Bodo + Snowflake

Faster, More Efficient Python Analytics for Snowflake

Drastically Improve Snowflake Efficiency and Performance for Large-Scale Python Computing

With fast distributed fetch and parallelized computation, Bodo helps data engineers to build highly performant, more cost-efficient Python analytics applications on their Snowflake data cloud.

Bodo’s performance and efficiency is most impactful for data engineers and data scientists using Snowflake workloads exceeding 100’s of GBs, and hundreds of millions of dataframe rows. Example use cases include ETL, data prep, feature engineering, and AI/ML ingestion.

Bodo's performance and cost efficiency has been shown to exceed 20x the speed of PySpark, and often 1/10 the EC2 computing cost for certain applications and benchmarks.
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How Bodo + Snowflake Work Together

The Bodo Platform sends a query to Snowflake. Snowflake workers compute the query then transform the resulting table into arrow files. Bodo’s distributed fetch loads the data in parallel chunks. The application is automatically parallelized by Bodo’s JIT compiler and executed by each cluster core on a parallel chunk of data loaded by the distributed fetch.
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