Extending DevOps to the database helps teams keep up with application deployments by shifting left and streamlining previously slow, manual stages in the workflow. Implementing change management automation and governance represents a major step in completing the “last mile” of your CI/CD pipeline. But to fully embrace DevOps and its benefits, you not only need to automate and integrate but continuously optimize your processes.
DORA’s four key metrics provide a compass to application and DevOps teams for continuous optimization of the development pipeline. Measured and represented in analytics and monitoring platforms, these insights drive a faster, more efficient and more innovative application development process.
How can the same spirit and quality of optimization extend to the database deployment workflow?
Organizations are leveraging database observability to apply DORA metrics to change management and use those insights to:
● Accelerate the pipeline
● Improve security
● Reduce errors and remediation
● Anticipate and prevent issues
● Detect and avoid database drift
Much like other elements of DevOps, it’s possible to bring visibility to the database change process thanks to the components that power workflow automation. Database observability unlocks DORA metrics along with other indicators that matter to your DevOps, application, database and IT teams.
The Role of Database Observability in DevOps
Database observability is the capacity to understand a system’s current state and optimize CI/CD pipeline performance, security, and efficiency by analyzing its outputs, logs and workflow data. These measurements inform insights for continuous improvement and proactive problem management, closing the gap between application development and database management in traditional DevOps practices.
Just like a DevOps observability dashboard provides visibility into the performance of application development workflows, a database observability dashboard showcases change management workflow measurements. It can answer questions like, “What is the current state of our environments?” or, more precisely, “Which changes have been deployed? Which of those succeeded or failed? What is the context surrounding those changes – and what can we learn from this information that can help make the pipeline better?”
Teams embrace database observability so they can:
● Streamline database operations by identifying bottlenecks and improving efficiency
● Enhance auditing capabilities through transparent and traceable database change logs
● Foster a culture of continuous improvement by leveraging data-driven insights for database management
● Align with application pipeline acceleration efforts to achieve overall DevOps agility and maturity
● Reduce the frequency and risk of deployment failures and operational downtime
Database observability enables you to shift left, proactively detecting issues and improving integrity earlier, before errors occur in production or further down the pipeline. It supports a high-performing data infrastructure with the ability to detect problems before they endanger your production system and identify opportunities for process improvement that make way for the team to focus on progress and innovation.
DORA Metrics for Database Change Management Optimization
With visibility into your systems, you can use actionable insights to inform decisions for the continuous improvement of your CI/CD pipeline. Think of DORA’s metrics (deployment frequency, lead time for changes, change failure rate, and time to restore service) in the database context, then expand to answer your optimization questions.
Identifying metrics is a critical step in the database observability initiative. Observability insights will help answer, “How do we improve these metrics?” and “What does improvement look like?” By capturing and analyzing database change logs over longer periods, teams can see trends and patterns previously obscured to them by lack of visibility.
Breaking down deployment frequency, for instance, can help highlight the metrics by database, application, and team to find high and low performers or discover inefficiencies in data connections. Similarly, filtering and slicing observability data to create baselines, comparisons and aspirations enables teams to use the data they’re already collecting to look inward and optimize the process itself. This can answer questions like:
● Which deployment pipelines are least efficient and why? How can these be optimized, unblocked and accelerated?
● How often are database rollbacks occurring – and how is that affecting reliability?
● What kinds of errors are most common and costly – and how can they be avoided?
● Which users and systems are accessing which databases – and how can they be more secure?
In addition, database observability provides security and compliance enhancements by enabling trend analysis, reducing remediation time and anticipating potential issues before they occur. It can also identify drift in the intended state of your environment with detailed reports to find the problem fast.
Achieving database observability has typically been an uphill battle – as one of the last areas of the overall application pipeline to be automated, database change management often relies on manual processes that take hours or days, not seconds. However, automated database change management workflow data provides all the ingredients for a successful database observability dashboard, not to mention the capability for self-service deployments and built-in policy checks.
In the context of full-pipeline visibility and continuous optimization, database observability brings much-needed clarity and granularity to teams embracing DevOps at every stage of their workflows.
