DevOps is a new area where both the Development and Operations are intertwined together as a single organization. Cloud DevOps is a newer development area, the need for which had arisen for agile development, automated deployment, as well as for faster time to scale. DevOps on premises is different from Cloud DevOps as Cloud DevOps require both cloud-expertise as well as DevOps knowledge to master the development of the same. DevOps Practices in different clouds are different and holds great promise if the awareness to handle the DevOps in the cloud is there.
Requirements of Cloud DevOps
Cloud Expertise: Cloud is still considered a new technology although the cloud concept has been there for more than a decade. The tools required for DevOps from Agile tracking of development, Continuous integration with new builds, Continuous delivery of code to production, and Site Reliability Engineering consisting of monitoring the availability, performance, and fault management of Infra and applications, are different for different cloud service providers. A cloud DevOps engineer has the knowledge of complete cloud DevOps Tools chain specifically optimized to the cloud service provider.
Cloud Costing Model: Awareness of the cloud costing model is a must. The number of products by a cloud services provider is daunting. As an example, AWS has 169 products whereas GCP has 90 products. Many costs are hidden in nature and many of them must be discovered on the way. Therefore, right experts are necessary to make sure the cloud costs are optimized to the best of the ability.
Scaling: One of the facets of DevOps is automation and requirement for automation is varies according to cloud service providers. As an example, with AWS lot of third-party service providers are available to automate the operations whereas in GCP many operations are automated by default. Standardization and automation are necessary to scale the operations. Cloud-native development has become the order of the day and many open-source tools are used to scale the deployment speed. DevOps as code should be used to scale the pipelines.
Security and Compliance: Code Security is still an important aspect of developing the code on the cloud. Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) are necessary in the cloud. Security and compliance scaling happens more with automation. SAST check should be automatically done with every code check-in and DAST check should automatically be done with every build. Security is a continuous service and public cloud service providers are enabling DevSecOps as a new practice. Application security level checks are now reaching new levels which many security professionals have been asking for as well. The goal of the DevSecOps Practice is to introduce security earlier in the SDLC lifecycle. The Objective of the DevSecOps is to make business, tech, and security work together.
AI in DevOps Chain: DevOps throws a lot of data and it is important to have complete visibility of the entire DevOps chain. One can use the Data with AIOPS and get important inferences for actionable intelligence. Data on DevOps is important to optimize the complete process. A new approach of combining DevOps with AIOPs is being done by public cloud service providers. Many of the AI applications require DevOps by default as well. AI is more iterative. While AI can help with DevOps data the DevOps practice in AI can help with more actionable intelligence in anomaly detection, prediction, and natural language processing. All AI applications will have DevOps approach. Cloud offers AI ML tools and can be used as part of the DevOps tools chain for optimization.
While DevOps practice itself has delivered faster productivity with enterprises setting up CI and CD chain it is important to understand the cloud DevOps chain and use it effectively for business purposes. The migration from On-prem DevOps to Cloud DevOps should be carefully calibrated for maximum benefits at minimal cost.