Make Update and Update: A Complete Guide

Make Update and Update: A Complete GuideKeeping software, systems, and documents current is a continuous task in technology and business. The phrase “Make Update and Update” may sound repetitive, but it captures two complementary ideas: the act of creating or preparing updates (“make update”) and the process of applying or executing them (“update”). This guide explains why both steps matter, how they differ, practical workflows, tools, and best practices to ensure updates are effective, safe, and sustainable.

What “Make Update” vs “Update” Means

• Make update — preparing, building, or packaging changes. This includes coding, compiling, creating change logs, building artifacts (packages, containers), and producing migration scripts or documentation.
• Update — applying those prepared changes to systems, devices, or records. This includes installing packages, deploying containers, running database migrations, or replacing files in production.

These stages map to development and operations responsibilities in many teams: developers and build engineers typically handle “make update,” while system administrators, DevOps engineers, or automated pipelines handle “update.”

Why Distinguishing the Two Matters

Separating preparation from execution reduces risk. Preparation ensures updates are tested, documented, and packaged consistently. Execution focuses on delivering updates reliably and safely to users or systems. If you skip preparation, updates may be incomplete, introduce regressions, or lack rollback paths. If you skip careful execution, well-made updates can still cause downtime or data loss.

Typical Workflow (End-to-End)

1. Requirements & planning

   • Identify bug fixes, features, security patches.
   • Prioritize based on impact, dependencies, and urgency.

2. Make update — development & build

   • Implement code changes or configuration updates.
   • Write or update tests and documentation.
   • Build artifacts: binaries, packages (.deb/.rpm), container images, or scripts.
   • Create release notes and changelogs.
   • Prepare migration scripts and backups for stateful changes.

3. Test & verify

   • Unit and integration tests.
   • Staging environment deployment for QA.
   • Run performance and security tests.
   • Validate rollback procedures.

4. Update — deployment & application

   • Schedule maintenance windows if needed.
   • Notify stakeholders and users.
   • Deploy via package manager, configuration management tools, CI/CD pipelines, or orchestration platforms (Kubernetes, Docker Swarm).
   • Monitor system health, logs, and metrics during rollout.

5. Post-deployment

   • Verify functionality, performance, and user reports.
   • Close change tickets and update documentation.
   • Retrospect and improve the process.

Tools & Technologies (Examples)

• Build & packaging
  • Make, Gradle, Maven, npm, pip, Cargo
  • Docker, Buildah, Kaniko for container images
• CI/CD
  • GitHub Actions, GitLab CI, Jenkins, CircleCI
• Configuration management & deployment
  • Ansible, Chef, Puppet, SaltStack, Terraform (infra)
  • Kubernetes, Helm, Argo CD, Flux
• Package repositories & registries
  • Artifactory, Nexus, Docker Hub, GitHub Packages
• Monitoring & rollback
  • Prometheus, Grafana, ELK/EFK, Sentry, PagerDuty

Strategies for Safe Updates

• Blue/Green deployments: run two identical environments and switch traffic to the new one when ready.
• Canary releases: roll out to a subset of users first and expand if stable.
• Feature toggles: deploy code with features disabled, then enable remotely.
• Transactional schema migrations: ensure database changes are backward-compatible; use phased migrations.
• Immutable infrastructure: replace servers rather than patching in place.
• Automated testing & gates: prevent promotion of builds that fail tests.

Common Pitfalls and How to Avoid Them

• Missing rollback plan — always prepare and test rollbacks.
• Incomplete tests — include integration and real-world scenario tests.
• Ignoring dependencies — version pinning and dependency scanning reduce surprises.
• Poor communication — notify affected teams and users in advance.
• Long-lived manual processes — automate repeatable steps to avoid human error.

Practical Examples

1. Open-source library

   • Make update: bump version, run tests, build package, update CHANGELOG, tag release.
   • Update: publish to PyPI/npm, update projects that depend on it, monitor integration tests.

2. Web application

   • Make update: implement feature, containerize app, run CI tests, create migration scripts.
   • Update: deploy via rolling update in Kubernetes, run database migrations safely, monitor latency/errors.

3. Embedded device firmware

   • Make update: compile firmware image, sign it, create delta update.
   • Update: push OTA update to a small set of devices, verify integrity and fallback on failure.

Checklist Before Updating Production

• [ ] Tests pass (unit, integration, end-to-end)
• [ ] Backups exist and are tested
• [ ] Rollback procedure documented and tested
• [ ] Monitoring and alerting set up
• [ ] Change and maintenance windows communicated
• [ ] Dependency versions and licenses reviewed
• [ ] Migration scripts ready and reversible where possible

Measuring Success

Key metrics to track:

• Mean time to deploy (MTTD)
• Mean time to recover (MTTR)
• Deployment failure rate
• Time between deployment and detection of incidents
• User-facing error rates and performance metrics post-update

Conclusion

“Make update” and “update” are two halves of a single lifecycle: preparation and execution. Treating them as distinct improves reliability, reduces risk, and enables repeatable, auditable change. With clear workflows, automation, safe deployment strategies, and good communication, updates become manageable rather than perilous.