IDUG NA 2026

Not all indexes are created equal. We explore the architectural differences between Partitioned, Non-Partitioned (NPSI), and Data-Partitioned (DPSI) indexes and when to deploy each for performance. Crucially, we explore each type's optimal recovery strategy so you can stop guessing between Rebuild and Recover.

In this presentation we will examine in detail the backup and recovery options available to DBAs for their Db2 environments. How to tune these utilities will be discussed and results will be reviewed from numerous tests. We will see what is the “art of the possible” from using local files, COS, Storage based copies, etc. … We will take a close look at the new version enhancements and how they can improve your backup and recovery experience.

Technical details to Db2 LUW usage and optimization of queries
There are many sessions about the EXPLAIN facility in Db2 LUW, but they all lack the concise statements to find the right SQL queries, set everything up, do the EXPLAIN and draw the right conclusions by examples and working statements to copy-and-paste.This is a back-to-basics session, enhanced with experiences from real situations and best practices.This session was presented at IDUG NA 2025 in Atlanta. It was in the Top10 of the user sessions. I will update the material to version 12.1 and preferrably include details regaring AI optimizer

Distributed client applications (from a server) are the most important and common of new workload using Db2 Z.
There are many concepts and terms thrown around which we often too often gloss over without a complete understanding.
This presentation will re-explain basic terms from my user perspective plus share some experiences learned (the hard way?) over recent years.
This presentation covers THREADS and DBATS and CONNECTION. What they mean and how they work and what our Db2 can tell us about their usage and importance.

In today’s busy environment, there is a lot of pressure to automate as much as possible. To that end, and lacking AI tools to do my job, I have come up with a few shortcuts that allow me to automate some normally onerous tasks. In this presentation, I will describe and document some tasks I have automated in my shop. Hopefully some of this will prove useful.

Organizations that have optimized institutional data through governance, stewardship, and quality management are AI‑ready by design. Leveraging that institutional data both internally and externally for competitive advantage opens possibilities at scale never before imagined. This session will discuss:
Tool adoption without data maturity creates risk: Weak data foundations lead to unreliable results, reduced transparency, and diminished business value.AI builds on existing data foundations: Incremental enhancements—such as data scraping, enrichment, and feature refinement—are most effective when layered on curated data stores.Efficient access to data still matters: AI tools need immediate access to data.Data curation presents as a DBA responsibility: DBAs control data curation through standard operating procedures and best practices. Decision engines still matter: AI outputs are only as effective as the decision logic, controls, and context in which they operate.AI is an evolution of data expertise, not a replacement: The role of data professionals becomes more critical, installing guardrails in automation and scalable intelligence.This session reframes AI success as the amplification of long‑standing data discipline. Attendees will gain practical insight into what they are doing today, for better or worse, that is curating data and what approaches they can apply to improve AI positioning. Best practices in database management are not a preparatory step for AI, but translate directly into sustainable AI advantage.