Building a Trusted, Auditable Conversational Data Agent on Apache DolphinScheduler

#datascience #agents #ai #apchedolphinscheduler

How can we usher data workflows into the AI era?

Enterprise data platforms house vast stores of data. Yet bridging the gap between a business request and a stable, production-grade data job still requires navigating multiple hurdles across data discovery, development, scheduling, and operations. When workflow orchestration meets AI Data Agents, can natural language truly power the end-to-end data production pipeline?

The Apache DolphinScheduler August Online Meetup is coming! In this edition, we have invited Qingwang Li, Apache DolphinScheduler Committer and Big Data Platform Development Engineer at Cisco Webex, to share first-hand enterprise practices in building Data Agents.

📅 Date & Time: August 25, 14:00 (UTC+8)
📍 Register Now: https://meeting.tencent.com/dm/Uo71cAJuc6Xa
🎤 Session Topic: Enterprise Data Agent Practices Powered by Apache DolphinScheduler

Session Highlights

While enterprise data platforms store massive amounts of data, transitioning from business inquiries to reliably running data jobs remains a multi-step journey through data discovery, development, orchestration, and maintenance. This presentation introduces a comprehensive solution that leverages Apache DolphinScheduler as the workflow orchestration and execution foundation, integrated with Codex and MCP to build an intelligent Data Agent.

Users simply describe their business objectives in plain, natural language. The Agent automatically handles trusted data retrieval, analysis, and visualization. Once confirmed, it turns the business logic into schedulable workflows, seamlessly connecting compute engines including SQL, Spark, Flink, and ETL. From task deployment and execution monitoring to automated fault diagnosis, this architecture creates an intuitive, reliable, and fully auditable conversational data workspace.