Data Engineering Podcast

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


Listen Later

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. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
  • Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
  • RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudder
  • Build Data Pipelines. Not DAGs. That’s the spirit behind Upsolver SQLake, a new self-service data pipeline platform that lets you build batch and streaming pipelines without falling into the black hole of DAG-based orchestration. All you do is write a query in SQL to declare your transformation, and SQLake will turn it into a continuous pipeline that scales to petabytes and delivers up to the minute fresh data. SQLake supports a broad set of transformations, including high-cardinality joins, aggregations, upserts and window operations. Output data can be streamed into a data lake for query engines like Presto, Trino or Spark SQL, a data warehouse like Snowflake or Redshift., or any other destination you choose. Pricing for SQLake is simple. You pay $99 per terabyte ingested into your data lake using SQLake, and run unlimited transformation pipelines for free. That way data engineers and data users can process to their heart’s content without worrying about their cloud bill. 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](https://files.fireside.fm/file/fireside-uploads/images/c/c6161a3f-a67b-48ef-b087-52f1f1573292/CKNV8HZ6.png)
                          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.
                          RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team.
                          RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again.
                          Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

                          Support Data Engineering Podcast

                          ...more
                          View all episodesView all episodes
                          Download on the App Store

                          Data Engineering PodcastBy Tobias Macey

                          • 4.6
                          • 4.6
                          • 4.6
                          • 4.6
                          • 4.6

                          4.6

                          134 ratings


                          More shows like Data Engineering Podcast

                          View all
                          Software Engineering Radio - the podcast for professional software developers by se-radio@computer.org

                          Software Engineering Radio - the podcast for professional software developers

                          262 Listeners

                          The Changelog: Software Development, Open Source by Changelog Media

                          The Changelog: Software Development, Open Source

                          285 Listeners

                          The Cloudcast by Massive Studios

                          The Cloudcast

                          154 Listeners

                          Thoughtworks Technology Podcast by Thoughtworks

                          Thoughtworks Technology Podcast

                          43 Listeners

                          Data Skeptic by Kyle Polich

                          Data Skeptic

                          474 Listeners

                          Talk Python To Me by Michael Kennedy

                          Talk Python To Me

                          584 Listeners

                          Software Engineering Daily by Software Engineering Daily

                          Software Engineering Daily

                          630 Listeners

                          AWS Podcast by Amazon Web Services

                          AWS Podcast

                          200 Listeners

                          Super Data Science: ML & AI Podcast with Jon Krohn by Jon Krohn

                          Super Data Science: ML & AI Podcast with Jon Krohn

                          295 Listeners

                          Python Bytes by Michael Kennedy and Brian Okken

                          Python Bytes

                          212 Listeners

                          DataFramed by DataCamp

                          DataFramed

                          267 Listeners

                          Practical AI by Practical AI LLC

                          Practical AI

                          196 Listeners

                          The Stack Overflow Podcast by The Stack Overflow Podcast

                          The Stack Overflow Podcast

                          63 Listeners

                          The Real Python Podcast by Real Python

                          The Real Python Podcast

                          137 Listeners

                          Latent Space: The AI Engineer Podcast by swyx + Alessio

                          Latent Space: The AI Engineer Podcast

                          64 Listeners