Since the term’s inception in the late 2000s, “DevOps” is by far the most common approach to product development. During the last 15 years, DevOps processes have evolved to new speeds and scales, with the latest iteration including AI-assisted coding and platform engineering.
Simultaneously, certain companies are struggling to master database operations. As a result, many organizations have data that sits in silos and/or can be hard to track down. What today’s businesses need is a way to combine DevOps and data management, ultimately creating a Database DevOps framework.
The Relationship, or Lack Thereof, Between DevOps and Database Operations
The goal of almost every DevOps framework is to streamline the production and delivery of each software product. Unfortunately, the acquisition, storage, and accessibility of large amounts of data can become complex and create a roadblock to speedy software development processes. As a result, teams tend to ignore the integration of database operations into their developer workflows.
These aforementioned complexities usually materialize because DevOps processes lie solely at the feet of developer teams—with little to no input from requisite database experts. In fact, certain organizations lack the talent to contribute to this database expertise.
This creates gaps in the ability to manage data. As a result, developers and other personnel in the product supply chain have little visibility into the data necessary for product development.
Poor data management doesn’t only affect companies during software development. Even after a product is released, a lack of quality data operations can cause applications to run slowly, create performance bottlenecks, and prevent the ability to scale software tools.
The key to better software development and better database management is the removal of silos between the two functions. The best way for this to happen is with database observability.
Observability: The Bridge Between DevOps and Your Database
With the right database observability solutions, database personnel can see the root cause of poor data quality, application issues, and suboptimal database performance. Once database experts can view the systems in their remit, they’ll be better prepared to offer help to software development personnel and combine DevOps practices and workflows into data management. From there, organizations will experience multiple benefits such as these:
- Better agility and flexibility—Similar to the automation in coding and software development of a new product, data management teams will be able to automate database provisioning, configuration, and deployment. This will allow organizations to improve their data infrastructure more easily or implement new software features.
- Mitigated risk and downtime—Database operators will be able to leverage CI/CD (continuous integration and continuous delivery/continuous deployment) to reduce downtime and human error. Moreover, with a comprehensive observability tool, they can test adjustments to software deployments with minimal risk of data loss or service interruptions.
- Improved team collaboration—One of the best benefits of proper database observability is improved teamwork. Developers, operations teams, and database administrators will have visibility into each other’s processes and work environments. There is less of a chance that one team makes changes to coding or structure without the other teams knowing. This creates less confusion about how each workflow affects another and allows a comprehensive solution to application problems—whether they are at the development or delivery stage.
- Increased security—The combination of database and DevOps should also include several security checks and parameters as the database grows. With proper security controls, companies can effectively implement data protection and fortify privacy measures.
Partnering Database Management With DevOps
Effectively combining database management and DevOps will be the only way for organizations to handle the massive amounts of data necessary for the future of innovation. With analysts predicting continued investments in AI technology, databases must be prepared to handle terabytes—if not petabytes—of data as they implement new solutions. Through a comprehensive observability approach, organizations will be able to create a Database
DevOps function that creates and ships better code, fortifies IT infrastructure performance, and maintains the data quality necessary to power modern business initiatives.