BlazingSQL Documentation

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BlazingSQL is a GPU accelerated SQL engine built on top of the RAPIDS AI data science framework. RAPIDS AI is a collection of open-source libraries for end-to-end data science pipelines entirely in the GPU. BlazingSQL extends RAPIDS AI and enables users to run SQL queries on Apache Arrow in GPU memory.

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BlazingSQL Intro

BlazingSQL is a GPU accelerated SQL engine built on top of the RAPIDS ecosystem. RAPIDS is based on the Apache Arrow columnar memory format, and cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.

BlazingSQL is a SQL interface for cuDF, with various features to support large scale data science workflows and enterprise datasets.

  • Query Data Stored Externally - a single line of code can register remote storage solutions, such as Amazon S3.
  • Simple SQL - incredibly easy to use, run a SQL query and the results are GPU DataFrames (GDFs).
  • Interoperable - GDFs are immediately accessible to any RAPIDS library for data science workloads.

Forget about schema management, preprocessing, and loading — run SQL directly on files or GPU DataFrames.


  • Anaconda or Miniconda installed
  • OS Support
    • Ubuntu 16.04/18.04/20.04 LTS
    • CentOS 7
  • GPU Support
    • Pascal or Better
    • Compute Capability >= 6.0
  • CUDA Support
    • 10.1.2
    • 10.2
    • 11.0
  • Python Support
    • 3.7
    • 3.8

Updated 25 days ago

BlazingSQL Intro

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