Presentation: Gimel: PayPal’s Analytics Data Platform

Track: Real-world Data Engineering

Location: Cyril Magnin III

Duration: 9:20am - 10:10am

Day of week: Wednesday

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Abstract

At PayPal, data engineers, analysts and data scientists work with a variety of datasources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive).
Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed "Gimel", a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that are powered by a centralized data catalog.
In this session, we will introduce you to the various components of Gimel - Compute Platform, Data API, PCatalog, GSQL and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.

Speaker: Deepak Chandramouli

Analytics Tech Lead @PayPal

Deepak has over 13 years of experience in Data Engineering & 5 years of expertise building scalable data solutions in the Big Data space. He worked on building Apache Spark based Foundational Big Data Platform during the incubation of PayPal's Data lake. He has applied experience in implementing spark based solutions across several types of No-SQL, Key-Value, Messaging, Document based & relational systems. Deepak has been leading the initiative to enable access to any type of storage on Spark via - unified Data API, SQL, tools & services, thus simplifying analytics & large-scale computation-intensive applications.

Find Deepak Chandramouli 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.