Feb 23, 2023 🚀 Product Announcement: Run NumPy and Pandas Directly on Your Data Warehouse
Read moreTrouble scaling Pandas, the most popular data science/AI library? Try Ponder, which helps you run Pandas at scale directly in your data warehouse—be it Snowflake, BigQuery or Redshift—all from the comfort of your favorite IDE or notebook.
Ponder’s mission is to make data teams more productive by empowering them to get insights faster with tools they already know and love, like Pandas, the bread and butter of data science.
Run your Pandas workflows at all scales, from megabytes to terabytes, without changing a single line of code. No more painful out-of-memory errors and slow single-threaded execution.
df.merge()
df.pivot()
df.fillna()
df.describe()
df.explode()
df.merge()
df.pivot()
df.fillna()
df.describe()
df.explode()
Iterate on your Pandas workflows quickly, from prototype to deployment, all running securely within your cloud-native data warehouse. Turbocharge your productivity and speed up development cycles with lightning-fast, interactive results.
Leverage Pandas as-is on your existing data infrastructure, without introducing new requirements and dependencies.
Ponder's open-source library, Modin (5M+ downloads) enables data scientists to run pandas at scale on distributed computing backends, such as Ray or Dask.