Over the past decade, advancements in analytics and machine learning technologies have revolutionized data collection, storage, processing, and visualization, yet the complexity and volume of data ecosystems pose significant challenges to maintaining data quality and reliability. As remote work increases reliance on data applications, the necessity for data performance monitoring becomes as essential as Application Performance Monitoring in software development, addressing issues like data anomalies and change management. Datafold aims to enhance data quality by embedding observability tools into analysts' workflows, enabling data developers to quickly verify changes and prevent incidents. By fostering interoperability within the modular data stack and engaging with the data community, Datafold seeks to democratize access to data observability tools, allowing teams to leverage data confidently without the extensive resources of tech giants. Their efforts are supported by investors like NEA, as they aim to shape the future of data engineering.