Enterprise-Ready Data Science at Your Fingertips
Ponder helps you run your Python data workflows at scale directly in your data warehouse. Seamlessly move from prototype to production using the tools that data teams know and love.
Your Python data workflows,
now running on your data warehouse
Ponder brings the best of both worlds: a fully pandas-native experience that is familiar and fast to prototype, plus the scalability and reliability of operating in a data warehouse.
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.
Get Started
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 or slow single-threaded execution.
Get Starteddf.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
Run Pandas everywhere
Don’t let your warehouse investments go to waste. Leverage your existing data warehouse as compute. No additional infrastructure setup required.
Get StartedFrom the team that brought you popular open-source library Modin
Ponder was founded by the creators of the popular open-source library Modin, which enables data scientists to run pandas at scale on distributed computing backends, such as Ray or Dask. Modin is embraced by the community and has seen adoption across sectors, including by the world's leading AI companies.
Powering data teams at the Fortune 100 and more!
