DevOps Database Strategies and Challenges: Automation, Scalability, and More!
DevOps enables development and operations teams to collaboratively produce software products. DevOps teams work together, using a continuous feedback loop and automation to speed up the Software Development LifeCycle (SDLC). This agile workflow has helped reduce many backlogs, but often database tasks that are kept separate create a bottleneck.
To reduce database bottlenecks, you can add database tasks into your DevOps pipeline. However, you need to be aware of challenges like data persistence and tooling compatibility, and then apply the relevant strategy. In this article, you will learn about the merger between and database management, including a review of implementation challenges and strategies.
What Is DevOps?
DevOps is a software development methodology that incorporates agile practices and the use of a joined development and operations team. It is used to increase the efficiency of teams and to speed up the software development lifecycle (SDLC). This is possible because of the collaboration between development and operations members. When these teams combine, communication is easier, workflows better align, and bottlenecks are removed.
To implement DevOps effectively, you need to combine cultural changes with supportive tooling. This methodology does not allow teams to work in silos or phases. Instead, it requires visibility across the SDLC and consistent communication. It also typically involves adopting a variety of DevOps automation tools and processes. This automation helps standardize workflows and enables teams to work together more efficiently.
DevOps and Databases
The tight integration of applications and databases can help teams produce more stable and consistent builds. When DevOps is used, applications and databases can be developed and deployed through a single infrastructure. This can help ensure that integration is standardized and that communication between the two is consistent. It also helps ensure that when components are configured, the database matches what is required by the application and vice versa.
In a DevOps environment, database management and changes are performed in the same production line as applications. This helps ensure that when changes are made, application delivery is not impeded. This is in contrast to traditional development and deployment methods which operate in discrete steps.
Additionally, when DevOps practices are applied to database creation and management, smaller changes are made more frequently. These changes are made in response to feedback from application development and delivery processes. This ensures that changes are well matched to needs and do not create conflicts during development and delivery.
DevOps Database Challenges
While incorporating database management into your DevOps processes can grant significant benefits, it also comes with certain challenges. Transitioning from traditional models to a DevOps model often requires overcoming the following challenges.
Integration of tools
When DevOps was developed, databases were not part of the original vision. The tools and processes created to meet DevOps needs are best suited for application development and deployment, not database management.
Additionally, database tools and processes don’t always merge well with existing pipelines. These tools tend to be database and environment-specific which can make it difficult to incorporate into pipelines designed for flexibility.
You can’t simply make schema changes or transfer databases to a different environment without accounting for the data contained within. Instead, you must carefully consider how changes may affect data and ensure that data integrity is maintained. This is very different from application and deployment changes. These changes only require changing code and data persistence is not a concern.
To integrate database management into your DevOps processes, you need to include solutions to help with data persistence. This typically means incorporating tools that can export and import data, manage storage, and perform integrity checks after restoration.
Size and scalability
DevOps pipelines and the applications created with pipelines are designed to be highly scalable. This is especially true when applications are developed as microservices, with multiple instances running to increase availability. These components and products are only restricted by the size of the host resources.
This is often not the case with databases, however. Database performance and ease of management often decrease as a database scales larger. Schema changes take longer and data syncing is complicated when multiple application instances are attached.
DevOps Database Strategies
To manage the challenges of integrating databases into DevOps processes there are numerous strategies you can implement.
Loosen coupled architectures
Trying to tightly couple applications to a central database does not work well with DevOps, however, microservices do. Rather than trying to navigate the complexities of central databasing, you can consider creating microservices with individual databases. This method does create more databases to manage but can help restore the flexibility that is otherwise lost.
Focus on small, incremental changes
DevOps processes are based on incremental changes and frequent review. This method ensures that changes are easy to revert if needed and reduces the risk of wasted work. You should apply this same principle to your database management. Doing so helps ensure that issues are discovered before application deployment.
Also, when making changes, make sure to track any alterations made and if possible save a backup of the database before changes are made. This can help you revert to previous versions more easily if necessary.
A vital element of DevOps is the automation used to enable CI/CD. It allows teams to create policies and procedures defining how applications should be submitted, versioned, built, tested, and delivered. This ensures that products are consistent and that visibility is continuous.
This same sort of automation should be applied to database management. This includes permissions handling, change rollouts, and backups, and upgrades. As much as possible, try to automate processes through your CI/CD pipeline. Doing so helps ensure that all relevant development and deployment processes are centralized and consistent.
Use a staging database
Staging databases help you ensure that you are testing application functionality and performance in a realistic environment. These databases also provide a better way to test changes to the database itself before changes are made in production.
Make sure to keep your staging database as close to your production database as possible to ensure effective testing. However, keep in mind that it is generally a bad idea to store live data in your test environments. You want to keep your schema and configurations but make sure that any data included is anonymized or otherwise protected for compliance purposes.
DevOps can help reduce bottlenecks created by siloed database management. However, it requires a merger between DevOps and data management. Essentially, this methodology requires shuffling data management tasks and assigning responsibilities to your DevOps team.
Some have referred to this workflow as DataOps, while others call it DevOps for data management. Either way, it’s a completely new way of working with data. Workflow changes always come with a set of unique challenges. In this case, you can expect issues with tooling integration, data persistence, and size and scalability incompatibility.
To cope with DevOps database challenges, you can apply a number of strategies. For example, you can loosen coupled architectures, and you can focus on small, incremental changes. You can also use DevOps automation tooling to create an efficient process. To ensure testing reliability, you can stage a database. Keep your workflow dynamic, and adapt as needed.
Author Bio: Gilad David Maayan
Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Ixia, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.