Apache Hive, initially developed at Facebook, evolved from using a MapReduce execution engine to utilizing Apache Tez for faster and more efficient query processing. While Tez enhances performance by reducing query latency and offering more flexible query plans, it introduces complexities in troubleshooting due to the scattered nature of logs and metrics across various systems. Acceldata Pulse addresses these challenges by providing a comprehensive data observability platform for Hive-on-Tez workloads. It offers features such as query lineage visualization, resource usage breakdowns, automatic anomaly detection, root cause insights, and historical query comparisons. This platform enables data engineers and platform teams to troubleshoot queries faster, optimize performance, and maintain a unified view of their data stack. With Pulse, users can detect performance regressions, identify resource wastage, and diagnose frequent job failures, ultimately improving the efficiency and reliability of Hive workloads.