Gnarly Data Waves

Episode 26

July 25, 2023

Versioning Data in the Data Lakehouse (File, Table and Catalog Versioning)

Learn about the benefits of versioning in integrating and deploying new data, and the differences between file, table and catalog versioning.

Versioning is a technique that has helped software developers to develop many practices that allow them integrate and deploy new code continuously allowing for more rapid development of software. In a world where data is being generated faster than even, the data community needs technology that allows for rapid integration and deployment of new data.

In this presentations we’ll discuss:

1. 3 Levels of Versioning on the Data Lakehouse (File, Table and Catalog)

1. Pros and Cons to each versioning paradigm

1. When should you use each?

Topics Covered

Data lakehouse

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Speakers

Alex Merced

Alex Merced is a Senior Tech Evangelist for Dremio, a developer, and a seasoned instructor with a rich professional background. Having worked with companies like GenEd Systems, Crossfield Digital, CampusGuard, and General Assembly.

Alex is a co-author of the O’Reilly Book “Apache Iceberg: The Definitive Guide.” With a deep understanding of the subject matter, Alex has shared his insights as a speaker at events including Data Day Texas, OSA Con, P99Conf and Data Council.

Driven by a profound passion for technology, Alex has been instrumental in disseminating his knowledge through various platforms. His tech content can be found in blogs, videos, and his podcasts, Datanation and Web Dev 101.

Moreover, Alex Merced has made contributions to the JavaScript and Python communities by developing a range of libraries. Notable examples include SencilloDB, CoquitoJS, and dremio-simple-query, among others.

Gnarly Data Waves

Versioning Data in the Data Lakehouse (File, Table and Catalog Versioning)

Register to view episode

Speakers

Ready to Get Started? Here Are Some Resources to Help

Infographic

Quick Guide to the Apache Iceberg Lakehouse

Analyst Report

It’s Time to Consider a Hybrid Lakehouse Strategy

Case Study

Navigating the Data Mesh Journey: Lessons from Scania’s Implementation

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