¶ AWS (Amazon Web Services)
¶ Dev Ops
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¶ Introduction to AWS for a DevOps Journey
When people first hear the term “DevOps,” they often think of automation scripts, deployment pipelines, or configuration tools. But DevOps is much more of a cultural movement than a collection of utilities. It is the idea that development and operations should flow together so naturally that the boundaries between them feel almost invisible. It is the belief that software should move from idea to production as smoothly as possible. Now, when you take this mindset and combine it with the scale, flexibility, and reach of cloud computing, you get a partnership that fundamentally reshapes how technology is built and delivered. That is where AWS steps into the story.
Amazon Web Services is not merely a cloud provider; it is the world’s largest playground for builders, engineers, and problem-solvers. It offers a vast collection of tools that let you craft applications the way modern organizations dream of — fast, resilient, automated, observable, and endlessly scalable. AWS didn’t become a global cloud giant by accident. It grew by understanding what developers and operations teams needed before many of them even realized it themselves. Today, countless startups, government agencies, Fortune 500 companies, and independent teams rely on AWS because it offers opportunities that traditional infrastructure simply cannot.
For anyone stepping into DevOps, understanding AWS is almost unavoidable, not because of popularity alone but because AWS embodies the principles DevOps stands for. The platform encourages automation at every layer, promotes clarity through monitoring and logging, supports rapid experimentation, and eliminates the drag of traditional hardware constraints. DevOps aims for speed and stability working hand in hand, and AWS gives you a canvas where those ideas can actually come alive.
When you begin working with AWS, one of the first things you notice is the sheer breadth of services it provides. What started with simple compute and storage offerings has grown into a massive ecosystem covering virtually every domain you can imagine—virtual servers, Kubernetes clusters, serverless functions, managed databases, message queues, identity management, monitoring systems, security tooling, analytics platforms, AI services, IoT capabilities, and an endless list of additional building blocks. This might sound overwhelming at first, but the beauty of AWS is that you don’t need to master everything. You learn the pieces relevant to your goals, and gradually the rest begins to make sense.
The real magic of AWS, especially for DevOps engineers, lies in how all these pieces integrate. A pipeline that can build code, test it automatically, deploy it to scalable infrastructure, capture logs, send metrics to dashboards, trigger alarms, and roll back if something goes wrong is not a futuristic concept. On AWS, it is ordinary. You can craft environments that adjust in real time to traffic patterns, automatically heal when resources fail, and adapt when requirements evolve. These capabilities shift the way teams think about reliability. Instead of preventing failure at all costs, you architect systems that expect failure and recover instantly when it happens.
AWS encourages this mindset not only through its services but also through its design philosophy. Compute instances, storage volumes, load balancers, and containers are treated as ephemeral and replaceable. Pipelines are configurable, repeatable, and testable. Infrastructure is no longer a fragile set of servers that must be nurtured; it becomes version-controlled code that can be created, modified, and torn down in minutes. DevOps engineers quickly learn that AWS removes much of the waste associated with traditional operations—waiting for hardware, maintaining outdated systems, guessing capacity, or walking carefully around infrastructure that no one wants to touch.
Another idea that becomes easier to grasp on AWS is scalability. In the old world, scaling required buying new machines, configuring them manually, and praying that traffic wouldn’t spike beyond what those machines could handle. On AWS, scaling is a natural part of system design. You can let your applications scale automatically based on CPU usage, incoming requests, queue length, or custom metrics. You no longer think in terms of “how many servers do we need?” but in terms of “how do we let the system decide?” That shift is transformative. It brings DevOps engineers closer to a world where efficiency is automated, and systems adapt fluidly to demand.
Security is another pillar where AWS plays a significant role. AWS provides tools to manage identity, control permissions, encrypt data, monitor activity, and enforce compliance. DevOps teams must understand that security is not an additional layer slapped on at the end; it is woven into every decision. AWS reinforces this idea with features that let you define fine-grained access rules, isolate environments, audit every action, and build architectures that align with modern security expectations. The more deeply you explore AWS, the more you realize that DevOps without security awareness isn’t DevOps at all.
As cloud-native philosophies grow, AWS also offers modern ways to build applications that don’t rely on managing servers at all. Services like Lambda, DynamoDB, Fargate, Step Functions, and EventBridge encourage a new style of engineering where you focus purely on logic and flow. AWS handles the infrastructure behind the scenes. This serverless mindset fits beautifully with DevOps because it supports rapid iteration, reduces operational overhead, and allows teams to release updates continuously without worrying about provisioning or maintaining machines. It removes friction and gives space for creativity and speed.
Of course, AWS is not just a set of technologies; it is also a culture of experimentation. DevOps thrives in environments where teams can try things, learn quickly, and adjust without fear of causing irreversible damage. AWS empowers this by letting you spin up test environments, run simulations, perform load tests, or experiment with new architectures without long-term commitments. Want to test a new database engine? Launch it. Want to try a different deployment model? Deploy it. Want to simulate a production-like environment without buying hardware? Clone it. This freedom shortens development cycles and encourages a bold, iterative approach to problem-solving.
Yet, despite all the advanced capabilities, the journey into AWS is best taken step by step. It’s easy to run ahead and try to absorb everything at once, but AWS rewards patience and practice. DevOps engineers who flourish on AWS are those who learn by building—deploying sample applications, automating processes, experimenting with pipelines, and observing how systems behave in real conditions. This course is designed to help you grow that experience layer by layer, until navigating AWS feels natural.
