This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
Analyze Massive Data At Interactive Speeds With The Power Of Bitmaps Using FeatureBase
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.
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 Matt Jaffee about FeatureBase (formerly known as Pilosa and Molecula), a real-time analytical database engine built on bitmaps
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?
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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