Agile experimentation is the new norm in software development. Organizations want to release the best version of their products as soon as possible. DevOps and the principles of continuous integration and continuous delivery/deployment (CI/CD) help you do this by balancing the need to move fast with the need for quality, security, and compliance. DevOps allows you to run with the hare and hunt with the hounds.
DevOps and CI/CD have been widely adopted to accelerate software development. Should you also extend these practices to your data pipeline? If you have implemented DevOps for software development, then it’s only natural to apply the same approach to your data pipeline for data integration, because your organization has already adapted to the cultural change DevOps requires. Here are 10 reasons why it’s a good idea.
Reduce integration issues: Continuous integration and delivery efforts need better approaches to data integration and data management. By automating testing at each level, you can drastically reduce integration issues. It starts with unit testing, but you have the flexibility to include QA, performance, acceptance testing, and others depending on how critical the job is.
Deliver value faster and at scale: Data processing can benefit from AI-powered automation that ensures better quality and faster release cycles. CI/CD not only helps you automate your data pipeline deployment and but also systematically store reusable components that make your team more productive.
Meet enterprise-level SLAs: With automated testing at every step, very few bugs escape to production, bringing down your error rate and improving SLAs. DevOps methodology on one hand allows any data engineer to modify the data pipeline and on the other hand it ensures only the quality jobs pass through the drill.
Data artifact lifecycle management for quality and reusability: When you treat data pipelines as a product, you implement stages of software development lifecycle like requirement analysis, design, development and testing, implementation, documentation, and evaluation. This improves the quality of the code. A continuous feedback mechanism helps data engineers and the DevOps team to optimize the data pipelines for performance and for speed. As you create and update objects like mappings, task flows and other data artifacts, they get documented and can be reused for no-code no-build data integration activities later. DevOps acts as guardrail and makes it easier to test, release and reuse.
Seamless collaboration: One way DevOps speeds up releases is by enabling teams to work in parallel. With check-in and check-out options for code, multiple team members can work on the same objects without any conflict. The automated feedback mechanism eliminates friction between different personas like data engineers, integrators, and operators. With real-time feedback the developers can iterate faster and with automation, teams can easily optimize operational overheads, making it a win-win for all!
Version control: Tracking versions encourage transparency and ownership. It enables your data engineers to check in and check out objects without worrying about who else is working on it and other dependencies.
Standardization: DevOps drives standardization in terms of process, toolchain and the framework in general. Using loosely coupled modular systems or applications as building blocks makes it easier to adapt to future technology or process changes. Say tomorrow you want to add another layer of testing to optimize your data pipeline. With DevOps methodology you can easily provision that, whereas with hand coding and erratic customization you would face more resistance to the desired change. A standard process across your organization helps to track and adapt to the shifting landscape.
Enable experimentation: DevOps encourages agile experimentation as you can always roll back to the previous version if the new version does not work out. The developers can try new technologies or tasks and the gatekeepers will ensure that the quality doesn’t drop on the way.
Monitoring: With an automated alert and response system it becomes easier to troubleshoot and monitor CI/CD pipelines. Developers can quickly rectify if there is a break or issue in the workflow without bothering the operations team. Role-based privilege and permission ensures reliability and security of the pipeline.
Getting ready for DataOps, MLOps, and AIOps: Depending on the data maturity level of your company you can take your DevOps knowledge and customize it for data products, machine learning models, and artificial intelligence projects in the future.
Let’s take the example of our customer Guy Carpenter’s DevOps approach in a hybrid cloud landscape. They streamlined and automated data processes as they moved through multiple release stages. In the development stage, they write the task and do unit testing. Once they commit, the code gets into the system integration testing environment. Then it passes through the QA testing phase, pre-production performance or user acceptance tests, and then finally into production. The whole process is so automated that it takes care of itself, bringing agility, productivity, and efficiency to the business.
Informatica cloud-native data integration solutions provide out-of-the box CI/CD capabilities. They enable you to break down silos across development, operations, and security to deliver a consistent experience across the development lifecycle.
Watch this video to learn more.