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
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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 val