Presentation: Gimel: Commoditizing Data Access

Track: Real-world Data Engineering

Location: Cyril Magnin III

Duration: 9:00am - 9:10am

Day of week: Wednesday

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Abstract

Accessing data across a multitude of data stores is extremely fragile and complicated. To address this issue, we built a unified data processing platform at PayPal called Gimel. In this short talk I will introduce you to Gimel's compute platform and data platform and give an overview of how PayPal's data scientists, analysts and developers are taking advantage of Gimel using GSQL and PayPal Notebooks. 

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Romit Mehta

Product Manager, Data Platforms @PayPal

Romit has over 19 years of experience with data, building data and analytics solutions for a wide variety of companies across networking, semi-conductors, telecom, security and fintech industries. At PayPal he is leading the product management of core big data and analytics platform products which include a compute framework, a data platform and a notebooks platform. As part of this role, Romit is working to simplify application development on big data technologies like Spark and improve analyst and data scientist agility and ease of accessing data spread across a multitude of data stores via friendly technologies like SQL and notebooks. Outside of data products, Romit's time is spent with his wife Kosha and two wonderful kids, Annika and Vedant. 

Find Romit Mehta at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.