Open-source Google BigQuery alternatives

ClickHouse is an open-source database management system built to generate analytical reports in real time. Its column-oriented storage makes scans and aggregations over very large tables efficient, which is why it is widely used for event data, logs, metrics and product analytics. It is written in C++ and queried with SQL.

The project's topics describe it as an OLAP, massively parallel and distributed system, and it is used for big data workloads where query latency matters. You can install it on Linux, macOS or FreeBSD, follow a tutorial that sets up a small cluster, or skip installation by trying ClickHouse Cloud, a managed service built by its creators. Releases arrive monthly, with a community call for each one, and there are Slack and Telegram channels for help.

ClickHouse is Apache-2.0 licensed. It is a good fit for analytics engineers and platform teams who need fast aggregations over billions of rows and are comfortable operating a database, or who prefer to hand that operation to the vendor's cloud.

Apache Doris is an open-source analytics database built on a massively parallel processing architecture. It offers fast SQL analytics, acceleration of queries over lakehouse data, and hybrid search spanning structured, text and vector data, and it presents itself as a real-time analytics and search engine suitable for AI agents. It is written in Java.

The README lists use cases that include customer-facing analytics with sub-second interactive queries for external users, data warehousing across business domains, observability for high-throughput logs, events and metrics analyzed with SQL, and AI use where vector, text, JSON and structured search run in one SQL engine. Topics mention lakehouse formats Hudi, Iceberg and Delta Lake and comparisons with warehouses such as BigQuery, Redshift and Snowflake.

Doris is a project of the Apache Software Foundation and Apache-2.0 licensed. You can deploy it on your own clusters, and the website provides release notes, use cases and user stories, with documentation in a large number of languages. It suits data engineering teams that want an open-source, high-concurrency analytical database.

StarRocks is a query engine for analytics that returns answers quickly to multi-dimensional, real-time and ad-hoc queries. It is a Linux Foundation project written in Java, and can be used both on its own tables and over data that sits in a data lake or lakehouse without first moving it.

It uses a vectorized SQL engine that takes advantage of CPU parallelism, supports standard ANSI SQL and the MySQL protocol so existing clients and BI tools can connect, and applies a cost-based optimizer to complex queries. Primary-key tables support upserts and deletes with efficient querying during concurrent updates, and materialized views refresh during data import and are chosen automatically at query time.

Data in Hive, Iceberg, Delta Lake and Hudi tables can be queried in place, and its topics point to star-schema, MPP and distributed-database designs. The software is licensed under Apache-2.0 and can be downloaded and run yourself, with documentation, benchmarks and a demo linked from the README. It suits data teams that want fast dashboards and lakehouse analytics without heavy denormalization.

Databend is an enterprise-grade, open-source data warehouse written in Rust. It combines large-scale analytics, vector search, full-text search and automatic schema evolution in one engine, and keeps data in object storage such as S3, Azure Blob Storage or Google Cloud Storage with elastic, cloud-native compute.

The project now positions itself as a warehouse that is ready for AI agents. Sandboxed Python user-defined functions run agent logic, SQL handles orchestration, transactions provide reliability, and Git-like branching lets agents experiment safely on production snapshots. A three-layer architecture separates the control plane, the execution plane and the sandbox workers. Typical use cases listed include AI agents, analytics and BI, and search and RAG.

You can use the managed Databend Cloud, run it locally from Python for development and testing, or start the full warehouse in Docker. The repository lists the licence as Other, so review the licence file before commercial use. Topics reference Snowflake and Elasticsearch as comparable products.

Apache Hive is data warehouse software built on Apache Hadoop. It makes it possible to read, write and manage very large datasets in distributed storage using SQL, so analysts can run extract-transform-load jobs, reports and data analysis without writing low-level processing code.

Hive provides tools for accessing data through SQL, a way of imposing structure on a variety of data formats, and access to files stored in Apache HDFS or other systems such as Apache HBase. Queries can run on the Apache Tez framework, which is designed for interactive queries and has far lower overhead than MapReduce. It supports much standard SQL functionality, including many analytics features from the 2003 and 2011 SQL standards.

Hive is an Apache Software Foundation project written in Java and licensed under Apache-2.0. You deploy it on your own Hadoop-based clusters, though hosted big data platforms from cloud providers also bundle it. It suits data engineering teams maintaining batch analytics on large distributed data.

