Symplifying the Data Stack with Ponder

Not affiliated, but spreading the love. Because the capability of running pandas directly on data warehouses is far from being something casual.

Run Python Data Workflows at Scale Directly in Your Data Warehouse

Here are several key points that summarize why I appreciate this remarkable tool:

1. No need to pull data samples out for in-memory operations – leverage your existing data warehouse for computation.
2. No need to pull data out at all – with just a single switch of an import statement, you can run your Pandas workflows at any scale, from megabytes to terabytes, without changing a single line of code, and it performs at lightning speed.
3. Security, scale, and reliability, combined with a streamlined data stack – data analysts and data scientists can work on the same source of truth.

Just pure beauty.

Try it yourself.