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 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.

Bodo + Snowflake: Better Together


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.

Snowflake Ready

Work with confidence. Bodo’s Snowflake Ready validated connector ensures functional and performance best practices and stability.

How Bodo and 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.

Use Cases

Bodo can perform ETL on the data and transfer it back to Snowflake, which can then
use the associated metadata and statistics to build fast analytics dashboards.
Data Prep and ETL
Data transformation to integrate and format data to be ready for analysis, reporting, and machine learning.
Ml Model Training
Where fast, efficient ingestion and transformation of large data sets is needed.
Feature Engineering
Data analysis to reveal categories, properties, and attributes of data.
Exploratory Analysis
For big data that includes large and/or repetitive data set analyses.
Where native Python is required, and/or may be more complex in SQL.
  • Queries with complex “if-else” logic
  • Queries with multiple aggregation layers requiring subqueries
  • Processing data and landing to multiple locations
Data Size
>100GB >100M Rows
>100 CPU Cores

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