Optimization and Compatibility in Analytics Data Management

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Exploring strategies for optimizing heavy read usage while minimizing writing, focusing on file manipulation, versioning, and ecosystem compatibility. Highlighting the efficiency gained by breaking compatibility with hive table format early on in favor of the iceberg table spec.

Apache Iceberg is an open source high-performance format for huge data tables. Iceberg enables the use of SQL tables for big data, while making it possible for engines like Spark and Hive to safely work with the same tables, at the same time.

Iceberg was started at Netflix by Ryan Blue and Dan Weeks, and was open-sourced and donated to the Apache Software Foundation in November 2018. It has now been adopted at many other companies including Airbnb, Apple, and Lyft.

Ryan Blue joins the podcast to describe the origins of Iceberg, how it works, the problems it solves, collaborating with Apple and others to open-source it, and more.

This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments.

Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.

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