About the Product Demo
Session 1: IBM – Turn Real-Time Data into AI-Ready Data with Unified Data Integration
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.
Session 2: Precisely – Modern Integration for Hybrid and Multi-Cloud Data
Data leaders don’t need another point-to-point connector — they need integration that holds up across mainframe, cloud, and everything in between. In this session, we’ll show how the Precisely Data Integrity Suite brings together real-time replication, flexible connectivity, and AI-assisted workflows to move and prepare data wherever it needs to go, without adding operational complexity. See how the suite handles the connections data teams actually deal with: legacy systems feeding modern platforms, hybrid and multi-cloud pipelines, and the growing need for data that’s ready for AI and analytics the moment it lands. Whether you’re modernizing infrastructure or scaling existing pipelines, you’ll leave with a clear picture of what integration built for reliability looks like in practice.
Session 3: Informatica – Architecting for AI: Modernizing Data Integration with Informatica
As enterprises scale Generative AI and advanced analytics, modern data architectures face unprecedented demands for speed, quality, and contextual richness. Join us as we explore how to design an AI-ready data architecture that unifies fragmented multi-cloud and on-premises environments. This presentation will demonstrate how Informatica’s Intelligent Data Management Cloud (IDMC)—powered by CLAIRE AI—automates high-performance integration pipelines, streamlines complex data orchestration, and feeds reliable, well-governed data directly to enterprise AI models and applications.
Session 4: Aiven – Why Is AI Still Such a Struggle? The Answer Is Layers Deep.
AI agents can react to events happening live, but only with access to the right data at the right time. That takes two things: grounding every answer in live operational data, and running the agent alongside the rest of your infrastructure. This can be accomplished with Retrieval Augmented Generation (RAG) and Aiven’s new Agents Runtime, and it’s what this session is about.
Most data architectures were built for people reading dashboards, not autonomous agents calling tools and making decisions. Bolt AI onto that same infrastructure and you get high token costs, slow responses, and answers nobody quite trusts.
We’ll show a working retail customer support agent, running on Aiven Runtime: querying PostgreSQL for order records, reading Kafka for real-time delivery events. Then we add DataHub, an open-source data catalog. The results are immediate. The agent now knows what data should exist, where it comes from, and why it might be missing. Instead of scanning blindly across every topic and table, it asks precise, targeted questions.
That’s what a unified platform changes. On Aiven, the open-source tools your agents depend on run under one control plane, across any cloud. Adding a new capability means provisioning a service, not building a whole new CI/CD pipeline. Every component is genuine open source: no proprietary forks, no vendor lock-in.
Session 5: CData – The Pipeline Trust Problem: How Enterprise Teams Move Data Without Breaking Production
Most enterprise teams aren’t stalling on ambition — they’re stalling on trust. Moving data off SAP, DB2, or SQL Server into Snowflake, Databricks, or ClickHouse sounds straightforward until a pipeline breaks mid-run, a source system can’t go offline, or a compliance audit asks what moved, when, and where.
This session shows you exactly how modern enterprises solve it — with working pipelines, not slides. Watch CData Sync replicate continuously across the systems your teams actually run.
You’ll see how CData Sync:
- Uses CDC to continuously capture changes from SAP, DB2, and SQL Server — eliminating full table scans, reducing source system load, and keeping downstream targets current without batch windows or manual refreshes
- Breaks down data silos across hybrid, on-prem, and cloud environments — connecting legacy and modern systems without re-architecting your infrastructure or writing a single custom script
- Keeps AI initiatives fed with fresh, governed, production-ready data — so your models, agents, and analytics aren’t running on stale copies or untrustworthy pipelines
- Fault-tolerant pipelines in action: incremental checkpointing, auto-retry, and automatic recovery — so your data keeps moving even when systems don’t behave
Where Online Conversations Become Real-World Connections
Join us this November in Providence, Rhode Island, for DGIQ + AIGov 2026 – where the insights you hear online turn into hallway conversations, meaningful connections, and collaborative breakthroughs.
