Managing Schema Validation in a Data Lake Using Data Version Control

It’s not uncommon for a data team to be dependent on many other “third parties” that send in the data. They often change the schema of the data without communicating anything or letting the data team know too late.

Whenever that happens, data pipelines break, and the data team needs to fix the data lake. This is a manual process filled with heavy-lifting tasks. Typically, teams engage in a blame game, trying to prove that the schema has changed.

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