Proactive Data Quality on the Data Engineering Podcast

In a 2021 episode of the Data Engineering Podcast, Gleb Mezhanskiy, co-founder and CEO of Datafold, discussed his proactive approach to tackling data quality issues, drawing from his experiences as a data engineer at companies like Autodesk and Lyft. He recounted a pivotal incident at Lyft where a minor SQL code change led to a significant data mishap, highlighting the need for tools that automatically detect and address data quality problems. This incident inspired him to create Datafold, a data observability platform aimed at enhancing the development process for data teams. The podcast explored the evolving challenges in data quality management, emphasizing the importance of integrating software development practices, such as version control and continuous integration, into data workflows. Mezhanskiy also detailed how Datafold's features, like Data Diff and column-level lineage, are used in data migration projects to ensure data consistency and build stakeholder confidence. The conversation underscored the critical role of data quality in business decisions and the necessity for organizations to adopt proactive data quality strategies.

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