DBTA E-EDITION
October 2026
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Trends and Applications
For much of the past decade, cloud storage has been thought of as the inevitable future of enterprise infrastructure. The promise was simple: Eliminate capital expenditures, scale infinitely, and only pay for what you use. For some workloads, that model still makes sense. But as data volumes explode and the economics of infrastructure shift, organizations are beginning to take a closer look at the long-term financial implications of relying primarily on cloud storage.
As enterprises move from AI experimentation to intelligent applications and agents, context engineering is emerging as a critical foundation for success. Delivering the right context at the right time across structured data, unstructured content, business rules, and real-time signals is becoming essential for improving AI accuracy, relevance, and reliability.
High availability and operational resilience are the goals of every CIO. They know that hardware fails, networks go down, software has bugs, and people make mistakes. Strategies are put in place to mitigate against such incidents, but there are times when IT operations management is betrayed by the very systems that are supposed to help keep technology and services running.
Companies are not doing enough to protect their data. Instead, they are mainly reacting after a problem occurs, but not anticipating or being proactive before an issue starts. That's the word from a number of industry experts who are sounding the alarm on AI applications and agents running on unstructured data that threatens to swamp even the most well-designed enterprise security infrastructures.
Data has long been considered the "Achilles Heel" of artificial intelligence. Data managers and their organizations have been pressing forward full throttle to build data estates that can handle the relentless AI surge. Yet, beneath all this activity is a persistent concern about the readiness of data environments to support enterprise-scale AI efforts.
Columns - DBA Corner
"Vibe coding" is the latest term making the rounds in the world of application development. The basic idea is simple enough: Instead of painstakingly writing every line of code manually, developers describe what they want to an AI coding assistant, review what it produces, adjust, and keep going. Development becomes more conversational, iterative, and, at least theoretically, faster.
Columns - SQL Server Drill Down
Today's enterprises have adopted AI with one promise in mind: efficiency. In the mindset of most IT leaders, efficiency equals simpler workflows, easier data access, faster results, and lighter burdens for IT teams. Unfortunately, according to a recent IT trends study, 71% of IT professionals report AI is actually making their role more demanding. Although AI has proven its ability to fast-track individual tasks, attaching AI technology to legacy processes has revealed a trap hidden under that promised simplicity.
Columns - Emerging Technologies
For more than 2 decades, job boards have been the default digital marketplace for talent. They promised efficiency, reach, and democratized access to opportunity. Yet in 2026, as organizations accelerate toward AI-driven workforce strategies, one truth has become impossible to ignore: Job boards are not database-intelligent. And that limitation is costing employers time, money, and competitive advantage.
MV Community
For distributors and manufacturers evaluating whether to modernize, the question is rarely "What will a new platform cost?" The better question is: "What is our current platform already costing us?" Kore Commerce is an ERP-integrated, customizable eCommerce platform built specifically for distributors and manufacturers. It integrates natively with Epicor (Eclipse, Kinetic, P21, and Prelude), DDI Inform, UniData/UniVerse, Infor, Oracle JD Edwards, NetSuite, and Microsoft Dynamics through Kore Integrate, a bi-directional data integration and warehousing platform.