Data warehouses and data lakes are converging on a common set of functions in the cloud, pairing high-performance SQL query structures with efficient, elastic object storage. Enterprises adopt these cloud data platforms, such as Microsoft Azure Synapse Analytics, Databricks and Snowflake, to reduce administrative hassle and consolidate data workloads. But data teams still need observability to keep a close eye on cloud platform performance, for example to meet BI query latency requirements. They also need to reduce the risk of compute cost overruns—and maintain all the necessary linkages back to legacy on-premises systems.

- Kevin Petrie in Data Observability: the Path to Healthier Analytics and AI Operations

December 23, 2020 (Blog)