The View Below The Waterline Of Apache Iceberg And How It Fits In Your Data Lakehouse | Data Engineering Podcast

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Episode description

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Episode description

Cloud data warehouses have unlocked a massive amount of innovation and investment in data applications, but they are still inherently limiting. Because of their complete ownership of your data they constrain the possibilities of what data you can store and how it can be used. Projects like Apache Iceberg provide a viable alternative in the form of data lakehouses that provide the scalability and flexibility of data lakes, combined with the ease of use and performance of data warehouses. Ryan Blue helped create the Iceberg project, and in this episode he rejoins the show to discuss how it has evolved and what he is doing in his new business Tabular to make it even easier to implement and maintain.

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 Ryan Blue about the evolution and applications of the Iceberg table format and how he is making it more accessible at Tabular

Introduction

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

Can you describe what Iceberg is and its position in the data lake/lakehouse ecosystem?

Since it is a fundamentally a specification, how do you manage compatibility and consistency across implementations?

What are the notable changes in the Iceberg project and its role in the ecosystem since our last conversation October of 2018?

Around the time that Iceberg was first created at Netflix a number of alternative table formats were also being developed. What are the characteristics of Iceberg that lead teams to adopt it for their lakehouse projects?

Given the constant evolution of the various table formats it can be difficult to determine an up-to-date comparison of their features, particularly earlier in their development. What are the aspects of this problem space that make it so challenging to establish unbiased and comprehensive comparisons?

For someone who wants to manage their data in Iceberg tables, what does the implementation look like?

How does that change based on the type of query/processing engine being used?

Once a table has been created, what are the capabilities of Iceberg that help to support ongoing use and maintenance?

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

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Iceberg/Tabular?

When is Iceberg/Tabular the wrong choice?

What do you have planned for the future of Iceberg/Tabular?

From your perspective, what is the biggest gap in the tooling or technology for data management today?

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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