Over the past 15 years, Agile and DevOps have transformed how organizations deliver software.
Agile emphasizes iterative development, collaboration, and continuous feedback, while DevOps brings development and operations together to automate and accelerate software delivery. Teams now rely on rapid iterations, shared responsibility, and automation.
Yet in many organizations, databases remain isolated from these practices, often treated as a separate layer rather than fully integrated into the software delivery process.
Lessons from the 2025 State of Database Report
The 2025 State of Database Report shows how database blind spots affect teams. DBAs spend a lot of time on reactive work. Many are open to changing roles. Only a minority have unified monitoring. Cloud database costs often run higher than planned, especially for teams operating without the advantage of modern IT practices.
Our Bridging the Observability Gap whitepaper focuses on what to do about this. It shows how unified observability, database-aware CI/CD, shift-left practices, and closer collaboration between developers and DBAs can bring the database into the same modern lifecycle as application code.
In this article, I’ll highlight the main ideas from that whitepaper and connect them to the key findings from the 2025 State of Database Report. The goal is to explain why the database layer still behaves like a black box in many environments, and show how teams can start to close that gap.
“Without seeing inside the database, performance and reliability remain a guessing game.”
The Silent Risk of Database Black Boxes
Databases contain critical technical and operational data, but visibility is usually limited to basic infrastructure metrics. A lot of monitoring tools often focus on the application or network layer, leaving the database as an unobserved silo. According to the 2025 State of Database Report, only a minority of DBAs report having a fully unified monitoring environment.
Subtle issues that thrive in the database dark include:
- Deadlocks and locking conflicts that create unpredictable latency spikes and can freeze systems.
- Resource exhaustion caused by sudden CPU, memory, or storage capacity concerns.
- Unoptimized queries and antiquated architecture that increase execution time and use up valuable resources.
- Schema drift between environments that breaks automated workflows and leads to unexpected failures.
Closing this observability gap brings these hidden issues into the light, so teams can catch performance problems before they affect users or cause downtime.
“When teams cannot see issues forming in the database until they become incidents, alerts spike, context is missing, and the same problems resurface again and again…”
The Human Cost: DBA Burnout and the 27-Hour Fix
The 2025 State of Database Report shows how this lack of visibility plays out in day-to-day work. DBAs spend an average of 27 hours per week on reactive or routine tasks, such as responding to alerts, troubleshooting incidents, and managing emergency fixes. That is more than half of a standard workweek spent treating symptoms instead of improving architecture, automation, or performance.
When teams cannot see issues forming in the database until they become incidents, alerts spike, context is missing, and the same problems resurface again and again. This fuels alert fatigue and burnout, with more than a third of DBAs actively considering leaving their current roles.
Instead of participating in Agile and DevOps practices such as planning performance, shaping schema changes, or leading AI adoption, the database team is pulled back into a permanent fire drill.
The Financial Impact of Invisible Inefficiency
The observability gap also shows up directly on the balance sheet, especially in cloud environments. Inefficient queries, unoptimized workloads, and ad hoc fixes drive unnecessary CPU, memory, and storage consumption. In the State of Database findings, more than half of professionals cite cloud costs for operational databases as a leading cause of budget overruns.
Teams that still treat the database as a separate, manual layer pay twice: once in cloud waste and again in instability. Without database-aware CI/CD, deployments fail more often, rollbacks take longer, and incidents consume expensive engineering time. When burnout finally pushes a DBA to leave, replacing that expertise typically costs six to nine months of salary in recruitment, onboarding, and lost productivity.
All of this makes a strong case for bringing the database fully into the same unified observability and shift-left practices that already protect the rest of the stack.
“High-performing organizations are characterized by unified environments and a formal investment in training to bridge these gaps.”
Closing the Ownership Gap
There is a notable perception gap between leadership and front-line DBAs. While about half of IT executives believe their teams have a fully unified monitoring environment, far fewer DBAs agree. This misalignment means executives may underestimate technical hurdles, such as replication lag or performance bottlenecks, that DBAs handle daily.
A collaborative Database DevOps approach addresses this by treating database changes with the same rigor as application code. High-performing organizations are characterized by unified environments and a formal investment in training to bridge these gaps.
Modernizing the CI/CD Pipeline
Integrating database changes into automated CI/CD pipelines allows for testing, peer review, and clearer tracking. In mature organizations, database elements such as schema changes, stored procedures, triggers, and seed data live alongside application code in version control and follow the same Git-based workflows.
Build automation tools package database artifacts, like DACPAC files or SQL schema scripts, into the same pipeline that builds and tests application code. The artifacts are then deployed consistently across development, staging, and production.
