PayPal Notebooks powered by Jupyter is new major ecosystem for data analytics and exploration at PayPal, with kernels, magics, and utilities for analytics and engineering. Hundreds of PayPal’s data scientists, analysts, and developers use Jupyter to access data spread across filesystem, relational, document, and key-value stores, enabling complex analytics and an easy way to build, train, and deploy machine learning models. Romit Mehta and Praveen Kanamarlapudi explain how PayPal built its Jupyter infrastructure and powerful extensions.
Romit Mehta is a product manager at PayPal focusing on core big data and analytics platform products, which include a compute framework, a data platform, and a notebooks platform. In this role, Romit is working to simplify application development on big data technologies like Spark and improve analysts’ and data scientists’ agility and ease their access to data spread across a multitude of data stores via friendly technologies like SQL and notebooks. In his 19-year career, Romit has built data and analytics solutions for a wide variety of companies across the networking, semiconductor, telecom, security, and fintech industries. Outside of data products, Romit spends his time with his wife Kosha and their two wonderful kids, Annika and Vedant.
Praveen Kanamarlapudi is a senior software engineer on the core data platform team at PayPal, where he builds scalable and distributed platforms, including a highly available Jupyter platform that is being used by hundreds of the company’s data scientists, analysts, and developers. He’s also a contributor to Livy and Sparkmagic.
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