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DevOps Trends for 2023

April 18, 2023

DevOps Trends for 2023

DevOps is a software development approach that emphasizes collaboration and communication between software developers and IT operations professionals. The goal of DevOps is to facilitate the rapid development and deployment of high-quality software through the use of automation, continuous integration, continuous delivery, and agile methodologies.

DevOps Trends for 2023

Orchestration via Kubernetes means managing containerized applications at scale, providing automated deployment, scaling, and managing the application containers across clusters of hosts. This can improve the reliability and scalability of software applications. Kubernetes is only one of the many tools that can be used; other tools that serve similar needs are Docker Swarm and AWS Fargate.

Serverless architecture means running applications without the need of managing the underlying infrastructure. This can simplify application development and deployment as developers can focus on writing code instead of managing servers. At the DevOps level, publishing can be an easy process when working with few features, but as the ecosystem grows, one can be faced with limitations from cloud providers as well as the complexity of publishing multiple serverless services. Serverless framework and its configuration files such as serverless-compose are tools that hello facilitate these complex processes.

Managing multiple projects is a critical aspect of DevOps, as organizations often have multiple software projects that need to be developed and deployed simultaneously. Effective project management tools and processes can help ensure that projects are delivered on time and meet the needs of stakeholders. Terraform is a tool for managing Infrastructure as Code, which allows developers to define infrastructure resources declaratively. This can help ensure that the infrastructure is consistent and reproducible across environments, improving the reliability of software deployments.

Multi-cloud support involves developing and deploying applications on multiple cloud platforms, such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. This can provide greater flexibility and redundancy for software applications.

AI in the future of DevOps is likely to play an increasingly important role in automating and optimizing various aspects of the software development and deployment process. This could include the use of machine learning algorithms to identify and diagnose problems in software applications or to automate tasks such as testing and deployment. Ultimately, the goal of AI in DevOps is to improve the efficiency, reliability, and quality of software development and deployment, while reducing the time and effort required by developers and operations professionals.

Q&A from our Engineering Managers: Pablo Rodriguez and Johnn Castro

How do you think container management has evolved with tools such as Kubernetes and what has been its role in microservices orchestration?

Johnn: Kubernetes can orchestrate microservices in a much better way. Before we had to deploy in different parts of each service and then communicate them. You had to stay there with only one provider or spend a lot of money to move it to a different one. By having Kubernetes now with microservices, they all use the same nodes in a fast way to connect between them and when you have the infrastructure set up in Kubernetes you can deploy in Google Cloud, in Azure, in different clouds.

I think it’s getting revolutionized. However, when you come to a Kubernetes project, there are different standards, so, there’s still a little bit more standardization to go.

Pablo: One of the reasons why organizations and companies are being urged to use Kubernetes is because it allows you to scale automatically without so much effort. For example, with Docker when deploying, you had to do container management and you had to do it with each of those containers individually. Kubernetes comes along and says: “I’m going to manage it and I’m going to give you the possibility to do scalability, to do automation, and to connect through plugins for login monitoring” and a lot of other things.

However, there are many things in development that do not fully require Kubernetes and it is not for all types of projects because it has a very high learning curve. I think a DevOps Engineer has to know when to use it and when not to use it.

How have you handled projects where complexity is growing?

Pablo: When it happens, my recommendation is to divide the project into small parts. Work a little bit with microservices (but in an oriented and well-thought way) where it is known that it can be solved with a Lambda. Another option is to handle it with a CD/CI, with automated pipelines to make the releases directly to the Lambdas. I have also been investigating configuration tools like Ansible where you can use Infrastructure as Code or infrastructure as a service to manage the configuration of the Lambdas and not have to configure it manually. And finally, a very important part is documentation.

So, I recommend managing the versions, breaking them down into small parts, using a configuration service like Ansible, and at the end documenting it.

Johnn: I believe that serverless is an alternative that greatly helps the development of the industry and makes it easier for developers to be able to develop compared to having to build a whole Kubernetes cluster. It’s amazing how serverless can improve efficiency. However, you have to be careful with serverless, in all clouds, because the sizes cannot be very large and it is one of the limitations that it has. Although there are already some solutions, for example, in AWS there are layers that are static code and it even helps make the code much more efficient because you don’t have to build the entire Lambda from scratch.

What practices are essential to work with on projects where we have to have something in AWS and something in Google Cloud, and how do you handle it in a robust way?

Pablo: I think that Infrastructure as Code is a very important part of DevOps right now. I would say that a DevOps engineer needs to know at least one of the Infrastructure as Code technologies, be it Terraform, CloudFormation, Ansible, or any other.

Terraform has the main advantage in that it is agnostic of any platform; it can work with AWS as well as Azure, Google, or any other provider. And it is also a disadvantage because it is open source, so we have the limitation that if, for example, AWS releases a new service or it puts in a new configuration, it may not arrive at the same time in Terraform as in AWS. On the other hand, we have the great advantage that, by working with different providers, we can then combine the entire architecture of the application in a single technological system. Also, we can create code from scratch or also import code into Terraform, so you can basically put the entire architecture in one place regardless of the providers you have.

Johnn: Besides Terraform, I’m also currently using Pulumi, where I can work with JavaScript, Python, and TypeScript. It is different and super easy; it also has an autocomplete option which helps a lot. For me, having these tools helps to reuse a lot of the code and to know exactly what is going on in the project. Also, I really like to use AWS couldformation.

Focusing on the topic of DevOps, have you heard of the topic of predictive analysis?

Pablo: I’ve heard that we can run artificial intelligence (highly trained models) on our architecture and it can tell us exactly what our architecture can fail with or what our application can fail with. As a concept, it seems impressive to me and possibly a game changer for the entire DevOps part. And, I think this type of analysis technology would come to give us a magnificent boost. I haven’t had a chance to work with any of them, but it’s definitely something I think is going to change the form in which we create architectures. It seems to me that what is coming is impressive and a very promising future. But, in my opinion, we do have to see it as a tool, as something with which we can accompany the growth, and not as a threat.

Johnn: I think it’s incredible. It’s going to make us better developers, better DevOps, and better at documenting projects, and it’s helping us learn even faster. In fact, there is already an AWS service that uses artificial intelligence to detect security problems, for example, to know what is happening. what is a user doing, and you can also see the malicious activity. From my point of view, I feel that it is not going to take away our jobs. The answers it gives are not entirely true or entirely correct, you have to review them. So a person without knowledge will not know what is wrong, you will always need the knowledge of the developer or engineer to audit it. So, I think that it is not going to replace us and rather it is going to help us.

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