IDUG NA 2026

This presentation explores the nuances of query parallelism in Db2, focusing on how to effectively tune queries for optimal zIIP exploitation. Attendees will gain insights through real-world query examples, highlighting the critical distinction of query access paths between iterative and merge strategies that are key to achieving successful query parallelism. Practical use cases will illustrate these principles in action, showcasing tangible improvements. Additionally, we will discuss how ChatGPT can assist in the query tuning processs. Join us to rediscover the power of parallelism and leverage zIIP processors to their fullest potential.

This presentation will cover how to benefit from using Real Time Statistic (RTS) and Utility History. I will explain a useful established example of Real Time Statistic History, share which usecases are covered by RTS History and why RTS Temporal out of the box is not sufficient for everyone.
Second part will share some necessary preparations for Vnext and will cover one path and best practices to get rid of deprecated non-UTS tablespaces, what to consider and why utility history is really helpful. It will give some early experiences on FL509 support to migrate remaining Catalog&Directory tablespaces. Relevant topics like “workfile separation” will be covered as well.

SQL Data Insights (SQL DI) in Db2 13 for z/OS brings built‑in AI directly into the database, enabling organizations to discover patterns, detect anomalies, and generate insights using familiar SQL—without moving data or building complex pipelines.
This session provides practical guidance on getting started with SQL DI, beginning with Db2 instrumentation data as a high‑value entry point. It will also demonstrate how to extend insights by ingesting VSAM data through Data Virtualization Manager.
In addition, the session highlights recent SQL DI enhancements, including a unified embedding model, SQL-based AI access, and incremental training capabilities—enabling teams to seamlessly embed AI into existing Db2 workflows.
The session concludes with an overview of Industry Starter Kits, featuring prebuilt datasets, vector tables, and AI queries that accelerate adoption and help organizations quickly realize high‑value use cases.

The AI problems landing on the desks of DBAs and application developers this year don't all need the same model. Some call for a Large Language Model. Others need a forecasting model, a clustering algorithm, or an embedding model. Plenty can be solved by predictive techniques that have been working quietly for years. One drawer of the toolbox has been getting all the attention. The skill that matters most, knowing which tool fits the problem, has gone quiet. If your data lives in Db2, much of the toolbox is already there.This session lays the toolbox out on the table. Two axes, what data you have and whether you have labels, organize the AI landscape into a 2×2 you can hold in your head: predictive and unsupervised models for structured data, the same for unstructured data, plus embeddings and generative AI.Then we map every drawer to a Db2 capability you can use today. In-database machine learning for classification, regression, and clustering. Native vector storage and similarity search. Built-in integration with external embedding and language models. Python frameworks for retrieval-augmented applications and AI agents. We close with production patterns we've seen ship, across the full range of model types and use cases.Whether you live in SQL or Python, you'll leave knowing which tool to reach for, and where to start.