Apache Cloudberry, currently in the Apache incubator, is an open-source massively parallel processing database. It was created by original developers of Greenplum Database and evolves from the open-source version of Pivotal Greenplum, but uses a newer PostgreSQL kernel and adds enterprise capabilities. It is described as an open-source alternative to Greenplum Database.

It can serve as a data warehouse and is also intended for large-scale analytics and AI or machine learning workloads, distributing queries across many nodes. Because it is based on PostgreSQL, it uses familiar SQL. The main repository sits alongside ecosystem repositories for the website, a backup utility, Go libraries and the Platform Extension Framework (PXF) for connecting to external data.

Cloudberry is written in C and licensed under Apache-2.0. You can build it from source on Linux, including RHEL, Rocky Linux and Ubuntu, and macOS, or try it quickly through a Docker-based sandbox. Community help is available through Slack and GitHub Discussions. It suits data engineering teams running large analytical workloads that want an open-source MPP engine they can operate themselves.

GlareDB is a light and fast SQL database aimed at analytics. The project describes it as a lightweight, ergonomic database for quickly running analytics across a variety of sources, and it is written in Rust. It is meant for developers and analysts who want to query data with SQL without setting up a heavy data warehouse.

Installation is a single command from the project's site, and documentation, a blog and a changelog are available there. GlareDB uses calendar versioning in a year, month and patch format, so version 25.5.0 was the first release in May 2025. The code is licensed under MIT, and the company behind it is GlareDB, Inc. The README itself is brief, so see the documentation for supported data sources and query features.

Dremio is a data analytics platform aimed at organizations that want to query and analyze large volumes of data without first moving or copying it into a separate, dedicated analytics database. It positions itself as filling a gap between raw data storage and the tools people need to get value out of that data.

The open-source edition can be built and run locally, exposing a web UI at localhost:9047 once started, or installed as a production tarball for server deployment, with an embedded mode also available. Building it requires a specific combination of JDK versions (21 as default, with 17 and 11 configured in the Maven toolchain for certain tests) plus Maven, reflecting a substantial, enterprise-grade Java codebase.

The README notes that to provide the best possible experience, the build includes some dependencies distributed under non-open-source licenses, so the fully open-source experience may differ slightly depending on build configuration. Dremio is written in Java and the open-source edition is released under the Apache-2.0 license, with full documentation hosted separately.

TensorBase was a modern big-data warehouse project written in Rust, aimed at engineers who wanted a high-performance analytical database inspired by, and aiming to be compatible with, ClickHouse. It explored several firsts in database engineering, including a no-LSM, write-and-read optimized storage layer and running on real-world RISC-V hardware.

The project reported faster write throughput and faster simple aggregation query speed than ClickHouse in its own benchmarks, alongside experiments with a 'copy-free, lock-free, async-free, dyn-free' design in its critical path and an early prototype of a whole-lifecycle JIT SQL query engine, some of which was shared only through blog posts, presentations and videos rather than released in the open-source repository.

The README states plainly that the project has paused in the general data-warehousing space, explaining that the maintainers did not want open source to become a copy-and-fork game and chose to step back rather than compete on GitHub star counts. They recommend ClickHouse for anyone seeking a production-ready data warehouse today, while TensorBase remains available for people who want to study database internals or high-performance Rust techniques. It is released under the Apache-2.0 license.

EventQL is a distributed database built specifically for large-scale data collection and analytics workloads, aimed at teams that need to ingest very high volumes of streaming data while still running fast SQL and MapReduce-style queries over it. It is designed as a columnar, massively parallel (MPP) system rather than a general-purpose transactional database.

Tables are automatically partitioned by primary key and distributed across machines without manual shard configuration, and the system supports idempotent primary-key-based INSERT, UPSERT and DELETE operations, with UPSERT suited to exactly-once ingestion from streaming sources. Its compact columnar storage engine drastically reduces I/O for analytical workloads compared with row-oriented systems, and it supports nearly complete SQL 2009, including joins, with queries automatically parallelized across many machines. It claims to scale to petabytes across equally privileged servers and supports streaming, low-latency writes, with mutations immediately visible and minimal query latency as low as 0.1 milliseconds.

EventQL natively supports both time-series and relational data models through the same automatic partitioning mechanism. It is written in C++11, and the repository metadata lists its license as Other, so check the license file for exact terms before adopting it, particularly given the project's age and apparently limited recent activity.