IDUG EMEA 2026

Warehouse and data lakehouses are converging and Db2 is ready for this shift. Db2 Datalake tables enable access to open-format data stored on remote object storage directly from within the relational database, eliminating the need for complex ETL pipelines or data duplication. This session explores the technical architecture supporting both Hive (ODF) and Iceberg table formats, demonstrating how organizations can query PARQUET, ORC, AVRO, and TEXTFILE data alongside traditional Db2 tables in complex SQL queries while maintaining ACID properties where needed. Attendees will learn implementation strategies for converged warehouse architectures, performance optimization techniques, and real-world use cases where Datalake tables provide bidirectional data flow between relational databases and data lake / lakehouses.

This session is about deep dive technical details of how Z data is integrated with other platforms and solutions.
This is an architectural, use case and product level technical session. There will be latest news from the zSW Development and Product Management in the context of Data Management, focused on Db2 for z/OS.
The goal of this session is

What are data modernization Challenges – Past, Present, Future
IBM Z Data and AI Enterprise Architecture
Architectural alternatives to provide Db2 for z/OS data into other platforms for data modernization story
Core Products, Virtualization, Data Integration and AI Techniques
Replicate, Direct Access, Virtualization – Pros & Cons.
Technical and Business Use Cases with Capabilities
Roadmaps and Future direction of IBM

Are you running maintenance because it’s Sunday, or because the data actually needs it? With shrinking windows and massive data growth, "business as usual" is no longer an option. This session explores how Broadcom Db2 utilities provide the precision and confidence needed to manage modern environments. We will discuss establishing data-driven housekeeping practices, leveraging recovery toolkits to validate resilience without production impact, and optimizing high-speed data movement to support DevOps and rapid testing. Learn to transform your batch utilities from a routine expense into a high-performance engine.

When using Q-replication in evironments with different Db2 HA configuraions like HADR and pureScale and even single server Db2 installations or Db2 z/Os all together, configuration of Q-replication can become quite complex, especially on pureScale when trying to integrate the capture and apply programs into the resource model.
In this presentraion we will go through a different approach and build a seperate HA cluster for the Q-replication environment. This approach simplifies the setup of Q-replicatation and makes the target and source Db2 environment trasnparent to the replication environment, and it works with all possible Db2 setups.
And the best part, if you use Q-replication and Db2 LUW, you already have all the needed parts for this setup.

If multiple statements can be combined into a single one it will be more efficient.
In this session we will explore multiple options to combine different SQL statements. Have you ever used the FINAL TABLE clause or selected the data you just deleted? Besides this the main focus is on the MERGE stamement which is probably the most powerful SQL operation which offers a lot of processing options and can even be used in ETL architectures and with temporal tables. Practical use case will be shown and explained.

AI agents are transforming enterprise applications, but they need access to trusted, business-critical mainframe data. This session shows how MCP servers enable agents to query up-to-date, replicated Db2 z/OS data from targets like Db2 LUW or watsonx.data. It covers the rationale for agent integration, architecture patterns, implementation steps, and a live demo of an agent working with z/OS data.

Enterprise database migrations are often slowed to a crawl by their reliance on flat files. Large volumes of data must be written to disk, moved across networks, and reloaded using traditional utilities—a process that is time‑consuming, storage‑intensive, and error-prone. In migrating a legacy Oracle environment on AWS RDS to a modern, containerized Db2 platform running on OpenShift, it became clear that a fundamentally different approach was needed—one that removes the disk from the equation altogether.
This session presents a high‑performance, zero‑disk migration approach that streams data directly from the Oracle source into Db2. It walks through how to set up Db2 Federation and automate nickname creation to establish a direct data pipeline. Rather than relying on conventional SQL processing, the approach feeds a federated cursor straight into the Db2 LOAD mechanism, allowing data to be written directly into target tables with maximum efficiency.
To make this solution practical at