As companies become increasingly data-driven, the technologies underlying these rich insights have grown more and more nuanced and complex. While our ability to collect, store, aggregate, and visualize this data has largely kept up with the needs of modern data teams (think: domain-oriented data meshes, cloud warehouses, data visualization tools, and data modeling solutions), the mechanics behind data quality and integrity has lagged. To keep pace with data’s clock speed of innovation, data engineers need to invest not only in the latest modeling and analytics tools but also in technologies that can increase data accuracy and prevent broken pipelines. The solution? Data observability, the next frontier of data engineering and a pillar of the emerging Data Reliability category and the fix for eliminating data downtime. In this talk, we will learn about: - The rise (and threat) of data downtime - The relationship between DevOps Observability and Data Observability - Data Observability and its five key pillars - How the best data teams are leveraging Data Observability to prevent broken pipelines