Alright. Let’s get started. This article is meant to give a beginner’s introduction to some of the key components in DevOps and how they fit together in the modern development process. There are countless web articles describing the same topic, and I don’t intend to provide something groundbreaking here. Instead, I want to focus on the main signals – what stands out as important – so that the essentials come through clearly amid all the noise.
+------------------------------------------------+
| THE DEVOPS PROCESS FLOW |
+==================+=============================+
| STAGE | KEY ACTION |
+==================+=============================+
| 1. Code | Write and commit changes |
+------------------+-----------------------------+
| 2. Build | Compile and package code |
+------------------+-----------------------------+
| 3. Test | Run automated tests |
+------------------+-----------------------------+
| 4. Deploy | Release to production |
+------------------+-----------------------------+
| 5. Feedback | Monitor and inform new code |
+------------------+-----------------------------+
| (Repeat) | The loop continues... |
+------------------+-----------------------------+
Diagram 1: Process flow of DevOps
+======================================================+
| Process Transformation: DevOps |
+======================+===============================+
| STAGE | MANUAL (BEFORE) |
+======================+===============================+
| Code Integration | Manual merges, "merge hell" |
| Build | Run on local machine |
| Test | Manual testing, slow |
| Deploy | Manual scripts, risky |
|----------------------+-------------------------------|
| **Result** | Slow, Error-Prone, Stressful |
+======================+===============================+
| STAGE | AUTOMATED (AFTER) |
+======================+===============================+
| Code Integration | Auto-merge via CI server |
| Build | Automated build on push |
| Test | Automated tests run in pipe |
| Deploy | Automated CD to production |
|----------------------+-------------------------------|
| **Result** | Fast, Reliable, Repeatable |
+======================+===============================+
Diagram 2: Benefits of DevOps
From Simple Development to Complex Reality
When we think about development, we generally imagine it in very simple terms. A programmer writes the code, and that gets implemented on the website. But in practice, the process can get complicated considerably in a short period of time. Multiple programmers might be working on the same codebase. Code changes made by one person can affect what another has already built. It’s important to track who changed what and when, so that it is easier to roll back if something goes wrong.
That’s where a source code management system like Git comes in – it keeps track of every change, every version, and lets multiple developers collaborate efficiently and seamlessly.
╔═══════════════════════════════════════════════════╗
║ THE ILLUSION VS REALITY OF DEVELOPMENT ║
╚═══════════════════════════════════════════════════╝
WHAT WE IMAGINE WHAT ACTUALLY HAPPENS
┌──────────────┐ ┌──────────────┐
│ Programmer │ │ Programmer A │
└──────┬───────┘ └──────┬───────┘
│ │
▼ ▼
┌──────────────┐ ┌──────────────┐
│ Code │ │ Code v1 │◄─┐
└──────┬───────┘ └──────────────┘ │
│ │ │
▼ │ CONFLICT!
┌──────────────┐ │ │
│ Website │ ▼ │
└──────────────┘ ┌──────────────┐ │
│ Code v2 │──┘
SIMPLE └──────────────┘
▲
│
┌──────┴───────┐
│ Programmer B │
└──────────────┘
COMPLEX
Diagram 3: Explains the reality of software development
╔═════════════════════════════════════════════╗
║ THE CHANGE TRACKING CHALLENGE ║
╚═════════════════════════════════════════════╝
BEFORE GIT (CHAOS)
Week 1 Week 2 Week 3
│ │ │
▼ ▼ ▼
Change Change Something
Made Made Broke!
