Scalable Data Science at Your Fingertips
Trouble 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.
The most popular tool for data science/AI, now scalable
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.
Clean data at scale,
with zero effort
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()
# import pandas as pd
import modin.pandas as pd
Time
Data science, now with scale, security, and governance built-in
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.
Run Pandas everywhere
Leverage Pandas as-is on your existing data infrastructure, without introducing new requirements and dependencies.
From the team that brought you popular open-source library Modin
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.
Powering data teams at the Fortune 100 and more!
