As more and more enterprises adopt the agile Devops operations, the focus is mainly on agile deliverability. To ensure a smoother and faster deliverability especially with continuous inflow of big data requires customized solutions. Continuous integration and continuous delivery are two such deliverability modules that offer agile delivery allowing the organizations to maintain faster pace of agile operations even with an inflow of huge volume of continuous big data.
Continuous integration and continuous delivery is in fact tailor made to ensure big data solutionsare tracked more efficiently while allowing the organization to speed up and coordinate everything from debugging to final future product or software release. Here is an insight into how continuous integration is helping enterprises give more power to their agile operations while making itself ready for big data solutions deliverability.
Understanding the world of continuous integration
Continuous Integration more commonly known as CI is a software development cycle in which codes from various team members are integrated together on a continuous basis. Since majority of software development work is done independently by various individuals and teams, having a common platform as a shared repository allows each such team or individual to integrate their code preferably on a daily basis or as per a pre fixed timeline.
Each such code check-in into the shared repository is then verified by an automated buildup ensuring that problems, errors, and bugs are all detected at an early stage. Any conflicts between the various coding teams are also detected and removed at an early stage thanks to continuous delivery shared repository. Any divergence from code strategy, duplication of works, and conflicts are all ruled out ensuring a smoother integration and deployment to the final product.
In the future where data integration is rising owing to use of multiple devices and big data analytics, having a continuous integration mechanism allows enterprises to remain agile irrespective of the huge volume of data to be tracked, assimilated, and then decoded to ensure future product deliverability. Continuous integration works best when streamlined collectively with big data solutions.
Core principles and working overview of continuous integration (CI)
The essence of having a continuous integration or CI process is to have a code repository that is capable of automating the buildup process. When codes are entered into the repository in self tests the code in isolation as well as in a clone of the production environment. The results of such testing are transparent and seen by every team members leading to early detection of any bugs or changes required. Continuous integration is the first step towards an automatic deployment especially with big data solutions.
For the entire positive the implementation of continuous integration is not as complicated as it is often perceived. Developers input their code in their private terminals each day. The code is then shared with the shared repository while it gets monitored by a dedicated CI server. Continuous integration tools and automated build runs integration tests while CI server assigns a build tag to each version. Any failure to run smoothly gets reported to the development team. Such a continuous integration process runs for the entire course till the project is completed or desired results are achieved.
The thin line between continuous integration and continuous delivery
Continuous Integration and Continuous Deployment (CD) are often spoken about in the same breath. While there are many common occurrences between the two, CI and CD have a fundamental difference. While CI is a process that is focused on quality of code and testing the coding process, CD is more focused on offering a final master functionality. For example, CI is like testing each individual component in a multiple supply chain while CD focuses on the final product deliverability assuring each such assembled component and the overall product runs smoothly as per the designed functionality. CD offers a final automated push to production while CI works more closely with various core functions and big data solutions to assure each component works as desired.
Conclusion: With big data solutions becoming an integral part of enterprise functionality, CI deliverability tools are making use they offer big data solution inbuilt in their design functions. There are various CI tools or build servers available today handling heavy daily workflows making them capable of handling big data solutions for the enterprise.