Database Access & DevOps – The Evolution, Challenges & Solutions

Database Access & DevOps – The Evolution, Challenges & Solutions

February 27, 2024

As software development has evolved, so have the demands for data environments. Initially, application architecture was simple, with the use of small, on-premise databases that needed to be replicated into lower environments only once every few months for major software updates. However, as agile software development and “shift left” have become the norm, the demand for database copies has increased, as has the frequency of the data refreshes required.Furthermore, data privacy regulations and cybersecurity concerns mean that access to sensitive data needs to be limited, and the data itself needs to be protected with various methodologies.The traditional way to get a privacy-safe copy of the database is that the data consumers request a copy via an ITSM ticketing system, then they wait for the DBA team to make time to do it. The DBAs – whose primary job is to maintain the production databases to ensure business can carry on as usual – don’t always have the bandwidth to handle the request in a timely manner. This leads to slow test cycles with agile teams working on poor quality data, resulting in more difficult-to-find software flaws being created.It’s even more of a challenge because most enterprises have a plethora of applications delivered across different environments, multiple data centers, and multiple clouds. One database may need to be used by multiple teams across multiple environments. Every time one of the teams changes the data, it can directly affect the progress of one of the other teams, creating unnecessary conflict, reducing efficiency, and may even lead to more errors.

To overcome database access challenges during software development and to create ideal test environments, it is critical to speed the delivery of privacy-safe test database copies to achieve faster time to market without compromising on the quality or security of the data.

Database Virtualization: a Better Approach to Test Data Management (TDM)

To meet the database copy needs of DevOps, QA, and agile development teams, a holistic approach to test data management (TDM) is required. It’s critical to balance the need for speedy test database provisioning with data privacy compliance and storage costs – which can get out of control if the database copy volume is high.

Modern TDM platforms that incorporate technologies like database virtualization and AI-based automated masking, all wrapped in a user-friendly UI and API are ideal. Furthermore, the ideal TDM platform provides a self-service process that eliminates the reliance on DBAs and other IT admins.

Using virtual databases allows for quick delivery of database copies while consuming negligible storage, with automated provisioning increasing DevOps release velocity. More copies of environments can be created quickly, and more functional testing can be done, reducing the volume of defects making it into production.

Furthermore, virtualized databases dramatically reduce cloud storage costs, as they are stored in one place and accessible in many others. Virtualization also acts as a data container, allowing it to be used across multiple cloud environments wherever storage is less expensive.

Teams should have an automated process in place to ensure that specific databases are masked, thus keeping the company data secure and privacy compliant. Automated, on-the-fly masking should be integrated directly into the build or refresh of test databases to prevent exposure of sensitive data.

The Future of Database Access in DevOps

As companies move towards the use of cloud environments and more distributed systems, the need for efficient and secure test data management solutions will only continue to grow. Companies must invest in TDM platforms that can provide reliable and efficient delivery of test data, while also ensuring data privacy compliance and minimizing storage costs. These TDM solutions will accelerate the ability to create more accurate and representative test data environments, leading to better testing outcomes, fewer software flaws, and faster time to market.