Cloud Exchange 2026: Splunk’s Jonathan Gines on tackling post-migration challenges

The National Endowment for the Arts used an artificial intelligence coding agent to develop a replacement in a week for its legacy grants system.

NASA has relied on the core Flight System framework to run everything from telescopes to avionics to command and control instruments since the early 2000s.

Agencies look to AI to modernize legacy processes, but disparate data and systems pose a challenge, says Salesforce's federal data and integration director.

Federal agencies are beginning to use AI agents to tackle complex mission-critical processes, Google specialist engineering expert says.

Agencies are moving quickly to adopt AI, but Red Hat chief architect says agentic AI implementation requires strong frameworks.

AWS Transform – continuous modernization (preview) automatically scans code repositories to detect, prioritize, and remediate technical debt at scale.

Between 2029 and 2032, every currently supported long-term support (LTS) version of Java will reach end-of-support within a single three-year window: Java 17 in 2029, Java 8 in 2030, Java 21 in 2031, and Java 11 in 2032. On paper, this looks like a manageable upgrade cycle. In practice, it creates a collision of timelines that most enterprises have failed to forecast. Organizations attempting to modernize incrementally—moving application by application, version by version—are operating on a model that the calendar has already rendered obsolete. The primary danger here is the illusion of time. Traditional modernization plans rely on sequential upgrades and controlled pacing. However, when every major Java version expires in the same compressed window, sequential planning collapses. By the time this becomes obvious, organizations will be forced into reactive mode, making rushed decisions under extreme pressure. The modernization illusion For organizations planning traditional stepwise up

How shifting the operational focus from isolated data products to systemic domain architecture resolves technical bottlenecks and optimizes platform investment. The post The Domain Shift: Moving Data Governance from Product Triage to Infrastructure Investment appeared first on Towards Data Science.