Over the past 15 years, Agile and DevOps have accelerated application delivery, enabling faster, more reliable releases. Yet the database layer often remains a blind spot for observability and performance monitoring.

In this article, Kevin Kline shows why closing the database observability gap is critical to improving performance, efficiency, and resilience for DBAs, developers, and the businesses that depend on their data.

In this post by Eoin Keenan, we look at a specific source of database risk most teams overlook: unmonitored, “shadow” database instances that never make it into your database monitoring tools. These “temporary” databases often run in production for months without patches, security controls, or performance visibility.

In the first part of this series, we established that successful AI adoption in database performance hinges on strong foundations. The most effective teams use a common framework for their database operations: monitor for a single, unified view; diagnose by cleaning signals with baselines and anomaly detection; optimize proactively to build durable resilience; and ensure these practices work everywhere across hybrid and multi-vendor environments.

This post looks at what stood out at SQL Bits 2025 and why it matters for today’s database teams.

It focuses on the key themes, practical takeaways, and broader conversations from the event, giving readers a clear sense of what was discussed and what they should pay attention to next.

In the race to modernize IT operations, many organizations have followed a predictable pattern: as infrastructure becomes more complex and tool sprawl surges, so does the tooling meant to manage it. More cloud; more dashboards. More apps; more telemetry. More vendors; more silos.

It’s easy to be tempted by the convenience of buying monitoring or observability software directly from your hardware provider. On the surface, it seems efficient: fewer vendors, “native” integration, and perhaps even bundled discounts. But this path can often lead to unintended complexity and long-term limitations.

In an era of constant technological flux, many IT teams find themselves running just to stand still. New platforms, services, and deployment models promise agility, but for organizations with deep investments in traditional infrastructure, adapting to this pace of change is anything but simple. The discussion around cloud-native transformation just keeps on growing.

Siloed data, siloed teams. Data fragmentation occurs when information is stored in isolated systems or formats, prompting inconsistencies, duplication, and a lack of visibility. In this second article in a series discussing the five most common obstacles to achieving full-stack observability, we explore why teams need a unified platform to shed light on their infrastructure and get their people on the same page.