Validation of replication, migration, and pipelines
Verify data migrations. Verify that all data was copied when doing a critical data migration. For example, migrating from Heroku PostgreSQL to Amazon RDS.
Verify data pipelines. Moving data from a relational database to a warehouse/data lake with Fivetran, Airbyte, Debezium, or some other pipeline.
Maintain data integrity SLOs. You can create and monitor your SLO of e.g. 99.999% data integrity, and alert your team when data is missing.
Debug complex data pipelines. Data can get lost in pipelines that may span a half-dozen systems. data-diff helps you efficiently track down where a row got lost without needing to individually inspect intermediate datastores.
Detect hard deletes for an
updated_at-based pipeline. If you're copying data to your warehouse based on an
updated_at-style column, data-diff can find any hard-deletes that you may have missed.
Make your replication self-healing. You can use data-diff to self-heal by using the diff output to write/update rows in the target database.
Comparing tables within one database to validate successful transformaitons
Inspect differences between branches. Make sure your code results in only expected changes.
Validate stability of critical downstream tables. When refactoring a data pipeline, rest assured that the changes you make to upstream models has not impacted critical downstream models depended on by users and systems.
Conduct better code reviews. No matter how thoughtfully you review the code, run a diff to ensure that you don't accidentally approve an error.