# BlazingDB/blazingsql

BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

Repository: https://github.com/BlazingDB/blazingsql
Canonical: https://ross.abutalabs.com/products/blazingsql
Homepage: https://blazingsql.com
Language: C++
License: Apache-2.0
License Family: permissive
Topics: rapidsai, sql, python, machine-learning, machine-learning-workflow, artificial-intelligence, gpu-acceleration, gpu, rapids, blazingsql, data-science, cudf, gpu-dataframes, sql-engine, arrow, conda-environment
Last push: 2022-09-16T23:58:37+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2900, "days_push": 1447, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2011, forks 183 (observed 2026-08-28T04:06:05.085082+00:00)

## What it is
BlazingSQL is a GPU-accelerated distributed SQL engine for Python, built on the RAPIDS ecosystem and cuDF GPU DataFrames. It lets users run SQL queries against cuDF DataFrames and external data sources like S3, returning results as GPU DataFrames interoperable with RAPIDS libraries.

## Use cases
- run sql queries on gpu dataframes in python
- query csv or parquet files stored in amazon s3 with sql
- accelerate etl and analytics workloads on nvidia gpus
- integrate sql queries into rapids cudf data science workflows
- join and aggregate large datasets entirely in gpu memory
- use sql as an interface for machine learning feature engineering

## When to choose
- you already use RAPIDS cuDF and want a SQL interface over GPU DataFrames
- you need to query large datasets stored in S3 or other remote storage with GPU acceleration
- your analytics pipeline runs on NVIDIA GPUs and benefits from columnar, Arrow-based processing

## When to avoid
- you need a general-purpose production database with transactional guarantees
- you have no NVIDIA GPU available, since the engine depends on CUDA and RAPIDS
- you need actively maintained software with recent releases and community support

## Facets
- artifact type: library
- maturity: maintenance
- function: database, search-engine, data-science
- domain: databases, data-science, gpu-computing, big-data, analytics
- platform: python
- tags: sql-engine, gpu-acceleration, rapids, cudf, apache-arrow, dataframes, distributed-sql, gpu, linux, docker

## Member repositories
- BlazingDB/blazingsql (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:05.085082+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:01:04.055963+00:00, confidence not recorded.
  - readme: https://github.com/BlazingDB/blazingsql (fetched 2026-08-28T04:06:05.085082+00:00, sha d4b45edeeadf)
  - homepage: https://blazingsql.com (fetched 2026-08-29T10:41:00.603120+00:00, sha 547ff7ce6998)
  - site_page: https://blazingsql.com/about-school (fetched 2026-08-29T10:41:00.612440+00:00, sha 70ddb58634b5)
- Data as of 2026-08-30T08:39:29.467469+00:00.