Amazon S3: The Backbone of Modern Data Systems

Episode • Jun 3 • 1h 1m

Summary
In this episode of the Data Engineering Podcast Mai-Lan Tomsen Bukovec, Vice President of Technology at AWS, talks about the evolution of Amazon S3 and its profound impact on data architecture. From her work on compute systems to leading the development and operations of S3, Mylan shares insights on how S3 has become a foundational element in modern data systems, enabling scalable and cost-effective data lakes since its launch alongside Hadoop in 2006. She discusses the architectural patterns enabled by S3, the importance of metadata in data management, and how S3's evolution has been driven by customer needs, leading to innovations like strong consistency and S3 tables.

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 Mai-Lan Tomsen Bukovec about the evolutions of S3 and how it has transformed data architecture

Interview

Introduction
How did you get involved in the area of data management?
Most everyone listening knows what S3 is, but can you start by giving a quick summary of what roles it plays in the data ecosystem?
What are the major generational epochs in S3, with a particular focus on analytical/ML data systems?
- The first major driver of analytical usage for S3 was the Hadoop ecosystem. What are the other elements of the data ecosystem that helped shape the product direction of S3?
Data storage and retrieval have been core primitives in computing since its inception. What are the characteristics of S3 and all of its copycats that led to such a difference in architectural patterns vs. other shared data technologies? (e.g. NFS, Gluster, Ceph, Samba, etc.)
How does the unified pool of storage that is exemplified by S3 help to blur the boundaries between application data, analytical data, and ML/AI data?
What are some of the default patterns for storage and retrieval across those three buckets that can lead to anti-patterns which add friction when trying to unify those use cases?
The age of AI is leading to a massive potential for unlocking unstructured data, for which S3 has been a massive dumping ground over the years. How is that changing the ways that your customers think about the value of the assets that they have been hoarding for so long?
- What new architectural patterns is that generating?
What are the most interesting, innovative, or unexpected ways that you have seen S3 used for analytical/ML/Ai applications?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on S3?
When is S3 the wrong choice?
What do you have planned for the future of S3?

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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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Links

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

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