│ │ │
└───────────┴───────────┘
│
▼
┌───────────────┐
│ Who did it? │
│ When? │
│ Why? │
│ How to undo? │
└───────────────┘
│
▼
[ MYSTERY ]
NEED: TRACKING SYSTEM
Diagram 4: Software development without Git(version control system)
╔════════════════════════════════════════════════╗
║ GIT: THE SOLUTION TO CHAOS ║
╚════════════════════════════════════════════════╝
┌─────────────┐
│ GIT │
│ REPOSITORY │
└──────┬──────┘
│
┌──────────────┼──────────────┐
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Dev A │ │ Dev B │ │ Dev C │
│ Branch │ │ Branch │ │ Branch │
└─────┬─────┘ └─────┬─────┘ └─────┬─────┘
│ │ │
│ ┌─────────┼─────────┐ │
│ │ │ │ │
└────┼─────────┴─────────┼────┘
│ │
▼ ▼
[ MERGE ] [ TRACKED ]
│ │
└─────────┬─────────┘
▼
┌───────────┐
│ NO CHAOS! │
└───────────┘
Diagram 5: Software development with Git(version control system)
╔═══════════════════════════════════════════════╗
║ THE ROLLBACK SAFETY NET CONCEPT ║
╚═══════════════════════════════════════════════╝
WITHOUT GIT
Past ──────────────► Present
│
▼
┌────────┐
│ BROKEN │
└────────┘
│
▼
[ NO WAY BACK ]
WITH GIT
┌────┐ ┌────┐ ┌────┐ ┌────┐
│ v1 │───►│ v2 │───►│ v3 │───►│ v4 │
└────┘ └────┘ └────┘ └────┘
▲ │
│ │
│ SOMETHING BROKE │
│ ▼ │
│ ┌────────┐ │
│ │ROLLBACK│ │
└─────────┤ TO v1 │◄──────────┘
└────────┘
SAFETY GUARANTEED
Diagram 6: Rollback concept in Git
The Manual Deployment Problem
Once the code is safely in Git, you still have to get it onto a server. Earlier, in the initial days, someone would manually pull that code, run some tests on their own machine, package it up, and deploy it to production. That works if you deploy less frequently. But with teams making frequent updates (adding more features), the manual way becomes a bottleneck.
Mistakes slip through – from missing a test step to deploying to the wrong environment. This is why automated CI/CD pipelines are needed.
+===================================================+
| The Manual Bottleneck Effect |
+===================================================+
LOW SCALE (1x/Month)
--------------------
[Commit] ---> ( 👤 ) ---> [Deploy OK]
(No pressure)
HIGH SCALE (10x/Week)
---------------------
[Commit] --
[Commit] --
[Commit] --- > ( 👤 ) --- > [ERROR]
[Commit] -- / (Pressure) [CONFLICT]
[Commit] --/ (Overwhelmed) [FAILURE]
CONCLUSION: A human 👤 cannot process high volume.
SOLUTION: Need an automated CI/CD pipeline.
Diagram 7: Manual Deployment
Automating the Build and Test Process
A CI/CD pipeline automates the steps between writing code and deploying it to production. When code is pushed to the repository, the pipeline takes over. It packages the code into a build, then runs automated tests – unit tests to check individual functions, integration tests to ensure different parts work together, security scans for vulnerabilities, and performance tests to check overall health.
All this happens without anyone needing to remember each task, and more focus can be given to newer feature development.
╔═══════════════════════════════════════════════╗
║ THE CI/CD PIPELINE ANATOMY ║
╚═══════════════════════════════════════════════╝
┌─────────────┐
│ CODE PUSHED │
│ TO REPO │
└──────┬──────┘
│
▼
╔══════════════════════╗
║ CI/CD PIPELINE ║
║ ║
║ ┌────────────────┐ ║
║ │ 1. PACKAGE │ ║
║ │ (Build) │ ║
║ └────────┬───────┘ ║
║ │ ║
║ ┌────────▼───────┐ ║
║ │ 2. TEST │ ║
║ │ - Unit │ ║
║ │ - Integration│ ║
║ │ - Security │ ║
║ │ - Performance│ ║
║ └────────┬───────┘ ║
║ │ ║
║ ┌────────▼───────┐ ║
║ │ 3. DEPLOY │ ║
║ │ Test/Dev Env │ ║
║ └────────┬───────┘ ║
║ │ ║
║ ┌────────▼───────┐ ║
║ │ 4. DEPLOY │ ║
║ │ Production │ ║
║ └────────────────┘ ║
╚══════════════════════╝
Diagram 8: CI/CD Pipeline
╔═════════════════════════════════════════════╗
║ THE CI/CD AUTOMATION ENGINE ║
╚═════════════════════════════════════════════╝
DEVELOPER CI/CD DOES ALL:
│
│ ┌──────────┐
│ │ Package │
▼ └────┬─────┘
┌────────────┐ │
│ PUSH CODE │ │
└────────────┘ ▼
│ ┌──────────┐
│ │Unit Test │
│ └────┬─────┘
│ │
│ ▼
Developer's ┌──────────┐
Job Done! │Integrate │
│ │ Test │
│ └────┬─────┘
│ │
▼ ▼
┌────────────┐ ┌──────────┐
│ FOCUS ON │ │ Security │
│ NEXT │ │ Scan │
│ FEATURE │ └────┬─────┘
└────────────┘ │
▼
┌──────────┐
│Performnce│
│ Test │
└────┬─────┘
│
▼
┌──────────┐
│ Deploy │
└──────────┘
Diagram 9: Improvement in Developer’s efficiency
If everything passes, the build goes to a test or development environment, where the new code is tried out alongside what’s already in production. When things look good, it gets deployed live. Jenkins, GitHub Actions, Azure DevOps, and Harness are some examples of popular CI/CD tools.
