AI readiness starts with your data foundation and for most organizations, that means confronting a database migration. This guide covers what makes a database truly AI-ready, how to assess whether your organization needs to modernize, and a practical step-by-step framework for executing a database migration for AI workloads with confidence.
14/04/2026 • 5 minutes
AI is transforming how software is built, tested, and deployed. In this DBTA webinar recap, we cover the key takeaways from our CEO Peter Caron and other industry experts on how AI is reshaping engineering roles, workflows, and data reliability in modern DevOps environments.
09/03/2026 • • 3 minutes
Scaling AI successfully depends on more than powerful models. Without strong data readiness for AI, engineering teams struggle with inconsistent data, hidden schema issues, and unreliable pipelines. Before organizations invest further in AI development, leaders must ensure their data is accessible, structured, and trustworthy enough to support it.
05/03/2026 • • 4 minutes
Poorly designed MongoDB queries can quietly become costly performance bottlenecks as data grows. In this guide, Anumadu Moses shares eight practical, repeatable steps to help you prototype queries incrementally, validate assumptions against real data, analyse execution plans early, and design indexes that support real world workloads. The result is more reliable, scalable, and production ready MongoDB queries.
16/02/2026 • • 5 minutes
In 2026, the biggest competitive advantage won’t come from automation alone, but from data analysts who can interpret outputs, apply business context, ensure governance, and turn insights into action. Explore the top data analysis trends for 2026 and why human–AI collaboration, not replacement, has a big part to play.
20/11/2025 • • 4 minutes
Josh Blair, Lead Development Engineer, Kanso Software
