Version Your Data Lakehouse Like Your Software With Nessie Data Engineering Podcast

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 invol