Master AI Observability Tools: Best Practices for Data Engineers

A best-practices guide for data engineers on using AI observability tools to maintain data integrity and pipeline visibility. Covers real-time monitoring with automated column-level lineage, integrating data quality and governance frameworks, leveraging ML for anomaly detection, and fostering cross-team collaboration. Uses Decube as the primary platform example, citing case studies like a 40% reduction in incident response times at a financial services firm and a 30% drop in quality incidents at a telecom company.