Joe Murphy

As enterprises deploy AI agents across Databricks at unprecedented pace, trust at production speed requires a complete trust platform. This session covers how Acceldata integrates three layers — data observability, agentic data management, and Agent Observability — into a single operating model for reliable AI on Databricks. Each layer reinforces the next: observability without action is alerting, action without observability is guessing, and agents without governance is risk. From anomaly detection through autonomous remediation to agent trust, this approach points toward the self-governing lakehouse where data, agents, and policies stay in lockstep without humans in every decision.

Your AI is only as good as your data. Downtime, pipeline failures, and blind spots threaten revenue, compliance, and trust. Join Acceldata at Big Data London to explore Agentic Data Management (ADM), where AI agents autonomously resolve issues, optimize pipelines, and ensure governance. Powered by xLake Reasoning Engine, ADM delivers trusted, AI-ready data with self-healing operations. Hear how enterprises like Dun & Bradstreet boosted reliability and compliance. Ideal for data leaders, engineers, architects, analysts, product managers, and governance heads seeking autonomous data excellence. Visit Booth M70 for live demos

An intelligent, action-driven approach to bridge Data Engineering and AI/ML workflows, delivering continuous data trust through comprehensive monitoring, validation, and remediation across the entire Databricks data lifecycle. Learn how Acceldata’s Agentic Data Management (ADM) platform: - Ensures end-to-end data reliability across Databricks from ingestion, transformation, feature engineering, and model deployment. - Bridges data engineering and AI teams by providing unified insights across Databricks jobs, notebooks and pipelines with proactive data insights and actions. - Accelerates the delivery of trustworthy enterprise AI outcomes by detecting multi-variate anomalies, monitoring feature drift, and maintaining lineage within Databricks-native environments.