╔═══════════════════════════════════════════════╗
║ THE ENVIRONMENT PROMOTION LADDER ║
╚═══════════════════════════════════════════════╝
┌──────────────┐
│ CODE PUSHED │
└──────┬───────┘
│
▼
┌──────────────┐
│ CI/CD RUNS │
│ TESTS │
└──────┬───────┘
│
TESTS PASS
│
▼
╔════════════════════════╗
║ TEST/DEV ENVIRON ║ ◄── Try Here First
║ ║
║ New Code + Old Code ║
║ Working Together? ║
╚════════════╤═══════════╝
│
LOOKS GOOD
│
▼
╔════════════════════════╗
║ PRODUCTION ENVIRON ║ ◄── Deploy Live
║ ║
║ Users Get Update ║
╚════════════════════════╝
SAFE PROGRESSION
TEST → PRODUCTION
Diagram 10 : Test/Dev environment to Production Environment
The Environment Consistency Problem
After progressing till here, we will naturally come across one more inconvenience: Sometimes code works on a developer’s laptop, but crashes in production because the underlying environments are different. The way a piece of software interacts with its operating system, libraries, or specific versions of dependencies can easily change from one machine to another. That’s where the need for isolation comes in, so applications don’t have a downtime.
╔═══════════════════════════════════════════════╗
║ "IT WORKS ON MY MACHINE" SYNDROME ║
╚═══════════════════════════════════════════════╝
DEVELOPER'S LAPTOP PRODUCTION SERVER
┌──────────┐ ┌──────────┐
│ CODE │ │ CODE │
└────┬─────┘ └────┬─────┘
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│ Python │ │ Python │
│ 3.9 │ │ 3.7 │
└────┬─────┘ └────┬─────┘
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│ Library │ │ Library │
│ v2.0 │ │ v1.5 │
└────┬─────┘ └────┬─────┘
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│ OS │ │ OS │
│ Ubuntu22 │ │ Ubuntu20 │
└────┬─────┘ └────┬─────┘
│ │
▼ ▼
[ ✓ WORKS ] [ ✗ CRASHES ]
THE ENVIRONMENT MISMATCH PROBLEM
Diagram 11: The environment mismatch problem
╔═══════════════════════════════════════════════╗
║ THE ISOLATION LAYER VISUALIZATION ║
╚═══════════════════════════════════════════════╝
WITHOUT CONTAINERS
┌───────────────────────────┐
│ Operating System │
│ │
│ App1 App2 App3 App4 │
│ │ │ │ │ │
│ └─────┴─────┴─────┘ │
│ │ │
│ Shared Resources │
│ (Conflicts!) │
└───────────────────────────┘
WITH CONTAINERS
┌───────────────────────────┐
│ Operating System │
├───────────────────────────┤
│ ╔═══╗ ╔═══╗ ╔═══╗ ╔═══╗ │
│ ║App║ ║App║ ║App║ ║App║ │
│ ║ 1 ║ ║ 2 ║ ║ 3 ║ ║ 4 ║ │
│ ╚═══╝ ╚═══╝ ╚═══╝ ╚═══╝ │
│ │
│ Each Isolated & Protected │
└───────────────────────────┘
Diagram 12 : Benefits of isolation
This is why containers were introduced. A container acts like a little package: it wraps up everything an application needs – its code, runtime, system tools, libraries – so it can run the same way regardless of where it’s deployed. Containers isolate the application from the underlying environment, making “it works on my machine” a thing of the past.
Out of the various container technologies, Docker is one of the most popular. Docker makes it easy to create, deploy, test, and manage containers. As a result, your app runs the same everywhere – from local machines to production servers.
