When committing code in the repository, the system enforces the user to write the changes. The ticket, id proof, and other relevant details are logged into the repository. The project management tool ticket could be created with a log message so every detail of code is logged in systematically.

This helps in getting a calendar versioning to indicate when the build is done. As soon as the commit is done, the CI (Continuous Integration) or build happens. So every time you build automatic calendar versioning will be given for the build. This helps to look back in the log and check for a stable build.

When the code is committed in the repository and once the build is done, the system will go and put a tag on the dashboard. The feature of automatic comments being integrated into the project management tool gives a clear idea to the team whether or not the code was used.

Jenkins is a build automation tool that helps define whether the code should be built post-commit or periodic build. This tool enables the DevOps team to be in complete control of when the commit should go for a build. The build script needs to be modified to comply with automation.

The tools like Cobertura help to test the code. There are automation scripts and Cobertura will tell the lines of codes that got executed. For example, when the developer writes 100 lines of code and 90 lines of code are already tested then the build is good, but if it is just 10 out of 100 then it did not test enough. If the code breaks some other code then the execution stops.

In order to install an application in the monolithic architecture, it was required to install SQL, SQL 2.2, Windows 7.8, and so on. As we replaced the monolithic with Microservices, the docker image contains all the required technology. With this image, there is no pain of installing dependencies and these are preconfigured. As a result, the docker image is ready to execute any time.

Unit test scripts were automated and this helped in adding more efficiency and speed in the test process.

Sonar tool helps understand the code coverage. Sonar will tell whether or not the coding standards were followed.

The implementation of logical progression is one of the very crucial and important aspects to help in production. When a docker image is good, it is unit tested, system tested, again unit tested, and after the rigorous progression, approval, and authentication the docker image goes to production. If someone else builds a docker image and directly puts it in the production…it will just not work. This also ensures that no one can tamper with the image. If a docker image is directly put into a production environment then it will fail.

As an aftermarket auto spare parts distributor, there are times when our client expects an increased volume of requests. In the monolithic architecture, we had to prepare months in advance to handle the demand. However, linking the docker image to the container services empowered the client to scale up within no time. Rather than planning months in advance, the docker image also enabled our client to autoscale whenever there is a boom in requests.

Now with the centralized log management, it is easier for the client to identify errors with great ease. For example, if there are 50 systems, the 50 logs are pushed into a common pool and a view engine enables the client to identify the error logs and machine. Bring down the faulty container has become very easy for the client.