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

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

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