IDUG NA 2026

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

Modern engineering teams move fast, but brittle tests, flaky mocks, and sluggish integration pipelines can slow even the strongest React + Node backends to a crawl. Database testing is often the first casualty—SQL tests get skipped because they require a persistent Db2 instance, complicating CI/CD and slowing development. This session presents a battle‑tested alternative: using SQLite as an ephemeral, SQL‑compatible stand‑in for Db2 to deliver fast, reliable, production‑like backend tests. You’ll learn how to spin up isolated in‑memory databases, seed realistic data, run true end‑to‑end API tests, and automate teardown without touching production infrastructure. We’ll cover schema alignment, SQL‑driven test design, and strategies for keeping tests blazing fast as your backend grows. If you want to test backend logic as it actually runs—not as mocks pretend it runs—this talk will give you the practical patterns and real‑world insights to elevate your testing strategy and ship with confidence.

This session will concentrate on customer experiences in Db2 13 and positioning of the next release of Db2. Where are most customers at in their Db2 13 experience, Function Levels, exploitations. What are the items customers are exploiting, utilizing and lessons learned in the endevor. Session will be a mixture of Db2 DBA and System Programmer viewpoints.

Security is often treated as an afterthought—apps launched without proper controls, cloud environments spun up outside organizational standards, third-party vendors granted excessive access to sensitive data. These missteps vary in form, but they all share a common denominator: data. And for attackers, data is the only thing that truly matters.
Regardless of how strong your perimeter defenses are, once an attacker gets inside, your ability to detect and respond quickly becomes critical. That’s where Guardium Database Activity Monitoring (DAM) comes in. DAM helps organizations understand what “normal” looks like—enabling real-time detection of anomalies, suspicious behavior, and potential breaches before serious damage is done.
In this session, we’ll explore how implementing IBM Guardium can reduce risk, improve incident response, and stop data leaks before they happen. Because when everything else can be bypassed, your best defense is knowing your data patterns.
Key scenarios we’ll examine include:- Privilege escalation and misuse- High-volume data extraction- Cross-site scripting (XSS) via database inputs- OS command injection through vulnerable queries- Abuse of privileged accounts- Unauthorized access to restricted or sensitive data- Password spraying and brute-force attempts- Anomalous behavior outside of baseline operations- Change reconciliation failures (e.g., Guard App event correlation)
For each use case, we’ll analyze:- What the attack looks like in terms of raw database activity- How DAM tools can be tuned to detect these patterns- Alerting and response workflows for containment and remediation

The future of Db2 for z/OS is Universal Table Spaces (UTS) with IBM signaling that the next version of Db2 will no longer support legacy structures.
At the same time, Db2 itself is evolving toward UTS, with catalog and directory objects already being prepared for conversion through future Db2 13 function levels—reinforcing that UTS is the long-term foundation of the platform.
Despite this, many environments continue to delay conversion. The result is growing data volumes, increased complexity, and higher risk when the transition eventually becomes unavoidable.
This session focuses on why UTS conversion is essential. Beyond the “why,” we will dive into the “how”—sharing real-world examples of conversion failures and lessons learned from the field.

Twenty-one years ago, I joined IBM as a software developer. I knew nothing about AI. Today I am the AI Architect and Master Inventor of IBM Db2, leading vector search, in-database machine learning, and LLM integration.
How did the journey happen? Not the way I thought it would be. 

In this talk, I will share the blueprint I wish someone had given me earlier in my career: seven skills that took me from software engineer to AI architect. I will be honest about which ones moved me forward and which I learned too late. Some are technical. Most are not. They include learning AI from mentors, building communication as a craft, and learning to focus in an attention-fractured industry.

If you are an experienced software engineer wondering how to make the move into leading AI work, this talk is for you.

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.