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

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 Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. 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For data engineering podcast listeners, we’re offering a 30 day trial with unlimited data, so go to dataengineeringpodcast.com/upsolver today and see for yourself how to avoid DAG hell. 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 LinkedIn jaffee on GitHub @mattjaffee on Twitter 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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers Links FeatureBase Pilosa Episode Molecula Episode Bitmap Roaring Bitmaps Pinecone Podcast Episode Milvus Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By:Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. 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