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.
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.
HOW TO USE
Simple
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.
Performant
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.
Scalable
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.
Efficient
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.
LEARN MORE
When To Use Bodo + Snowflake
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.
Use Cases
Where Bodo and Snowflake work together the best.
Data Prep and ETL
Data transformation to integrate and format data to be ready for analysis, reporting, and machine learning.
Feature Engineering
Data analysis to reveal categories, properties, and attributes of data.
Ml Model Training
Where fast, efficient ingestion and transformation of large data sets is needed.
Exploratory Analysis
For big data that includes large and/or repetitive data set analyses.
Exceptions
Where native Python is required, and/or may be more complex in SQL.
Complex Queries
Queries with complex “if-else” logic
Subqueries
Queries with multiple aggregation layers requiring subqueries
Multiple Locations
Processing data and landing to multiple locations
Data Size
>100GB >100M Rows
Processing
>100 CPU Cores
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