This standardizes rollouts, reduces configuration drift and manual mistakes, and makes every change auditable. When combined with automated tests, static code analysis, and performance-focused quality gates, database changes can move at the speed of application development with a lot less risk.
The ultimate goal is to help teams shift from reactive troubleshooting to more strategic optimization.
Shifting Left for Proactive Health
In modern database DevOps practices, shifting left means adding performance checks and monitoring earlier in the development cycle. Currently, while most organizations use some form of proactive performance testing, not many conduct regular disaster recovery drills. By embedding observability from the start, teams can detect bottlenecks before they reach production.
This approach provides true visibility by correlating telemetry data, including metrics, logs, and traces, across the entire stack.
Reimagining Observability with Advanced Insights
As data environments continue to grow in scale and complexity, advances in AI and machine learning are starting to play a bigger role. By combining database observability with AI-driven anomaly detection and predictive analytics, teams can move even further from reactive firefighting toward proactive performance management.
Modern anomaly detection spots unusual query patterns, bottlenecks, and potential incidents before they affect users >
- Faster diagnosis: Nearly three-quarters of DBAs in unified environments report faster diagnosis of performance issues when they use AI.
- Greater efficiency: More than half of DBAs using AI say it has reduced the time they spend on manual or repetitive tasks.
The Path Forward
So where do we go from here?
Bridging the observability gap starts with treating the database as s part of software delivery, not a separate system.
Database work should follow the same processes for planning, change management, and incident response, with comparable visibility to applications and infrastructure. A practical way forward focuses on three areas:
- Standardize how changes flow: Treat schema updates, stored procedures, and configuration changes as code in shared repositories, deployed through automated pipelines across development, staging, and production. This cuts manual steps and rollout surprises.
- Shine a light on database behavior: Use observability to correlate query performance, waits, resource usage, and blocking with application and infrastructure telemetry. This replaces ad hoc scripts with a single view of where time, cost, and risk are building up.
- Build a shared performance culture: Give developers, DBAs, and operations access to the same data so they can diagnose issues together and align on priorities. Training and enablement help DBAs move beyond firefighting to lead optimization and modernization efforts.
Teams that follow this path move away from reactive, siloed workflows toward a model where everyone owns data performance and the database is fully integrated into how software is planned, built, and run.
FAQ: The Database Observability Gap
1. What is the database “observability gap”?
This is the gap between how complex databases have become and how little most teams can actually see inside them. Application and infrastructure layers are usually well monitored, but the database is often a black box. Problems like deadlocks, resource exhaustion, slow queries, and schema drift can build up quietly until users feel the impact.
2. How does this gap affect DBAs and teams?
Because issues show up late and without clear diagnostics, DBAs spend a lot of time in reactive “firefighting.” The State of Database findings show they average about 27 hours a week on reactive or routine work. That workload drives alert fatigue, burnout, and higher attrition risk, which makes it harder for teams to keep up with growing data demands.
3. How do Database DevOps and CI/CD make databases safer?
Database DevOps applies the same good habits used for application code to the database. Schema changes, stored procedures, triggers, and seed data are version-controlled, peer-reviewed, and deployed through automated CI/CD pipelines. With tests, static analysis, and performance gates built in, changes become more consistent and auditable, and deployments fail less often.
4. What does it mean to “shift left” for database health?
Shifting left means moving performance checks and monitoring earlier in the development cycle. Instead of waiting for production incidents, teams use telemetry such as metrics, logs, and traces to spot bottlenecks and resilience gaps in pre-production. This reduces downtime and cloud waste and gives DBAs more space to focus on optimization instead of emergencies.
5. How can SolarWinds help close the observability gap?
Teams need tools that make database behavior visible in real time and work with existing DevOps workflows. SolarWinds® Database Observability, SolarWinds® Database Performance Analyzer, and SolarWinds® SQL Sentry® provide deep visibility into query performance, blocking, and resource usage across platforms like SQL Server, Oracle, MySQL, and PostgreSQL. With historical baselines, anomaly detection, and proactive alerts, these solutions help close the observability gap and keep database performance aligned with user expectations.
Resources
- Bridging the Observability Gap: A detailed guide to modernizing database management with the help of observability software.
- 2025 State of Database Report: This report explores what this disconnect means for your business, revealing risks to performance, reliability, and talent retention.
- Optimizing Database Performance: A practical guide for DBAs and IT pros. Learn best practices in indexing, SQL tuning, execution plans, and observability in today’s hybrid IT environments.