╔══════════════════════════════════════════════╗
║ THE DEPENDENCY ISOLATION SHIELD ║
╚══════════════════════════════════════════════╝
HOST MACHINE (ANY OS)
┌────────────────────────────────────┐
│ │
│ ╔═══════════════════════════════╗ │
│ ║ CONTAINER SHIELD ║ │
│ ║ ║ │
│ ║ ┌─────────────────────────┐ ║ │
│ ║ │ YOUR APPLICATION │ ║ │
│ ║ │ │ ║ │
│ ║ │ • Code │ ║ │
│ ║ │ • Runtime │ ║ │
│ ║ │ • System Tools │ ║ │
│ ║ │ • Libraries │ ║ │
│ ║ │ • Dependencies │ ║ │
│ ║ │ │ ║ │
│ ║ └─────────────────────────┘ ║ │
│ ║ ║ │
│ ║ ISOLATED FROM HOST ║ │
│ ╚═══════════════════════════════╝ │
│ │
│ Host Changes Don't Affect App! │
└────────────────────────────────────┘
Diagram 13 : Containers Design
╔═══════════════════════════════════════════════╗
║ FROM LOCAL DEVELOPMENT TO PRODUCTION ║
╚═══════════════════════════════════════════════╝
SINGLE CONTAINER
╔═════════════╗
║ Docker ║
║ Container ║
╚══════╤══════╝
│
│
DEPLOY JOURNEY
│
│
┌────────────┼────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ LOCAL │ │ STAGING │ │ PROD │
│ DEV │ │ SERVER │ │ SERVER │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
▼ ▼ ▼
[IDENTICAL] [IDENTICAL] [IDENTICAL]
NO CONFIGURATION DRIFT!
NO ENVIRONMENT SURPRISES!
PREDICTABLE DEPLOYMENTS!
Diagram 14 : Apps in Docker containers works identically in all the environments
The Orchestration Challenge
Docker is perfect for packaging apps consistently, but when you have a lot of containers in production for things like web servers, databases, caches, and microservices, manual management can’t keep up. If a container stops, traffic needs rerouting, scaling is needed with changing demand, and network connections become complex. That’s where Kubernetes steps in.
╔═════════════════════════════════════════════╗
║ THE ORCHESTRATION REQUIREMENT ║
╚═════════════════════════════════════════════╝
PRODUCTION REALITY
Web Server Database Cache APIs
Containers Containers Containers Containers
│ │ │ │
▼ ▼ ▼ ▼
┌────┐┌────┐ ┌────┐┌────┐ ┌────┐ ┌────┐
│ W1 ││ W2 │ │ D1 ││ D2 │ │ C1 │ │ A1 │
└────┘└────┘ └────┘└────┘ └────┘ └────┘
┌────┐┌────┐ ┌────┐
│ W3 ││ W4 │ │ A2 │
└────┘└────┘ └────┘
MANUAL QUESTIONS:
• Which container crashed?
• How to reroute traffic?
• When to scale up/down?
• How to connect them?
▼
NEED ORCHESTRATOR
▼
KUBERNETES!
Diagram 15: Need for container orchestration
Kubernetes is an orchestration layer on top of Docker. It automatically manages containers – restarting failed ones, scaling up or down, balancing traffic, and linking containers together. Docker handles containerization; Kubernetes handles orchestration across your infrastructure, whether in the cloud (AWS, Azure, Google Cloud) or on-premises.
