What does a data observability dashboard look like and how does it work?

The article explores the concepts of observability and monitoring within data pipelines, using Snowplow BDP as a case study, emphasizing the importance of high-quality behavioral data. It explains how observability offers a high-level view of data health through metrics like event volume and latency, which help identify potential issues. In contrast, monitoring involves a more granular investigation into specific pipeline components to diagnose and resolve problems. Snowplow's approach focuses on creating a single, ultra-high-quality data table serving as a single source of truth, emphasizing the need for observability to prevent issues from escalating. The text details the use of monitoring dashboards and metrics, such as CPU utilization and stream latency, to maintain data pipeline efficiency and reliability, underscoring the significance of integrating observability into data workflows to mitigate the costs of data downtime and poor-quality data.