According to the team at Verified Market Research, The DevOps Market size, valued at USD 7.01 Billion in 2021, is predicted to climb to about USD 51.18 Billion by 2030, growing at a CAGR of 24.7% from 2023 to 2030. Coupled with the recent advancements in AI, what does this mean for the near future of DevOps in 2023? Let’s have a look:
1. AI for DevOps: Embracing Automation
AI for DevOps is entirely about using technology to make development and operational tasks more efficient and automated than ever before. By using AI, teams can work faster by identifying and fixing problems more quickly, improving code quality, and optimizing application performance.
The predictions:
As AI for DevOps becomes more common in 2023 and beyond, here are the most common use cases we’ll observe:
1. AI-assisted incident management to automatically detect and resolve operational issues:
AI for DevOps will enhance the ability to automatically detect and resolve operational issues. With AI, teams can quickly diagnose and fix problems, which can help to reduce the amount of time it takes to get systems back up and running.
2. AI-powered code quality control to improve code quality with continuous monitoring
AI has the potential to significantly improve code quality by continuously monitoring code and identifying bugs and security vulnerabilities. By using AI-powered recommendations, teams can fix issues more quickly and make sure that code is up to standard before it is released.
3. AI-driven performance optimization to optimize application performance and reduce cost
Another benefit of AI for DevOps is the ability to optimize application performance and reduce costs. AI can be used to identify and fix performance issues, such as logging, CPU, or memory issues, and make sure that applications are running as efficiently as possible.
4. AI-assisted secure code review to improve security by building automated code reviews
Finally, AI can also be used to improve security by building automated code reviews into the development process. This can help to identify and fix security vulnerabilities at scale, giving teams more confidence in the code they are releasing.
2. Going Mainstream: The Rise and Rise of Serverless Computing
What is Serverless Computing, to begin with?
Serverless computing is a cloud-based computing model that allows developers to run their code without provisioning or managing servers. Instead of managing infrastructure, developers simply upload their code to a serverless platform, such as AWS Lambda or Azure Functions, and the platform takes care of scaling, patching, and managing the underlying servers.
This approach has several benefits for DevOps operations:
- For one, it eliminates the need for developers to manage servers, which frees them up to focus on writing code.
- Secondly, it allows for more efficient scaling, as the platform automatically scales up or down based on the number of requests.
- Lastly, it can significantly reduce costs, as developers only pay for the computing time they use, rather than having to provision and pay for servers all the time.
The prediction:
As serverless computing becomes more mainstream in 2023, experts predict that it will speed up code deployment further. Additionally, its cost and scalability benefits will make it an attractive option for many more businesses in the future.
3. GitOps: Lead in Automating and Controlling Infrastructure
Let’s begin with an overview of GitOps.
What is GitOps, and how does it help developers and IT teams?
GitOps is a development approach that utilizes Git as the central source of truth for managing the entire application delivery process, including infrastructure and deployments. This eases up the usage of Git and Git-based workflows by developers and IT Teams to manage and deploy their applications.
One essential benefit of GitOps is its ability to automate and control infrastructure. By managing the application delivery process through code and making it fully auditable, changes made to the infrastructure or application can be easily tracked and rolled back if necessary. Additionally, GitOps enables faster and more consistent deployments and, as its name explains, facilitates collaboration between development and operations teams.
Built on top of Kubernetes, an open-source container orchestration system, GitOps uses Kubernetes’ declarative configuration model and APIs to allow developers and IT teams to manage the entire application delivery process using Git and Git-based workflows.
The prediction:
Experts predict that GitOps will continue to rise in popularity and in the vital aspects of DevOps practices, providing a solution for automating and controlling infrastructure.
4. Security Takes Center Stage: The Emergence of DevSecOps
What is DevSecOps?
DevSecOps is an enhanced approach to DevOps that places a greater emphasis on security and observability. This involves incorporating security considerations and best practices throughout the entire software development, delivery, and operations process. Not only does it promote smooth collaboration and communication between development and operations teams, but it also helps to protect against external threats and breaches.
What does DevSecOps have in store for 2023?
The predictions:
- As the threat of cyberattacks and data breaches continues to grow, organizations are becoming more aware of the importance of security in their operations. DevSecOps helps by allowing them to incorporate security considerations and best practices throughout the entire software development, delivery, and operations process.
- DevSecOps promotes collaboration and communication between development and operations teams, which is becoming increasingly important with organizations moving towards more agile and DevOps-based development models. By incorporating security considerations into the development process, DevSecOps helps to ensure that security is not an afterthought and that vulnerabilities are identified and addressed early on.
- With the use of cloud-based services and APIs growing, the attack surface area of an application also increases. DevSecOps approach helps to secure those as well.
- As regulatory compliance requirements become more stringent, organizations are faced with the need to demonstrate that they have appropriate security measures in place. DevSecOps helps organizations meet these requirements by incorporating security considerations into their development and operations processes.
5. Big Data Meets DevOps: The Power of Analytical DevOps
What is Big Data?
Big Data is basically the large volume of structured and unstructured data that’s generated by various sources, including social media, sensors, and transactional systems. It is characterized by its high volume, high velocity, and high variety, which makes it difficult to process and analyze using traditional data management and processing tools.
Big Data processing and analysis are done using specialized tools and technologies like Hadoop, Spark, and NoSQL databases. These are specifically designed to handle the volume, velocity, and variety of Big Data.
The predictions:
- As the volume of data being generated continues to grow in explosive numbers, data analytics will become increasingly important in DevOps. By integrating data analytics into the DevOps process, organizations can gain valuable insights to identify and solve key application-related issues.
- Aside from the more known tools for Big Data processing and analysis (for example, Hadoop and Spark), other technologies such as natural language processing (NLP) and deep learning will be increasingly used to work with data. These technologies will allow organizations to gain a deeper understanding of their customers, improve the performance of their applications, and identify potential issues before they grow into major problems.
- For developers, this means that they must learn to be more efficient in learning to collect, process and analyze data. This includes understanding the basics of big data technologies and other technologies such as NLP and deep learning. It also means that they need to be increasingly aware of the best practices for data management, such as data governance and data security.
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