IDUG NA 2026

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.

After completing an assessment of IBM’s Bob, using Db2 for z/OS use cases, I was amazed and disappointed. This introductory session summarizes key items found during the assessment. I’ll demonstrate a Db2 for z/OS client and a System Profiles GUI created, both without writing a single line of code.

The panel frames this discussion around the aspects most critical to enterprise database operations—the “Fantastic Four”: system capacity, performance optimization, cost management, and AI-driven analysis. Panelists will share real-world experiences and practical insights on applying AI to analyze database telemetry, anticipate workload changes, optimize resource utilization, and support data-driven decision-making at scale.By bringing together perspectives from multiple organizations, this session highlights how AI-enhanced database management is evolving from reactive monitoring to intelligent, predictive operations, helping enterprises improve reliability, efficiency, and business outcomes while managing growing data demands.

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.