Episode Details

Summary

Data and analytics are permeating every system, including customer-facing applications. The introduction of embedded analytics to an end-user product creates a significant shift in requirements for your data layer. The Pinot OLAP datastore was created for this purpose, optimizing for low latency queries on rapidly updating datasets with highly concurrent queries. In this episode Kishore Gopalakrishna and Xiang Fu explain how it is able to achieve those characteristics, their work at StarTree to make it more easily available, and how you can start using it for your own high throughput data workloads today.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan.
• This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product today at dataengineeringpodcast.com/acryl
• Your host is Tobias Macey and today I’m interviewing Kishore Gopalakrishna and Xiang Fu about Apache Pinot and its applications for powering user-facing analytics

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Pinot is and the story behind it?
• What are the primary use cases that Pinot is designed to support?
• There are numerous OLAP engines available with varying tradeoffs and optimal use cases. What are the cases where Pinot is the preferred choice?
  • How does it compare to systems such as Clickhouse (for OLAP) or CubeJS/GoodData (for embedded analytics)?
• How do the operational needs of a database engine change as you move from serving internal stakeholders to external end-users?
• Can you describe how Pinot is architected?
  • What were the key design elements that were necessary to support low