head to head · open source
Apache Pinot vs bigquery-utils
Apache Pinot has 6,139 GitHub stars, 1,509 forks, 1,403 open issues and last shipped yesterday. bigquery-utils has 1,312 stars, 337 forks, 64 open issues and last shipped 3 months ago. Apache Pinot leads on adoption by 368% (6,139 vs 1,312 stars). Apache Pinot is written in Java under Apache-2.0; bigquery-utils is written in Jupyter Notebook under Apache-2.0. Apache Pinot has attracted 25% as many forks as stars, bigquery-utils 26%. Apache Pinot was the more recently maintained of the two, and both are self-hostable with no licence fee.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| Apache Pinot | bigquery-utils | |
|---|---|---|
| GitHub stars | ★ 6.1K | ★ 1.3K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Java | Jupyter Notebook |
| Last push | 2026-09-18 | 2026-07-03 |
| Forks | ⑂ 1.5K | ⑂ 337 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Apache Pinot if
- You weight community size — 6.1K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Java
- You value the larger contributor base for long-term maintenance
pick bigquery-utils if
- You want the bigquery-utils feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Jupyter Notebook
- You evaluated both and bigquery-utils fits your workflow better
About Apache Pinot
Apache Pinot is a real time distributed OLAP datastore built to deliver scalable real time analytics at low latency. It lives in the Java ecosystem, is licensed under Apache 2.0, and was originally built by engineers at LinkedIn and Uber. The project ingests from batch data sources such as Hadoop HDFS, Amazon S3, Azure ADLS, and Google Cloud Storage, as well as from stream data sources such as Apache Kafka. It is designed to scale up and out with no upper bound, and its performance stays constant based on the size of the cluster and an expected query per second threshold.
read the full Apache Pinot overview →
About bigquery-utils
Useful scripts, UDFs, views, notebooks, dashboards, and other utilities for migrating to and operating a data warehouse in Google BigQuery, aimed at data engineers, analytics engineers, and migration teams working on Google Cloud Platform.
read the full bigquery-utils overview →
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Frequently asked questions
Is Apache Pinot or bigquery-utils more popular?
Apache Pinot has 6,139 GitHub stars and bigquery-utils has 1,312. Apache Pinot has the larger community by that measure.
Are Apache Pinot and bigquery-utils free?
Both are open source. Apache Pinot is licensed under Apache-2.0 and bigquery-utils under Apache-2.0. Neither carries a licence fee.
What is the difference between Apache Pinot and bigquery-utils?
Apache Pinot is written in Java and bigquery-utils in Jupyter Notebook. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Apache Pinot or bigquery-utils?
Choose Apache Pinot if you want the larger community (6,139 stars) or its Apache-2.0 licence terms. Choose bigquery-utils if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.