From VentureBeat
Ponder addresses Pandas scalability challenges with new tools
By Taryn Plumb
No one likes having to redo their work. It’s not only time-consuming, but it saps energy, creativity and productivity.
Yet that can often be the case for data scientists — particularly those working with large data sets and using the popular Pandas library for the Python programming language...
One of the most popular tools in the discipline, pandas is used by millions of data scientists. Still, the free software can become unusable when it comes to the large datasets that are now the norm. Although they may use pandas extensively, data scientists can run into performance problems at scale. As a result, they must rewrite pandas workloads into big data frameworks. In turn, they are producing fewer models and gleaning fewer insights in the production process, said Doris Lee, CEO of startup Ponder. Data pipelines from that point on can also be difficult to maintain and debug...
The data science startup launched in summer 2021 and was born out of RISELab. Co-founders Lee and Devin Petersohn focused their PhDs on the development of the technology. They were supported by Aditya Parameswaran, associate professor in the School of Information and Electrical Engineering and Computer Sciences.
Ponder was co-founded by I School alumna Doris Lee (Ph.D. 2021), School of Information and EECS professor Aditya Parameswaran, and UC Berkeley EECS alumnus Devin Petersohn.
