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Analyze Massive Data At Interactive Speeds With The Power Of Bitmaps Using FeatureBase

2022/11/28
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Data Engineering Podcast
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Shownotes Transcript

Summary

The most expensive part of working with massive data sets is the work of retrieving and processing the files that contain the raw information. FeatureBase (formerly Pilosa) avoids that overhead by converting the data into bitmaps. In this episode Matt Jaffee explains how to model your data as bitmaps and the benefits that this representation provides for fast aggregate computation. He also discusses the improvements that have been incorporated into FeatureBase to simplify integration with the rest of your data stack, and the SQL interface that was added to make working with the product easier.

Announcements

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  • Your host is Tobias Macey and today I’m interviewing Matt Jaffee about FeatureBase (formerly known as Pilosa and Molecula), a real-time analytical database engine built on bitmaps

Interview

  • Introduction

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

  • Can you describe what FeatureBase is?

  • What are the use cases that it is designed and optimized for?

  • What are some applications or analyses that are uniquely suited to FeatureBase’s capabilities?

  • What are the notable changes/evolutions that it has gone through in recent years?

  • What are the forces in the broader data ecosystem that have had the greatest impact on your project/product focus?

  • What are the data modeling concepts that platform and data engineers need to consider when working with FeatureBase?

  • With bitmaps as the core data structure, what is involved in translating existing data into bitmaps?

  • How does schema evolution translate to the data representation used in FeatureBase?

  • How does the data model influence considerations around security policies and governance?

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

  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on FeatureBase?

  • When is FeatureBase the wrong choice?

  • What do you have planned for the future of FeatureBase?

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 Machine Learning Podcast) helps you go from idea to production with machine learning.

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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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