BlazingDB/blazingsql

Health v2 · maintenance only

Adoption not part of the score

2011 stars · 183 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity maintenance

database search-engine data-science databases data-science gpu-computing big-data analytics python sql-engine gpu-acceleration rapids cudf apache-arrow dataframes distributed-sql gpu linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="BlazingDB/blazingsql")