Datafold addresses the challenge of maintaining data quality in complex data pipelines by catching unintended changes to immutable data before deployment to production. While data engineers often focus on optimizing pipelines for performance, the accuracy of data across these systems is frequently overlooked due to the complexity of validation methods, such as dbt tests, unit tests, or manual SQL queries. Immutable data, which should remain constant over time, can change due to coding errors, data integration failures, data transformation errors, and data migration issues. Datafold proposes a simple solution to proactively detect these changes, which are not as uncommon as assumed, ensuring the integrity and reliability of data pipelines.