Even with dedicated database staff (often with impressive technical skills), businesses need help understanding how to address database issues. IT managers and database specialists are keen to try different approaches to keeping databases working as efficiently as possible. One popular approach is ‘DevOps’.
As companies navigate the complexities of digital transformation and a growing trend toward cloud migration, IT environments have become extraordinarily complex. While modern database solutions offer a range of benefits (greater speeds, security, reliability, and scalability than traditional solutions), even for very talented data professionals, moving data to the cloud can mean learning a whole new set of professional tricks.
Crucially, in this rapidly evolving landscape, regardless of the complexity on the backend, data-hungry applications on the frontend are still expected to run as smoothly as possible—often at even greater speeds than ever. No one accepts system complexity as an excuse for mediocre application performance.
The Database Challenge
Much of the drive for higher database performance comes from IT teams, database professionals, and business leaders hoping to use data to make meaningful technical and business decisions. The goal is increased productivity, with inefficiencies and blockers kept to a minimum. (Derailing the business with tactical decisions based on data from subpar systems just isn't part of the game plan.) But harnessing the power of data is easier said than done, particularly when the data is distributed across multiple platforms and multiple locations.
Unfortunately, as user demands on resources are at an all-time high, so are the chances that something will go amiss. Instead of faster resolution times, increased cost-efficiency, and improved end-user and customer experiences (to name some of the expected benefits of an investment in modern database technologies), poorly coded database solutions can end up causing performance bottlenecks and scalability issues.
Most application performance issues can be traced back to the data layer. The culprit? Inevitably, it’s either poor database design and/or poor SQL code quality. Without the right strategies and tools in place to mitigate database performance risks, what should be an asset (ultramodern database solutions brimming with useful data) can quickly become a liability. The good news is that most of the issues can be solved with good development and database management practices. This is where DevOps comes in.
The DevOps Promise
DevOps is a software development methodology that combines the work of software development (Dev) and IT operations (Ops) teams. The goal of DevOps is to deliver software and services at a faster pace than more traditional software development approaches—and with higher quality and reliability. In theory, this is achieved by striking a balancing act combining the right people with the right processes and tools—with a focus on automating and streamlining the software delivery lifecycle. When done right, incorporating database management into DevOps practices is a remarkably effective way of ensuring data and applications run as efficiently and smoothly for as long as possible.
Unfortunately, the complex nature of acquiring, storing, and accessing masses of business data means companies often struggle to bring their database requirements into their existing DevOps framework. Part of the reason for this struggle is because DevOps is almost always managed by Dev teams without the input of database experts. Another part of the struggle is due to the complex nature of managing data itself. Even businesses where DevOps methodology is openly embraced tend to neglect or ignore the database part of the development cycle. Without the right database monitoring software in place, for many companies the data layer is a kind of blind spot in the development landscape.
Database observability solutions serve as an X-ray machine into database systems—allowing data professionals to quickly understand the root cause of issues and to proactively optimize database performance going forward. Once your databases are under careful observation, incorporating the database component into your DevOps cycle becomes a lot easier. A good database observability tool will provide a unified view of database health and performance across servers, while helping to identify and troubleshoot performance bottlenecks and scalability issues.
DevOps Improves with Database Observability
If your IT professionals are considering incorporating the data layer into existing DevOps practices, or if your data management teams are looking for ways to ensure the continued health of your databases by adopting a DevOps approach to data management, here are the top six benefits to look forward to:
Database Observability for the Win
By adopting a DevOps approach to database management, IT operations, DevOps teams, and DBAs can help ensure their database resources reap the benefits typically associated with DevOps. For example, they gain the ability to fully analyze database performance and effectively address critical business challenges. By implementing a database observability solution alongside database DevOps, enterprises get direct visibility and performance insights into their applications. This simplifies the management and lifecycle of even the most complex databases.
Ultimately, taking a DevOps approach to database management enables teams to ship better code by understanding query performance before and after deployments. Organizations that successfully use database observability and a DevOps framework together can cater to diverse and growing business needs. This approach empowers organizations to design robust new database architectures, fulfill performance requirements, and drive successful digital transformation and modernization initiatives.
