Accelerate Your Embedded Analytics With Apache Pinot

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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• 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-latency queries with high concurrency?
• Can you describe a typical end-to-end architecture where Pinot will be used for embedded analytics?
  • What are some of the tools/technologies/platforms/design patterns that Pinot might replace or obviate?
• What are some of the useful lessons related to data modeling that users of Pinot should consider?
  • What are some edge cases that they might encounter due to details of how the storage layer is architected? (e.g. data tiering, tail latencies, etc.)
• What are some heuristics that you have developed for understanding how to manage data lifecycles in a user-facing analytics application?
• What are some of the ways that users might need to customize Pinot for their specific use cases and what options do they have for extending it?
• What are the most interesting, innovative, or unexpected ways that you have seen Pinot used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pinot?
• When is Pinot the wrong choice?
• What do you have planned for the future of Pinot?

Contact Info

• Kishore
  • LinkedIn
  • @KishoreBytes on Twitter
• Xiang
  • LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

• Apache Pinot
• StarTree
• Espresso
• Apache Helix
• Apache Gobblin
• Apache S4
• Kafka
• Lucene
• StarTree Index
• Presto
• Trino
• Pulsar
  • Podcast Episode
• Spark

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA