Quick answer (featured-snippet ready): Centralize a DevOps commands collection that covers git, Docker/Podman, kubectl, terraform, and CI runner tooling; automate CI/CD with pipeline stages for build, test, scan, and deploy; orchestrate containers with Kubernetes manifests and apply IaC best practices for reproducible cloud infrastructure.

This guide ties command-level practicality to pipeline automation, container orchestration, infrastructure-as-code, observability and security scanning, with ready pointers to a working repository of examples and manifests.

Why this DevOps commands collection matters

Teams grow fast; ad-hoc commands piled into READMEs become technical debt. A curated DevOps commands collection is the operational playbook that turns tribal knowledge into reproducible automation. The right collection saves context switching, reduces mean time to recovery (MTTR), and speeds onboarding.

At command granularity you can document common CI/CD tasks (build, test, push, deploy), container lifecycle management, IaC workflows with Terraform, and incident triage procedures. Those commands form modular building blocks that map directly into CI runners, GitOps agents, and runbooks.

Beyond convenience, the collection acts as a test harness: commands that are exercised in CI are less likely to bit-rot. When the pipeline runs the same sequence you run locally, parity improves — fewer surprises in production and better reproducibility across cloud infrastructure workflows.

Core components: CI/CD, IaC, containers, and orchestration

Effective automation requires a clear separation of responsibilities. CI focuses on building and testing artifacts; CD handles delivery and rollout strategies. Infrastructure as code (IaC) — primarily Terraform or CloudFormation — codifies cloud resources, while Kubernetes manifests describe the runtime state for container orchestration tools.

Design pipelines with discrete stages: source checkout + lint, build + unit test, image build + scan, integration/staging deploy, canary/blue-green promotion, and production. Each stage maps to explicit commands and artifacts: git commands, container build/push commands, terraform plan/apply, kubectl apply/rollout, and monitoring queries for post-deploy checks.

Container orchestration tools (Kubernetes as the de facto example) handle scheduling, service discovery, and scaling. Complement orchestration with a GitOps approach — store manifests, charts, or kustomize overlays in version control, and let automated agents reconcile cluster state. That shifts the security model to controlled pull processes and easier auditability.

Terraform and Kubernetes manifests: Practical usage

Terraform and Kubernetes manifests are complementary: Terraform provisions cloud resources (VPCs, load balancers, managed databases) and may render cluster-level artifacts; Kubernetes manifests specify deployments, services, ingress, and config maps. The best practice is to keep infrastructure provisioning (Terraform) and app manifests (k8s YAML) modular but versioned together for reproducible deployments.

Practical pipeline steps include: terraform init → terraform plan (export plan) → terraform apply (in gated environment), then render k8s manifests (helm template or kustomize), validate with kubeval or kubectl apply –dry-run=client, and deploy with kubectl apply or via a GitOps operator. Automate secrets and config using sealed-secrets, HashiCorp Vault, or cloud KMS integrations.

See a concrete example repository with scripts and manifests to bootstrap these workflows: the DevOps commands collection includes Terraform and Kubernetes manifests, CI snippets, and useful runbook commands you can adapt to your cloud environment. Browse the repo to copy CI job snippets and preset terraform modules into your pipeline.

CI/CD pipelines automation: Commands, patterns, and examples

Automate the mundane: translate manual CLI sequences into idempotent pipeline jobs. Examples of canonical commands you will automate: git checkout & submodule sync, docker build -t :, docker push, terraform plan -out=plan.tfplan, terraform apply plan.tfplan, kubectl set image / kubectl rollout status. Wrap these in reusable pipeline templates and parameterize environment variables for dev/stage/prod.

Use caching to speed builds (docker layer cache, shared artifact storage), and parallelize independent test suites where possible. Gate promotion stages with automated acceptance tests and security scans. For pipelines, prefer declarative CI configuration (GitLab CI YAML, GitHub Actions, Azure Pipelines) so that commands are version-controlled and reviewable.

Don’t forget rollback strategies: store previous image tags and keep migration steps reversible. Implement health checks and post-deploy validation commands in pipelines (curl endpoints, run smoke tests, query Prometheus for error rates). These validation commands are often the difference between a successful release and a rollback-triggered incident.

Monitoring, incident response, and security scanning

Instrumentation should be part of the pipeline. Include commands to deploy or update monitoring exporters, alert rules, and dashboards alongside your application. For example, pipeline steps can apply Prometheus rules and Grafana dashboard provisioning templates, ensuring visibility changes are reviewed with code changes.

Incident response plays well with a documented commands collection: triage commands (kubectl get pods -o wide, kubectl logs -f pod, terraform state list, cloud CLI queries) should live in runbooks. Automate post-incident data capture (snapshot logs, collect heap dumps), and teach the pipeline to run forensic commands on demand via privileged jobs.

Security scanning is critical at multiple layers. Integrate SAST for source, dependency scanning for package managers, container image scanning (Trivy, Clair), and IaC scanning (tfsec, Checkov). Run these scans automatically in PRs and block merges on critical findings. Add a staged remediation flow so high-confidence false positives can be triaged with minimal disruption.

Cloud infrastructure workflows and best practices

Cloud workflows should be reproducible and auditable. Enforce environment parity with immutable artifacts: build artifacts once (images, binary releases) and promote them across environments rather than rebuilding. Lock down credentials and use ephemeral tokens for CI runners. Keep terraform state in remote state backends with locking to prevent concurrent writes.

Adopt a branching strategy that maps to release flows: feature branches for development, protected main for production, and short-lived release branches if you need stabilisation windows. Use pipeline feature flags and controlled rollout strategies so you can test in production safely without exposing all users to changes instantly.

Automate housekeeping: rotate IAM keys via scheduled pipelines, prune old images via lifecycle policies, and run periodic IaC drift detection commands to catch configuration divergence. These automated maintenance commands extend your DevOps commands collection beyond day-to-day deploys into long-term reliability.

Implementation checklist

• Centralize a commands collection in a repo (examples, runbooks, CI snippets).
• Parameterize pipelines and make stages idempotent (build → test → scan → deploy → validate).
• Automate Terraform and k8s manifest validation and apply with gated approvals.
• Integrate automated security scans (SAST/DAST, image, and IaC scanners) in PRs.
• Provision monitoring and include post-deploy validation commands in pipelines.

For an immediately usable starter, clone the example repo with a testbed of scripts and manifests: Terraform and Kubernetes manifests and adapt the CI job templates to your runner.

FAQ

How do I use Terraform and Kubernetes manifests together in a CI/CD pipeline?

Generate and validate infrastructure with Terraform (plan and apply in controlled environments), then render or reference Kubernetes manifests. Validate manifests with kubeval or kubectl –dry-run, run image scans, and only deploy after automated tests pass. Use a GitOps reconciliation or pipeline-driven kubectl apply for deployment.

What are the essential DevOps commands to automate CI/CD tasks?

Automate git (checkout, merge, tag), container build/push (docker build/push or podman), terraform init/plan/apply, kubectl apply/rollout/status/logs, and scanner commands (trivy image, tfsec). Wrap these into pipeline jobs with retries and artifacts for traceability.

Which monitoring and security scanning tools should be included in pipelines?

Include Prometheus and Grafana (metrics/dashboards), ELK/Opensearch (logs), and automated scanners like Trivy, Snyk, tfsec, Checkov. Schedule periodic full scans and run lightweight scans during PRs to catch issues early without blocking fast feedback cycles.

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Repository link (backlinks): DevOps commands collection — browse CI snippets, Terraform modules, and k8s manifests to bootstrap your automation.