What others are saying about Ponder

"Data scientists hate dealing with databases, but Ponder's integration with DuckDB is helping to change that. I got early access to try Ponder and it was magical. You just import ponder, connect it to DuckDB, and run pandas. And it just works!"

Jordan Tigani portrait

Jordan Tigani

CEO and Co-Founder of MotherDuck

"Wrangling large data sets is one of my biggest model development bottlenecks - it just takes so much time even with optimized clusters! I gave Ponder a try and I was amazed at how fast it was able to handle large data sets. For instance, simple pandas/pyspark functions like value_counts/countDistinct and count would take several minutes but using Ponder, results were coming back in less than 2 milliseconds - it's mad!"

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Henok Yemam

ML Scientist at Expedia Group

"At Intel, we believe that Modin is increasingly a critical component of data science and machine learning workflows. Intel is investing heavily in Modin through our Intel oneAPI toolkit to make accelerated computing accessible to all data science teams."

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Areg Melik-Adamyan

Principal Engineer and Data Platform Chief Architect at Intel

"Modin allows you to use the same Pandas script for a 10KB dataset on a laptop as well as a 10TB dataset on a cluster. This is possible due to Modin’s easy to use API and system architecture. This architecture can utilize Ray as an execution engine to make scaling Modin easier."

“What does Modin have to offer you as the end user? [...] it offers a very simple, drop-in replacement for pandas – you just switch your “import pandas as pd” statement with “import modin.pandas as pd” and gain better scalability for a lot of use cases.”

“Data scientists who don’t necessarily want to manage OmniSci as a separate component in their workflow sometimes need the full API surface of pandas, particularly during data shaping and ingestion. Modin [...] aims to provide a drop-in (but also scalable and performant) replacement for pandas that can leverage both Ray and Dask for distributed execution.”

“Data Scientists are increasingly required to do and learn more, but tools have largely lagged supporting all of these new requirements. [...]To improve data science productivity, MindsDB has teamed up with Modin to bring SQL to distributed Modin Dataframes. Now you can run SQL alongside the pandas API without copying or going through your disk.”

“Data infrastructure is already Intel-optimized, and Intel has now streamlined the most popular data science and AI tools, and created new ones that help clear the path forward. [...]Modin is an open source library that accelerates the popular Pandas data library by up to 20 times.”

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