kubectl foreach is a command-line tool that enables running kubectl commands on one or more contexts (clusters) in parallel. Users can match context names from kubeconfig using patterns such as exact names and regular expressions. The tool offers options for limiting parallel executions, disabling confirmation prompts, and replacing values in kubectl arguments with context names. kubectl foreach can be installed using Krew kubectl plugin manager and used to query pods and run commands on multiple contexts at the same time. The tool is not intended for deploying workloads to clusters or using programmatically yet.

https://github.com/ahmetb/kubectl-foreach

This article provides a deep dive into container file systems, specifically the use of OverlayFS in containers. It explains the need for container file systems to reduce data redundancy and save disk space, as well as how UnionFS mounts multiple directories together in one directory. The article also provides sample commands to illustrate how OverlayFS works and how Docker container uses it to divide container image files into multiple layers. Overall, this article is useful for those who want to understand the technical details of container file systems and how they work in containers.
https://medium.com/geekculture/k8s-container-file-system-ec26eda8b3ea

The article explains how to enable communication between microservices in a Kubernetes cluster, using various methods. The author starts by deploying a simple setup that simulates two pods communicating with each other. They then explore different methods to achieve communication between these pods, including using pod IPs directly, creating and using services, and communicating between services across namespaces. The author also explains how to use environment variables and fully-qualified DNS names to address services. The article is a useful reference for anyone working with Kubernetes microservices. https://dev.to/narasimha1997/communication-between-microservices-in-a-kubernetes-cluster-1n41

The article discusses the differences between Red Hat OpenShift and Kubernetes, two popular container orchestration management systems. While Kubernetes is an open-source container orchestration system developed by Google, OpenShift is a cloud-based Kubernetes container platform that offers consistent security, built-in monitoring, centralized policy management, and compatibility with Kubernetes container workloads. OpenShift contains all the native Kubernetes and Podman features and adds value through its own management functionality and DevOps tooling features. OpenShift offers stronger security features than native Kubernetes, but its stricter policies can make it harder to administer initially. While Kubernetes is more flexible, OpenShift is cheaper and offers enterprise-level support, making it more valuable to large organizations. The article provides details on deployment options, support, cost, releases and updates, networking, templates, image registry management, and integrated CI/CD for both systems.
https://itnext.io/openshift-vs-kubernetes-what-is-the-difference-cadee96497b7

ClusterWatch is an open-source product that offers a comprehensive solution for monitoring the health and performance of Kubernetes clusters. It provides real-time monitoring of the different architectural layers of a cluster, including nodes, pods, services, and other components. It also offers an automated configuration process for Prometheus and Grafana, allowing users to set up custom alerts for the metrics they want to track. With its interactive and responsive dashboard, ClusterWatch provides a single view point for users to access all the information they need about their cluster's health and performance. ClusterWatch is available on Github and is an ideal solution for organizations that want to ensure the health and performance of their Kubernetes-based applications.

https://medium.com/@shengli356/clusterwatch-official-launch-8c77885de58b

The publication is a comprehensive guide to learning about MLOps, including key concepts and skills to master. It outlines a step-by-step roadmap to follow to become an expert in MLOps and provides a selection of the best free learning resources available online. The roadmap includes the following steps: (1) Machine Learning Fundamentals, (2) Version Control for Machine Learning, (3) Continuous Integration & Continuous Delivery (CI/CD) Tools, (4) Infrastructure & Resource Management for Machine Learning, (5) Machine Learning Monitoring & Observability Tools, (6) Managing Machine Learning Projects & Pipelines, and (7) Machine Learning Security & Compliance Tools. The publication also includes links to various learning resources for each step.

https://pub.towardsai.net/ultimate-mlops-learning-roadmap-with-free-learning-resources-in-2023-3ba7664cb1e9

In this article, the author shares their experience of testing AWS Serverless Microservices and emphasizes the importance of quality assurance in software development. The author discusses various testing strategies such as pyramid testing, ice-cream cone testing, honeycomb testing, black box testing, and white box testing. The need for writing tests is illustrated with a story about a fast-paced development project that eventually faced issues due to insufficient testing.
https://dev.to/epilot/how-to-test-aws-serverless-microservices-the-proper-way-1f05

Criteo switched from monolithic applications to microservices, which introduced the challenge of monitoring hundreds of interacting applications. To address this, the "Central Monitoring" tool was created during a Hackathon. Central Monitoring is a web application that visualizes interactions between applications hosted in different data centers, providing an up-to-date view of the services architecture. It displays a graph of services and their statuses, along with quick access to documentation for further investigation.
https://medium.com/criteo-engineering/monitoring-microservices-central-monitoring-a-tool-for-a-global-view-of-things-80e46a810fd5

DevOps and cloud engineers do not necessarily need coding skills to excel in their roles. However, having coding skills can significantly enhance their capabilities and make them more competitive in the job market. DevOps engineers bridge the gap between developers and operations teams, while cloud engineers design, deploy, and maintain cloud-based infrastructure and services. Coding can help these professionals collaborate effectively with developers, communicate technical requirements, troubleshoot issues, and create custom workflows. Python and Go are two recommended programming languages for those looking to learn coding for DevOps and cloud engineering, with Python offering versatility and Go being popular for DevOps tools like Docker and Kubernetes.

https://kodekloud.com/blog/devops-cloud-coding/

Introduction to Platform Engineer https://medium.com/@chussenot/what-is-a-platform-engineer-e9f7f8413c4

Migrating from the cloud https://dev.37signals.com/bringing-our-apps-back-home/

Do you use Helm Dashboard https://levelup.gitconnected.com/introduction-to-helm-dashboard-dddf43e38cc2

Interesting experiment with ChatGPT and DevOps https://ghumare64.medium.com/i-tried-using-chatgpt-for-funn-as-a-devops-engineer-8239313269d4

Time to prepare a k8s interview https://manoj777.medium.com/top-7-kubernetes-interview-questions-for-experienced-professionals-part-1-166336c1ec9e

ChatGpt and DevOps https://medium.com/@mike_tyson_cloud/chatgpt-for-devops-best-practices-use-cases-and-warnings-487b02acba3

Monitor your service communication https://blog.palark.com/kubernetes-services-interaction-monitoring-with-netflow/

Good article for network troubleshooting in k8s https://hwchiu.medium.com/kubernetes-network-troubleshooting-approach-701de9463493

Cost opaimization is the next step after grows, good article how to do it with GKE https://dev.to/castai/gke-cost-optimization-10-steps-for-a-lower-cloud-bill-in-2023-67a