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Why use Pachyderm with Snowflake?

Build ML models on top of your data warehouse solution with Pachyderm

Data Engineering and Science teams are increasingly looking to leverage their Data Warehouse for innovative machine learning projects such as churn analysis or customer lifetime value projections. However, getting the requisite data out of Snowflake or Redshift, and into data pipelines for experimentation and model training can be challenging.

Why do data engineering teams love Pachyderm?

Data-Centric. Pachyderm’s pipelines leverage automated versioning that drives incremental processing and data deduplication that shorten processing times and reduce storage costs. 

Scalable. Pachyderm scales to petabytes of data with autoscaling and data-driven parallel processing. 

Reproducible. Pachyderm automatically versions all data changes as well as keeps track of code changes so you always have full reproducibility and lineage for your ML models.

Native Integration. Getting data into and out-of your data warehouse is as simple as writing a SQL query.

Language and Data Agnostic. Use any language or library in your Pachyderm pipelines such as Python, R, Scala, or Bash. If you can get it into a container, then Pachyderm can run it as a pipeline. Easily process both structured and unstructured data.

Long-Lived, Multi-Step Pipelines. With Pachyderm you can build complex workflows that can support the most advanced ML applications.

See Pachyderm + Snowflake in Action

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