We’re all familiar with the core principles of DevOps: building small, well-tested increments, deploying frequently, and automating pipelines to eliminate manual processes. We closely monitor applications, set up alerts, roll back problematic changes, and ensure we’re notified when issues occur. Yet, when it comes to databases, this level of control and visibility is often lacking. Debugging performance issues can be daunting, and understanding why databases slow down isn’t always straightforward. Schema migrations and modifications can quickly become chaotic, creating significant challenges. Addressing these issues requires strategies to streamline schema migration and adaptation, allowing for seamless database changes with minimal downtime or performance degradation. Without these measures, the risk of data loss becomes very real - just as Linear experienced. Learn how to avoid such pitfalls.

You don’t need to test your databases to keep them in shape. There is a better way to achieve database reliability. Let’s see how.

Keeping pace with database advancements becomes even more challenging when machine learning and AI enter the equation. Companies that fail to adopt AI risk falling behind, wasting resources on outdated solutions, and missing out on the automation and efficiency AI can offer. If you're a C-level executive looking to elevate your business, read on to discover how AI can revolutionize observability.