Today we cannot imagine software development and deployment processes without applying the DevOps methodology. This methodology is focused on establishing the interaction between developers and system administrators in a company. Thereby, the DevOps practice creates a smooth development circle and speeds up the delivery of a software product.
The era of the DevOps trend originated in 2008 when Patrick Debois attended the Agile conference and created the Agile Systems Administration Group with Andrew Shafer. Since that time, the DevOps progressive tools have been actively used in most companies where it is required to increase the efficiency of software development and operation.
Even though the DevOps principles are becoming a major part of the software delivery process, there is still a challenge in uniting database development with the DevOps process.
Database DevOps challenges and possible future changes
The Database DevOps philosophy aims to improve and simplify database management by automating some facets of the database lifecycle. This process is highly effective but at the same time it is complicated as you may face the following issues:
Continuous deployment
In the DevOps environment, continuous deployment of software is achieved by using a CI/CD pipeline. In case there are some new changes in the application code, these new changes just replace the old ones. Unfortunately, this approach doesn’t work with databases. For instance, to add schema changes, there must be a strategy for migrating old data to a new structure. It makes the continuous deployment of databases much more difficult.
Scalability
An application is highly scalable in the microservice architecture. As for databases, their performance gets much lower if a database scales in a size.
Rollback
The database management process excludes a possibility to get back and fix a database source code, test it, and then redeploy it, if you have some issues in the production environment. This might cause a breakdown in the work of a database.
Incompatibility between relational databases and the microservice architecture:
Microservices have a non-shared architecture. The reason for this is to remove any dependencies from other microservices and to protect them from being affected by a failed microservice.
As you can see, it is not so simple and seamless to automate database development and deployment as applications. But the world of IT technologies is progressing very fast and we are sure that within a few years, we will see great changes in this process, which will also cover all existing needs. We assume that these changes would affect:
- Continuous delivery: a database pipeline would be integrated into an application pipeline even if a couple of applications or microservices use a database. Thus, the database changes would be synchronized with the ones of the application.
- Rational cloud-native database solutions: the solutions would be integrated with DevOps CI/CD pipelines through APIs and cloud-native infrastructure.
- Equal database deployment: there would be an ability to deploy databases to cloud environments, Docker containers, Kubernetes, and all operating systems in the same way.
- Scalability: databases would scale dynamically based on changes in workloads and users’ demands.
- Source control: we would like to believe that a database code would be maintained through version control. It would allow reviewing databases changes just like applications changes.
The biggest challenge is that nobody can tell exactly how much time it will take until we are able to manage databases without restrictions from the DevOps perspective. So, how to overcome all challenges and automate a database deployment right now? That’s the question we have the answer to. DevOps Automation for SQL Server can really improve the Database DevOps practices of your team. The tool is included both in the SQL Tools pack and in the dbForge Studio for SQL Server.
With DevOps Automation for SQL Server and dbForge Studio for SQL Server, database lifecycle goes through five stages displayed on the image below.
At the Development stage, database developers modify a database schema. The Version control stage is about committing the changes to the version control system, for example, to Git. After that the Build stage gets started—the database is compiled from .sql files on the SQL Server. At this stage, you get the desired database.
It isn’t the end of the process, here comes the Unit test stage. This stage is essential as it helps to ensure that the database still operates as expected after the changes have been added. The testing is performed by SQL unit tests.
The Publish stage brings the CI process to a logical conclusion. At this stage, you publish the NuGet package or ZIP to be able to share it. Optionally, you can generate the database documentation and include it in the package. Finally, the NuGet package or ZIP is published in the NuGet repository or added to a separate folder.
Conclusion
The DevOps principles are a popular trend in many companies. More and more developers strive to automate manual operations in the deployment process of a software product. Minimizing human intervention in the product delivery allows releasing faster and with fewer failures.
But as we can see, things don’t look so easy when it comes to the deployment of databases. There are still some blockers in database development process, most of which can be removed by the Devart product. To take your databases deployment to a new level, try 30-day trial versions of dbForge Studio for SQL Server and DevOps Automation for SQL Server.
