About the Product Demo
AI initiatives depend on access to trusted, timely, and complete data, yet most organizations still rely on a patchwork of tools built for one workload at a time — batch ETL here, streaming ingestion there, replication and observability bolted on separately. As enterprise data spreads across applications, databases, event streams, and cloud and on-premises systems, this fragmentation slows delivery, drives up cost, and makes it harder to trust the data feeding AI.
In this session, we’ll go beyond the concept and show it live. Using IBM watsonx.data integration, we’ll build and run a working pipeline in real time — bringing batch and streaming integration together under a single control plane, with end-to-end observability across the entire flow. Along the way, we’ll highlight how IBM’s latest engine innovations are pushing the performance of real-time data processing, and how agentic capabilities are helping teams design and deliver pipelines faster without giving up governance or control.
Attendees will leave with a practical view of what it takes to unify batch, streaming, and observability into a single architecture, and a firsthand look at how modern, agentic data integration is helping organizations activate their data for AI — without sacrificing flexibility, trust, or choice.
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