Top 4 Kubernetes Anti Patterns

Good day. It's Wednesday, Aug. 14, and in this issue, we're covering:

• Top 4 Kubernetes Anti Patterns

• Go 1.23 Arrives With Faster Build Times

• Gartner Predicts 30% of GenAI Projects to be Abandoned by 2025

• GitOps Kubernetes Starter Template

• Networking Best Practices For Generative AI on AWS

• Modern UI for Ansible, Terraform, OpenTofu, Bash, Pulumi

You share. We listen. As always, send us feedback at [email protected]

Use Case

4 Top Kubernetes Anti Patterns

This quote from Charlie Munger sums it all up; no better intro for this topic can I think of.

Kubernetes is powerful but also complex. There are some common anti-patterns that can slow things down or cause problems.

Let’s look at four of those anti-patterns and how to fix them.

Anti Pattern 1. Manual Deployments

Problem: Deploying multiple applications manually is slow and increases the chance of mistakes. Each time you do it, it might be a little different, leading to issues.

Solution: Automate your deployment process. Tools like Helm and CI/CD pipelines help you deploy apps the same way every time, making things faster and reducing errors.

Anti Pattern 2. Security As An After Thought

Problem: If you don't focus on security from the start, your apps might have vulnerabilities. These weaknesses can be exploited, putting your cluster and data at risk.

Solution: Make security a priority by using DevSecOps practices in your CI/CD pipelines. This helps you catch and fix security issues early, before they become big problems.

Anti-Pattern 3. Chaotic Access Control

Problem: As your Kubernetes environment grows, managing who has access to what becomes complicated and time-consuming. Without a clear system, you could end up with security issues.

Solution: Use Kubernetes Role-Based Access Control (RBAC). RBAC makes it easy to control and automate who can do what in your cluster, keeping things secure and organized.

Anti-Pattern 4. Single Cluster Deployments

Problem: If you only use one cluster, you have one point of failure. If that cluster goes down, all your services could go down too, leading to downtime.

Solution: Use a multi-cluster deployment strategy with Helm charts and GitOps. This spreads your workloads across multiple clusters, so if one fails, the others keep running.

These are just 4 examples of anti-patterns; there are many more out there to watch for.

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Trends & Updates

Go 1.23 Arrives With Faster Build Times

In this release, new iterator functions, packages like iter, tool enhancements, and improved time.Timer and time.Ticker implementations are introduced.

Gartner Predicts 30% of GenAI Projects to be Abandoned After POC by 2025

Due to challenges like poor data quality, high costs, and unclear business value, many Generative AI projects may be abandoned after proof of concept by 2025. Organizations must carefully assess investments to achieve meaningful ROI and long-term benefits.

Resources & Tutorials

GitOps Kubernetes Starter Template

This guide presents a GitOps Kubernetes starter template for quick setup, automating tool installations like ArgoCD and CloudNativePG with the Glasskube Package Manager.

Networking best practices for generative AI on AWS 

This best practices guide recommends using AWS DataSync with Direct Connect for efficient data transfer, EFA for low-latency networking, and SageMaker HyperPod for seamless ML infrastructure management in workflows.

Modern UI for Ansible, Terraform, OpenTofu, Bash, Pulumi

Semaphore lets you easily run Ansible playbooks, get notifications about fails, control access to deployment system. Ideal for projects outgrowing terminal deployment.

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