self-host, or managed cloud
All open-source alternatives
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.
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.
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.
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
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.
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
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.
chDB
In-process SQL OLAP engine powered by ClickHouse
Active developmentPermissive licenseIntegration-friendlyC++
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.
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.
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.
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.
ByConity
Cloud-native data warehouse with compute-storage separation for large-scale analytics
Permissive licenseFast to deployAI-powered workflowsC++
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.
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.
- 11 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.