Reconciling The Data In Your Databases With Datafold

Data Engineering Podcast

Tobias Macey에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Tobias Macey 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

4M ago 58:14

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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?

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.
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Links

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