╔═════════════════════════════════════════════╗
║ THE AUTOMATIC MANAGEMENT LOOP ║
╚═════════════════════════════════════════════╝
╔═══════════════════════╗
║ KUBERNETES ║
║ CONTROL PLANE ║
╚═════════╤═════════════╝
│
Constantly Monitors
│
┌─────────────┼─────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│Container │ │Container │ │Container │
│ A │ │ B │ │ C │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
└─────────────┼─────────────┘
│
Reports Status
│
▼
╔═══════════════════╗
║ AUTO ACTIONS: ║
║ • Restart ║
║ • Scale ║
║ • Balance ║
║ • Link ║
╚═══════════════════╝
CONTINUOUS AUTOMATION
Diagram 16: Automatic management
╔══════════════════════════════════════════════╗
║ THE FAILED CONTAINER AUTO-RECOVERY ║
╚══════════════════════════════════════════════╝
TIME LINE
t=0 t=1 t=2 t=3
│ │ │ │
▼ ▼ ▼ ▼
┌────────┐ ┌────────┐ ┌────────┐ ┌─────────┐
│Running │ │Crashed!│ │Detected│ │Restarted│
└────────┘ └───╳────┘ └────────┘ └─────────┘
│ │ │
│ ▼ │
│ ╔═══════════════╗ │
│ ║ KUBERNETES ║ │
└───►║ Monitors ║ │
║ & Acts ║───┘
╚═══════════════╝
AUTOMATIC HEALING
NO MANUAL INTERVENTION
Diagram 17: Auto-recovery
╔═══════════════════════════════════════════════╗
║ THE SCALING AUTOMATION ENGINE ║
╚═══════════════════════════════════════════════╝
╔═══════════════════════╗
║ KUBERNETES ║
║ Watches Metrics ║
╚═══════╤═══════════════╝
│
┌───────────┴───────────┐
│ │
LOW DEMAND HIGH DEMAND
│ │
▼ ▼
┌──────────┐ ┌──────────┐
│ Scale │ │ Scale │
│ DOWN │ │ UP │
└────┬─────┘ └────┬─────┘
│ │
▼ ▼
┌────┐ ┌────┐┌────┐
│ C1 │ │ C1 ││ C2 │
└────┘ └────┘└────┘
┌────┐┌────┐
1 Container │ C3 ││ C4 │
Enough └────┘└────┘
4 Containers
Needed
ELASTIC SCALING
Diagram 18: Elastic scaling
╔═════════════════════════════════════════════╗
║ THE TRAFFIC BALANCING MECHANISM ║
╚═════════════════════════════════════════════╝
USER TRAFFIC
│
│
▼
╔═══════════════╗
║ KUBERNETES ║
║ LOAD ║
║ BALANCER ║
╚═══════╤═══════╝
│
┌───────────┼───────────┐
│ │ │
33%│ 33%│ 33%│
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│Container │ │Container │ │Container │
│ 1 │ │ 2 │ │ 3 │
└──────────┘ └──────────┘ └──────────┘
EVEN DISTRIBUTION
OPTIMIZED ROUTING
NO OVERLOAD
Diagram 19: Load Balancing
╔══════════════════════════════════════════════╗
║ THE CONTAINER LINKING SYSTEM ║
╚══════════════════════════════════════════════╝
╔═══════════════════════╗
║ KUBERNETES ║
║ Service Mesh ║
╚═══════╤═══════════════╝
│
Auto-Links
│
┌───────────┼───────────┐
│ │ │
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ WEB │◄─┤ API │◄─┤DATABASE │
│ SERVER │ │ SERVICE │ │ │
└─────────┘ └─────────┘ └─────────┘
│ │ │
└───────────┼───────────┘
│
┌─────▼─────┐
│ CACHE │
└───────────┘
AUTOMATIC SERVICE DISCOVERY
DYNAMIC CONNECTIONS
Diagram 20: Linking of Containers
╔═════════════════════════════════════════════╗
║ THE DIVISION OF RESPONSIBILITIES ║
╚═════════════════════════════════════════════╝
DOCKER'S JOB KUBERNETES' JOB
┌─────────────┐ ┌─────────────┐
│ CREATE │ │ DEPLOY │
│ Containers │ │ Where? │
└──────┬──────┘ └──────┬──────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ PACKAGE │ │ MONITOR │
│ Application │ │ Health │
└──────┬──────┘ └──────┬──────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ RUN │ │ SCALE │
│ Containers │ │ Count │
└─────────────┘ └──────┬──────┘
│
▼
┌─────────────┐
│ MANAGE │
│ Lifecycle │
└─────────────┘
CONTAINERIZATION ORCHESTRATION
Diagram 21: Shared responsibility model
Watching What Happens in Production
The task isn’t completed after deployment. It’s essential to observe how the application actually runs in production. Are errors cropping up? Is the application slow? Monitoring tools track metrics like CPU utilization, memory utilization, response times, and error rates. Logging captures what the app is doing and errors that occur. When something goes wrong, this infrastructure helps you quickly find and fix problems before the end users notice.
