Roy Daniel

Data observability is now standard in lakehouse platforms, but it stops at reactive monitoring. As teams push toward proactive agentic workflows, LLMs alone aren’t enough. Without production-aware context, ability to take action in-flight, and automated guardrails, AI remains limited to suggestive copilots trained on generic data. This session breaks down what it takes to build agentic capabilities to continuously optimize cost, resolve incidents in-motion, and evolve pipeline code. We’ll show how definity enables them through full-stack context across data, pipelines, and infrastructure; runtime execution control; and built-in validation loops — turning signals into autonomous action inside Spark pipelines.

Enterprise lakehouse platforms are rapidly scaling – and so are complexity and cost. After monitoring over 1B vCore-hours across Databricks and other Apache Spark™ environments, we consistently saw resource waste, preventable data incidents, and painful troubleshooting. Join this session to discover how definity’s unique full-stack observability provides job-level visibility in-motion, unifying infrastructure performance, pipeline execution, and data behavior, and see how enterprise teams use definity to easily optimize jobs and save millions – while proactively ensuring SLAs, preventing issues, and simplifying RCA.