Traditional data testing methods are often insufficient for ensuring data integrity due to their complexity and inability to catch edge cases, prompting the need for innovative approaches like Datafold. Data testing verifies the accuracy, consistency, and validity of data, akin to unit testing in software, and is crucial for avoiding costly downstream errors that can result from data quality issues. By shifting data testing to the left, integrating it early in the development process, developers can proactively catch and fix issues, thus preventing data anomalies from reaching production. The complexity of data testing arises from the need to ensure data accuracy across intricate, interdependent systems where traditional tests might miss edge cases and downstream effects. Datafold addresses these challenges via its data diff and column-level lineage technologies, which provide insights into how code changes impact data across the entire pipeline. By automating data testing within CI pipelines, Datafold ensures consistent application of data quality checks, allowing developers to focus on building features rather than managing tests. Through integration with version control systems and data warehouses, Datafold offers both no-code and programmatic implementations to accommodate diverse workflows, even for teams without existing CI pipelines.