Version Your Data Lakehouse Like Your Software With Nessie

Data Engineering Podcast

תוכן מסופק על ידי Tobias Macey. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Tobias Macey או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.

8M ago 40:55

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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.
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Links

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