1. Chaos Engineering Tools

The Chaos Engineering methodology has become a necessary component to ensure the stability and reliability of systems. Its essence is to test the environment through controlled experiments that simulate real scenarios of system failure. In this way, vulnerabilities and potential problems can be detected and eliminated before they occur, which means that serious consequences and losses can be avoided.

From my own experience, I can say that it would be a good practice to introduce regular audits using Chaos Engineering. Of course, today quite a lot of tools have already been created for conducting such tests, so everyone can choose what suits their specific needs. I offer the most popular, in my opinion, examples:

1. ChaosIQ allows engineers to run controlled experiments on a variety of platforms, including the most popular cloud providers and K8s, to test the resilience of the system to various types of failures. It is also possible to integrate it into popular CI/CD providers: Jenkins, Travis, CircleCI or GitHub Action.
2. Gremlin also helps developers create and run robustness tests in real-world environments.

2. GitOps Tools

GitOps is becoming an increasingly popular approach to managing infrastructure and deployments in the cloud environment. GitOps tools help automate the deployment, monitoring, and management of infrastructure through Git.

GitOps is a set of principles and practices that will help you take your automated deployment process to the next level. This is achieved by keeping the current state of the environment (K8s) in Git, and by continuously synchronizing Git and the working environment. The following tools are most often used for this:

1. FluxCD allows you to describe the state of the infrastructure in Git and automatically synchronize it with the actual state.
2. ArgoCD provides version control and automatic deployment of applications on Kubernetes infrastructure.

3. Kubernetes Security Tools

As Kubernetes has grown in popularity, so has the need for security tools specifically designed for the platform that detect, monitor, and remediate potential threats.

Kubernetes provides us with the ability to use native tools to organize security in the cluster, but from my own experience, I will say that their functions are sometimes not enough. In this case, tools that extend and improve the experience of implementing security practices help, for example:

1. Falco  is a threat detection system specifically designed for container environments that can detect anomalous activity and security incidents in real time.
2. Aqua Security helps ensure compliance with safety standards and regulatory requirements.

4. Serverless Monitoring and Debugging Tools

I suggest to start by defining the concept of serverless. What is it and why is it used?

Serverless is a feature typically provided by cloud providers. Thanks to it, you no longer need to run your own facilities (servers) or use the cloud provider’s servers directly (EC2 instances). Instead, it is possible to use services based on serverless.

For example, using AWS Fargate instead of AWS ECS eliminates the need to think about which servers to host containers on. AWS (or another cloud platform) will do this for you.

That is, AWS will place the capacity on its own servers and will independently deal with their support, which takes away some of your responsibilities, and also makes it possible to pay for services according to the pay-as-you-go scheme.

With the use of serverless, the need for additional tools that allow you to identify and solve performance problems and shortcomings of serverless-based applications has increased:

1. AWS X-Ray monitors software running on AWS, including serverless applications.
2. Datadog Serverless provides advanced tools for monitoring and analyzing serverless solutions.

5. Continuous Compliance Tools

I, like most engineers on projects, regularly face the task of complying with the norms and requirements of certain standards that usually relate to data security and infrastructure. In order to avoid deviations from these requirements, we need to automate part of the workflow.

For automation, we can use tools pre-configured exactly for our requirements, and also implement these tools in CI/CD in such a way that the infrastructure code (this is most relevant for DevOps engineers) will be checked automatically every time after changes or additions.

Therefore, by detecting certain inconsistencies with security rules, we will be able to detect the problem in time and prevent it from entering the working environment.

Below is an example of several tools that will help you work with requirements and standards:

1. Terraform Compliance allows you to check infrastructure configurations for compliance with internal security rules and regulatory standards.
2. Chef Compliance also provides automated verification of compliance of your infrastructure with security standards and organizational policies.

6. Observability Platforms

System monitoring is a well-known topic, however, in my opinion, its relevance and importance cannot be overestimated. Monitoring services provide collection, logging and visualization of metrics for all components of the software. Below are some examples of popular monitoring solutions:

1. Grafana  is a powerful data visualization tool that can integrate with various data sources and allows you to create a variety of dashboards.
2. Prometheus  is a monitoring system that is perfect for container environments like Kubernetes.
3. Datadog  is a more comprehensive tool for monitoring, visualizing and storing metrics, which stands out from the competition with a very wide range of capabilities and, as you can easily guess, a higher cost.

7. AI/ML Ops Tools

In my experience, there has recently been a growing need for tools specifically designed to manage Machine Learning (or ML) models and deploy them in a prod environment. AI/ML Ops tools help automate model training, experimentation, and monitoring processes. Among the best are:

1. MLflow  is an open source tool for managing the process of developing machine learning models from the initial stage to implementation in a product.
2. Kubeflow  is an open source platform for developing, training and deploying machine learning models on Kubernetes.

8. Infrastructure as Code Security

It’s no secret that infrastructure security is critical in the world of cloud technologies. Of course, this directly applies to our work with you.

Most cloud projects require DevOps engineers to describe the infrastructure in code. Yes, I consider this practice appropriate and even mandatory, but infrastructure code can hardly be called high-quality if it does not take into account security best practices. This is where infrastructure-as-code security tools come in handy.

Also, from my own experience, it is a good practice to create separate pipelines for your infrastructure code. It is in these pipelines that you will be able to use the tools that I will list below.

In addition, you can use, for example, the software to calculate the approximate price of the infrastructure or to check for compliance with certain regulations, which I have already mentioned in this article.

It is thanks to a successful combination of solutions and methods that we will achieve a full verification of the infrastructure code before launching it into the environment and will be able to react in time and prevent potential problems.

Examples of tools this time include:

1. Checkov analyzes infrastructure templates (such as Terraform or CloudFormation) for potential security issues and provides recommendations for remediation.
2. TerraScan  is a standalone security tool for Terraform with a similar feature set.

9. Service Mesh Tools

Service Mesh provides an additional layer of management of network interactions between services in microservice architectures, helping to improve security, observability, and management of traffic between services.

Among the popular examples of using Service Mesh systems, I can mention the implementation of canary deployment in Kubernetes. As for specific solutions, I recommend the following:

1. Istio provides traffic management, observability, security, and other features for microservice architectures.
2. Linkerd provides low latency and high performance for managing network interactions between services.

10. Automated Incident Response Tools

Automated incident response tools help teams respond quickly to security issues or system failures.

I suggest analyzing a case when you need to react to an incident — for example, the failure of a website. You have up to 10 minutes to react, regardless of the day and time of day, that is, you have to be ready 24/7. In this case, you need to set up some sort of automated system that will notify certain users when a problem occurs.

PagerDuty and  Opsgenie tools can help with 24/7 monitoring – they will wait for a certain event/trigger and report the incident in a way that’s convenient for you, such as SMS or a Slack message. Also, if you need to expand the logic of such processes, you can use workflow automation tools, for example, n8n.

Conclusion

I understand that all the tools I wrote about cannot be used in one project, because they have different requirements and approaches. However, you can bring a few tools from this list to your current project, try a few others in the next, and in this way you can develop and expand your toolkit and thereby become a more valuable specialist in the market.