Charting A Path For Streaming Data To Fill Your Data Lake With Hudi - Episode 209

Summary

Data lake architectures have largely been biased toward batch processing workflows due to the volume of data that they are designed for. With more real-time requirements and the increasing use of streaming data there has been a struggle to merge fast, incremental updates with large, historical analysis. Vinoth Chandar helped to create the Hudi project while at Uber to address this challenge. By adding support for small, incremental inserts into large table structures, and building support for arbitrary update and delete operations the Hudi project brings the best of both worlds together. In this episode Vinoth shares the history of the project, how its architecture allows for building more frequently updated analytical queries, and the work being done to add a more polished experience to the data lake paradigm.

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  • We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial.
  • Your host is Tobias Macey and today I’m interviewing Vinoth Chandar about Apache Hudi, a data lake management layer for supporting fast and incremental updates to your tables.

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Hudi is and the story behind it?
  • What are the use cases that it is focused on supporting?
  • There have been a number of alternative table formats introduced for data lakes recently. How does Hudi compare to projects like Iceberg, Delta Lake, Hive, etc.?
  • Can you describe how Hudi is architected?
    • How have the goals and design of Hudi changed or evolved since you first began working on it?
    • If you were to start the whole project over today, what would you do differently?
  • Can you talk through the lifecycle of a data record as it is ingested, compacted, and queried in a Hudi deployment?
  • One of the capabilities that is interesting to explore is support for arbitrary record deletion. Can you talk through why this is a challenging operation in data lake architectures?
    • How does Hudi make that a tractable problem?
  • What are the data platform components that are needed to support an installation of Hudi?
  • What is involved in migrating an existing data lake to use Hudi?
    • How would someone approach supporting heterogeneous table formats in their lake?
  • As someone who has invested a lot of time in technologies for supporting data lakes, what are your thoughts on the tradeoffs of data lake vs data warehouse and the current trajectory of the ecosystem?
  • What are the most interesting, innovative, or unexpected ways that you have seen Hudi used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Hudi?
  • When is Hudi the wrong choice?
  • What do you have planned for the future of Hudi?

Contact Info

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

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

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