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.
No new APIs to learn. Code using native Python, pandas, NumPy, and others. Your code is quickly production-ready, with 1-click to scale from a laptop to 1000’s of cores; no tuning needed.
Analytics run 10x-100x faster than PySpark, Dask, or Ray, with linear performance scaling – benefitting from true parallel computing speed. Even large analytics jobs can generate near-real-time results.
Easily handle TBs of data and billions of rows across 1000's of cores - all with the native Python, pandas and other APIs you currently use. No Spark needed.
Reduce computing costs by 90% or more with improved resource efficiency. Parallel computing architecture eliminates schedulers, wait-states, and other performance bottlenecks found in distributed computing architectures.
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.