Create common_use_cases.md

Author: williebsweet

docs/common_use_cases.md

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+# Common Use Cases
+
+## joindiff
+- **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 have 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.
+
+## hashdiff
+- **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.