EKS Setup and Configuration

EKS Setup and Configuration#

Amazon EKS runs the Kubernetes control plane for you – managed etcd, API server, and controller manager across multiple AZs. You are responsible for the worker nodes, networking configuration, and add-ons.

Cluster Creation Methods#

eksctl is the fastest path for a working cluster. It creates the VPC, subnets, NAT gateway, IAM roles, node groups, and kubeconfig in one command:

eksctl create cluster \
  --name my-cluster \
  --region us-east-1 \
  --version 1.31 \
  --nodegroup-name workers \
  --node-type m6i.large \
  --nodes 3 \
  --nodes-min 2 \
  --nodes-max 10 \
  --managed

For repeatable setups, use a ClusterConfig file:

EKS Troubleshooting

EKS Troubleshooting#

EKS failure modes combine Kubernetes problems with AWS-specific issues. Most fall into a handful of categories: IAM permissions, networking/security groups, missing tags, and add-on misconfiguration.

Nodes Not Joining the Cluster#

Symptoms: kubectl get nodes shows fewer nodes than expected. ASG shows instances running, but they never register.

aws-auth ConfigMap Missing Node Role#

The most common cause. Worker nodes authenticate via aws-auth. If the node IAM role is not mapped, nodes are rejected silently.

Ephemeral Cloud Clusters: Create, Validate, Destroy Sequences for EKS, GKE, and AKS

Ephemeral Cloud Clusters#

Ephemeral clusters exist for one purpose: validate something, then disappear. They are not staging environments, not shared dev clusters, not long-lived resources that someone forgets to turn off. The operational model is strict – create, validate, destroy – and the entire sequence must be automated so that destruction cannot be forgotten.

The cost of getting this wrong is real. A three-node EKS cluster left running over a weekend costs roughly $15. Left running for a month, $200. Multiply by the number of developers or CI pipelines that create clusters, and forgotten ephemeral infrastructure becomes a significant budget line item. Every template in this article includes auto-destroy mechanisms to prevent this.

etcd Maintenance for Self-Managed Clusters

etcd Maintenance for Self-Managed Clusters#

etcd is the backing store for all Kubernetes cluster state. Every object – pods, services, secrets, configmaps – lives in etcd. If etcd is unhealthy, your cluster is unhealthy. If etcd data is lost, your cluster is gone. Managed Kubernetes services (EKS, GKE, AKS) handle etcd for you, but self-managed clusters require you to operate it directly.

All etcdctl commands below require TLS flags. Set these as environment variables to avoid repeating them:

Event-Driven Architecture for Microservices

Event-Driven Architecture for Microservices#

In a microservices architecture, services need to communicate. The two fundamental approaches are synchronous (request-response) and asynchronous (event-driven). Most systems use both – the decision is which interactions should be synchronous and which should be event-driven.

Synchronous vs Asynchronous Communication#

Synchronous (request-response): Service A calls Service B and waits for a response. Simple, familiar, and works well when A needs the response to continue. The cost is temporal coupling – if B is down, A fails.

FIPS 140 Compliance: Validated Cryptography, FIPS-Enabled Runtimes, and Kubernetes Deployment

FIPS 140 Compliance#

FIPS 140 (Federal Information Processing Standard 140) is a US and Canadian government standard for cryptographic modules. If you sell software to US federal agencies, process federal data, or operate under FedRAMP, you must use FIPS 140-validated cryptographic modules. Many regulated industries (finance, healthcare, defense) also require or strongly prefer FIPS compliance.

FIPS 140 does not tell you which algorithms to use — it validates that a specific implementation of those algorithms has been tested and certified by an accredited lab (CMVP — Cryptographic Module Validation Program).

From Empty Cluster to Production-Ready: The Complete Setup Sequence

From Empty Cluster to Production-Ready#

This is the definitive operational plan for taking a fresh Kubernetes cluster and making it production-ready. Each phase builds on the previous one, with verification steps between phases and rollback notes where applicable. An agent should be able to follow this sequence end-to-end.

Estimated timeline: 5 days for a single operator. Phases 1-2 are blocking prerequisites. Phases 3-6 can partially overlap.


Phase 1 – Foundation (Day 1)#

Everything else depends on a healthy cluster with proper namespacing and storage. Do not proceed until every verification step passes.

GCP Fundamentals for Agents

Projects and Organization#

GCP organizes resources into Projects, which sit under Folders and an Organization. A project is the fundamental unit of resource organization, billing, and API enablement. Every GCP resource belongs to exactly one project.

# Set the active project
gcloud config set project my-prod-project

# List all projects
gcloud projects list

# Create a new project
gcloud projects create staging-project-2026 \
  --name="Staging" \
  --organization=ORG_ID

# Enable required APIs (must be done per-project)
gcloud services enable compute.googleapis.com
gcloud services enable container.googleapis.com
gcloud services enable sqladmin.googleapis.com

Check which project is currently active:

GCP Terraform Patterns: Projects, GKE, Workload Identity, Cloud SQL, and Common Gotchas

GCP Terraform Patterns#

GCP’s Terraform provider (google and google-beta) has patterns distinct from both AWS and Azure. The biggest differences: APIs must be explicitly enabled per project, IAM uses a binding model (not inline policies), and GKE requires secondary IP ranges for VPC-native networking. GCP resources also tend to have longer creation times with more eventual consistency.

Projects and API Enablement#

Before creating any resource in GCP, the corresponding API must be enabled in the project. This is the most common source of first-time failures.

Git Branching Strategies: Trunk-Based, GitHub Flow, and When to Use What

Git Branching Strategies#

Choosing a branching strategy is choosing your team’s speed limit. The wrong model introduces merge conflicts, stale branches, and release bottlenecks. The right model depends on how you deploy, how big your team is, and how much you trust your test suite.

Trunk-Based Development#

Everyone commits to main (or very short-lived branches that merge within hours). No long-running feature branches. No develop branch. No release branches unless you need to patch old versions.