╔═══════════════════════════════════════════════╗
║ THE COMPLETE OBSERVABILITY STACK ║
╚═══════════════════════════════════════════════╝
┌─────────────────┐
│ APPLICATION │
│ IN PRODUCTION │
└────────┬────────┘
│
┌────────────┼────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│MONITORING│ │ LOGGING │ │ INCIDENT │
│ │ │ │ │ RESPONSE │
│Tracks │ │Captures │ │ │
│Metrics │ │Events │ │Fixes │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
└────────────┼────────────┘
│
▼
╔═══════════════╗
║ COMPLETE ║
║ VISIBILITY ║
║ ║
║ Know What's ║
║ Happening ║
║ Always ║
╚═══════════════╝
Diagram 22: Monitoring, Logging, and Incident Response
╔══════════════════════════════════════════════╗
║ DETECTING ISSUES BEFORE USERS ║
╚══════════════════════════════════════════════╝
WITHOUT MONITORING
Error Occurs ──► User Complains ──► Fix
│ │
└───────────────────┘
Hours/Days !
WITH MONITORING
Error Occurs
│
▼
╔═════════════╗
║ MONITORING ║
║ Detects ║ ──► Alerts Team
║ Instantly ║
╚═════╤═══════╝
│
▼
Team Fixes Issue
│
▼
Users Never Know!
PROACTIVE > REACTIVE
Diagram 23: Proactive response
What One Needs to Keep in Mind
Anyone wanting to gain more expertise in DevOps should also focus on understanding the different types of automated tests that are written. Writing strong automated tests relies on knowing what’s being developed and thinking through how it might be used or misused.
╔══════════════════════════════════════════════╗
║ THINKING THROUGH USE & MISUSE ║
╚══════════════════════════════════════════════╝
┌─────────────────┐
│ FEATURE TO │
│ BE TESTED │
└────────┬────────┘
│
Think About:
│
┌────────────┴────────────┐
│ │
▼ ▼
┌───────────┐ ┌───────────┐
│ NORMAL │ │ ABNORMAL │
│ USE │ │ USE │
│ │ │ │
│• Expected │ │• Wrong │
│ Inputs │ │ Inputs │
│• Happy │ │• Edge │
│ Path │ │ Cases │
│• Valid │ │• Malicious│
│ Flow │ │ Intent │
└─────┬─────┘ └─────┬─────┘
│ │
└────────────┬────────────┘
│
▼
[ COMPREHENSIVE
TEST COVERAGE ]
Diagram 24: Testing consideration
Programming language skills, API familiarity, domain knowledge, and awareness of testing frameworks all help. The choices here should work with the rest of your DevOps ecosystem – cloud provider, CI/CD tool, automation platform. For example, Jenkins(A CI/CD tool) integrates with various languages, including Python and frameworks like pytest, so it’s crucial to know which tools work well together.
╔═════════════════════════════════════════════╗
║ THE DEVOPS ECOSYSTEM COMPATIBILITY ║
╚═════════════════════════════════════════════╝
┌─────────────────┐
│ TEST CHOICE │
└────────┬────────┘
│
Must Work With:
│
┌────────────┼────────────┐
│ │ │
▼ ▼ ▼
┌──────────┐ ┌──────────┐ ┌──────────┐
│ Cloud │ │ CI/CD │ │Automation│
│ Provider │ │ Tool │ │ Platform │
│ │ │ │ │ │
│AWS/Azure │ │ Jenkins │ │ Etc. │
│ /GCP │ │ GitHub │ │ │
└────┬─────┘ └────┬─────┘ └────┬─────┘
│ │ │
└────────────┼────────────┘
│
▼
╔═══════════════╗
║ INTEGRATION ║
║ IS CRITICAL ║
╚═══════════════╝
Diagram 25: Ecosystem compatibility
Wrapping Up
This is a starting point for making sense of DevOps, connecting the steps from code and collaboration to automation and infrastructure to monitoring and feedback. Each layer shows up to address a problem that couldn’t be solved with the previous one alone. As this landscape keeps evolving, noticing those key transitions is what helps turn the complicated deployments into something that actually works.
+------------------+
| Source Control |
+------------------+
|
+------------------+
| CI/CD |
+------------------+
|
+------------------+
| Containers |
+------------------+
|
+------------------+
| Orchestration |
+------------------+
|
+------------------+
| Monitoring |
+------------------+
Diagram 26 : Summary
More than anything, DevOps is about understanding how the pieces fit together and why each is needed, rather than just learning tools in isolation. Thank you for reading my article.
If you have any questions, please feel free to send me an email. You can also contact me via LinkedIn. You can also follow me on X.
You can read my article on system design here.
If you want me to write on any other topic, please let me know in the comments.
Link to my HackerNoon profile.
