DiscoverData Engineering PodcastVersion Your Data Lakehouse Like Your Software With Nessie
Version Your Data Lakehouse Like Your Software With Nessie

Version Your Data Lakehouse Like Your Software With Nessie

Update: 2024-03-10
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Summary



Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between data lake and warehouse capabilities is the catalog. The primary purpose of the catalog is to inform the query engine of what data exists and where, but the Nessie project aims to go beyond that simple utility. In this episode Alex Merced explains how the branching and merging functionality in Nessie allows you to use the same versioning semantics for your data lakehouse that you are used to from Git.



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 Alex Merced, developer advocate at Dremio and co-author of the upcoming book from O'reilly, "Apache Iceberg, The definitive Guide", about Nessie, a git-like versioned catalog for data lakes using Apache Iceberg



Interview




  • Introduction

  • How did you get involved in the area of data management?

  • Can you describe what Nessie is and the story behind it?

  • What are the core problems/complexities that Nessie is designed to solve?

  • The closest analogue to Nessie that I've seen in the ecosystem is LakeFS. What are the features that would lead someone to choose one or the other for a given use case?

  • Why would someone choose Nessie over native table-level branching in the Apache Iceberg spec?

  • How do the versioning capabilities compare to/augment the data versioning in Iceberg?

  • What are some of the sources of, and challenges in resolving, merge conflicts between table branches?

  • Can you describe the architecture of Nessie?

  • How have the design and goals of the project changed since it was first created?

  • What is involved in integrating Nessie into a given data stack?

  • For cases where a given query/compute engine doesn't natively support Nessie, what are the options for using it effectively?

  • How does the inclusion of Nessie in a data lake influence the overall workflow of developing/deploying/evolving processing flows?

  • What are the most interesting, innovative, or unexpected ways that you have seen Nessie used?

  • What are the most interesting, unexpected, or challenging lessons that you have learned while working with Nessie?

  • When is Nessie the wrong choice?

  • What have you heard is planned for the future of Nessie?



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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.

  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

  • If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.



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

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Version Your Data Lakehouse Like Your Software With Nessie

Version Your Data Lakehouse Like Your Software With Nessie

Tobias Macey