Reconciling The Data In Your Databases With Datafold

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March 17th, 2024

58 mins 14 secs

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About this Episode

Summary

A significant portion of data workflows involve storing and processing information in database engines. Validating that the information is stored and processed correctly can be complex and time-consuming, especially when the source and destination speak different dialects of SQL. In this episode Gleb Mezhanskiy, founder and CEO of Datafold, discusses the different error conditions and solutions that you need to know about to ensure the accuracy of your data.

Announcements

  • 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 welcoming back Gleb Mezhanskiy to talk about how to reconcile data in database environments

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by outlining some of the situations where reconciling data between databases is needed?
  • What are examples of the error conditions that you are likely to run into when duplicating information between database engines?
    • When these errors do occur, what are some of the problems that they can cause?
  • When teams are replicating data between database engines, what are some of the common patterns for managing those flows?
    • How does that change between continual and one-time replication?
  • What are some of the steps involved in verifying the integrity of data replication between database engines?
  • If the source or destination isn't a traditional database engine (e.g. data lakehouse) how does that change the work involved in verifying the success of the replication?
  • What are the challenges of validating and reconciling data?
    • Sheer scale and cost of pulling data out, have to do in-place
    • Performance. Pushing databases to the limit, especially hard for OLTP and legacy
    • Cross-database compatibilty
    • Data types
  • What are the most interesting, innovative, or unexpected ways that you have seen Datafold/data-diff used in the context of cross-database validation?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Datafold?
  • When is Datafold/data-diff the wrong choice?
  • What do you have planned for the future of Datafold?

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

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