Cover art for podcast Data Engineering Podcast

Data Engineering Podcast

360 EpisodesProduced by Tobias MaceyWebsite

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.

59:24

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
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  • 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.
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  • 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 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 hosts@dataengineeringpodcast.com) 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

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

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