Do you feel like you're solving a puzzle when deploying applications in Kubernetes? You are not alone in this! When something goes wrong during application deployment, it becomes all the more crucial to diagnose the issue methodically and get things back on track. This guide walks you through practical steps for troubleshooting deployment failures efficiently.

The Kubernetes API server is a key component in the control plane. Every interaction, whether deploying applications, scaling workloads, or monitoring system health, depends on the API server. Consider the human body: We have the brain as the critical organ, and the nerves function as the control system. The Kubernetes API server is like the nerve center of cluster management.

Persistent storage is the backbone of stateful applications running in Kubernetes. Whether you are managing databases, logs, or application states, ensuring transactional data remains intact despite pod restarts or node failures is a challenge. In this blog, we will discuss the most common persistent storage issues in Kubernetes and how to handle them with practical, real-world solutions.

As software grows ever more complex, the growing trend of Internal Development Platforms (IDPs) is becoming undeniable. It doesn't matter if you are the next AI super company or a small tech startup—if you are building software, you should consider having a repeatable set of practices and tooling in place to remove developer friction and supercharge operations. IDPs can be used to solve a number of issues, from context switching and lack of collaboration to lack of visibility and security risks.

In the early days, people debated how safe it was to store their money in the bank; now, we debate running databases on Kubernetes. Over the years, Kubernetes has evolved significantly, transforming into a capable platform for handling various workloads, including stateful ones. In this blog, I will consolidate some of the best arguments from both sides and provide you with some points to discuss with your team lead in your next conversation. It's an interesting topic with varying answers.

Imagine you're developing an application on your laptop. It runs perfectly, but when you deploy it on a server, things break—dependency mismatches, configuration issues, and endless debugging. Docker eliminates these problems by packaging applications and their dependencies into portable, lightweight containers. This ensures that applications run consistently across different environments, whether it's a developer’s laptop, a testing server, or a cloud platform.

Security and compliance: Many industries, especially those governed by regulations like HIPAA, the PCI DSS, or the GDPR, require detailed logs for compliance and to trace security incidents. Troubleshooting and forensic analysis: If something goes wrong—whether due to accidental configuration changes or malicious activity—having detailed logs helps diagnose the root cause and quickly remediate it.

Kubernetes has been facilitating container orchestration for around a decade for both stateful and stateless application workloads. With the recent rise of AI and the advent of tools like Kubeflow and Argo Workflows, Kubernetes is also becoming a first-class citizen when it comes to running AI workloads. When you are training a model on K8s, you may be tweaking many parameters and have to test each of them one by one.

Prometheus is one of the most popular open-source monitoring and alerting tools. Setting up Prometheus with Docker Compose can make your monitoring stack easier to deploy and manage if you're running containerized applications. This guide will walk you through everything you need to get Prometheus up and running with Docker Compose, from installation to configuration and setting up basic alerts.

Over the past few years, Kubernetes (K8s) has become the preferred method of orchestrating containers and microservices. Its self-healing, high scalability, and open-source nature make it appealing to a wide range of users. However, deploying, running, and scaling containerized applications and microservices with Kubernetes can be challenging. The Kubernetes community keeps growing, but there still aren’t that many experienced K8s engineers.