Throughout the hundred articles that follow, you will move through the AWS landscape from multiple angles. You will see how compute services can be orchestrated, how networks are crafted, how security boundaries are drawn, how automation removes repetition, how deployments become predictable, and how observability gives you insight into running applications. You will understand everything from IAM roles to container clusters, from Lambda triggers to VPC layout, from CI/CD workflows to cost optimization. Each concept, tool, or service will add another piece to your mental map until AWS becomes less of a vast unknown and more of a playground you can explore with confidence.
Along the way, you’ll notice that AWS and DevOps share a common spirit. Both aim to make software delivery smoother, more reliable, and more enjoyable. Both value automation, clarity, experimentation, and continuous improvement. Both challenge the old idea that operations must be slow and rigid. And both empower teams to think bigger, move faster, and build systems that scale not just technically but culturally.
By the time you reach the end of this course, AWS will no longer feel like a collection of services; it will feel like an ecosystem you understand deeply. You will know how each piece contributes to the DevOps story, how systems become more resilient through good design, and how automation elevates productivity in ways that manual processes never could. More importantly, you will be equipped to build things—not theoretical examples but real systems that could run in production, handle traffic, survive failures, and support continuous deployment with confidence.
This journey is not just about learning AWS. It is about stepping into a mindset that transforms how you think about software, infrastructure, and collaboration. DevOps is not simply a job description; it is a new way of seeing the world of technology. AWS becomes the platform that lets you express that vision.
So take a breath, approach each topic with curiosity, and enjoy the path ahead. AWS has a way of rewarding those who explore it with patience and creativity. And DevOps has a way of turning that exploration into something meaningful and impactful. By the end of this course, both will feel like natural parts of how you build, operate, and imagine technology.
¶ AWS (Amazon Web Services)
¶ Beginner (Introduction to AWS and DevOps Basics)
1. Introduction to Cloud Computing and DevOps
2. Overview of AWS: Key Services for DevOps
3. Getting Started with AWS Free Tier
4. Setting Up an AWS Account and IAM Users
5. Understanding AWS Regions and Availability Zones
6. Navigating the AWS Management Console
7. Introduction to AWS CLI and SDKs
8. Creating Your First EC2 Instance
9. Understanding EC2 Instance Types and Sizing
10. Using Amazon Machine Images (AMIs) in AWS
11. Connecting to EC2 Instances via SSH
12. Introduction to Amazon S3 and Object Storage
13. Setting Up Amazon S3 Buckets for Storage
14. Managing Permissions with AWS IAM
15. Using AWS Elastic Load Balancer (ELB)
16. Setting Up Auto Scaling in AWS
17. Getting Started with AWS CloudWatch for Monitoring
18. Introduction to AWS CloudFormation and Infrastructure as Code
19. Creating CloudFormation Templates for Resources
20. Exploring AWS Elastic Beanstalk for Quick App Deployment
21. Understanding AWS VPC and Network Basics
22. Setting Up VPCs, Subnets, and Security Groups
23. Getting Started with Amazon RDS for Databases
24. Understanding Amazon DynamoDB for NoSQL
25. Introduction to AWS Lambda and Serverless Computing
26. Setting Up AWS SQS for Message Queues
27. Using AWS SNS for Simple Notifications
28. Managing Data in Amazon Aurora
29. Basic CI/CD in AWS with CodePipeline
30. Introduction to AWS Elastic File System (EFS)
¶ Intermediate (Expanding DevOps Practices in AWS)
31. Creating and Managing Elastic Kubernetes Service (EKS)
32. Setting Up and Managing ECS for Containers
33. Using Amazon Lightsail for Simpler Deployments
34. Configuring S3 for Static Website Hosting
35. Building Infrastructure with AWS CloudFormation Stacks
36. Using AWS Elastic Beanstalk for Multi-Tier Applications
37. Integrating AWS CodeBuild with Your DevOps Pipeline
38. Building and Managing CI/CD Pipelines with AWS CodePipeline
39. Implementing GitOps with AWS CodeCommit
40. Managing Secrets with AWS Secrets Manager
41. Integrating AWS Lambda with Amazon S3 Events
42. Monitoring Applications with AWS CloudWatch Logs and Alarms
43. Creating Scalable APIs with Amazon API Gateway
44. Using AWS CloudTrail for Security Auditing
45. Leveraging AWS Systems Manager for Configuration Management
46. Automating Tasks with AWS CloudWatch Events
47. Optimizing AWS EC2 with Auto Scaling Policies
48. Provisioning and Managing Databases with RDS
49. Container Orchestration with Amazon EKS
50. Using AWS Fargate for Serverless Containers
51. Implementing Multi-Region Deployments in AWS
52. Networking Security with VPC Peering and Transit Gateway
53. Configuring Security Groups and Network ACLs in AWS
54. Securing AWS Resources with IAM Roles and Policies
55. Using AWS Cost Explorer for Cost Management
56. Setting Up and Managing AWS Backup Services
57. Continuous Integration with AWS CodeDeploy
58. Optimizing EC2 Performance and Cost Efficiency
59. Setting Up AWS Elasticache for Caching
60. Securing Lambda Functions with VPC and IAM Policies
61. Managing Application Security with AWS WAF
62. Configuring Auto Scaling in Amazon RDS and Aurora
63. Using AWS CloudFormation for Multi-Account Management
64. Setting Up CI/CD for Serverless Applications with AWS
65. Deploying Applications Using AWS CodePipeline
66. Building Containerized Applications with ECS and Fargate
67. Using Amazon Kinesis for Real-Time Data Processing
68. Leveraging AWS CloudWatch for Advanced Monitoring
69. Managing User Authentication with Amazon Cognito
70. Integrating Amazon EFS with EC2 for Shared Storage
¶ Advanced (Mastering AWS for DevOps Automation and Scalability)
71. Designing a Multi-Region, Multi-Account AWS Architecture
72. Advanced AWS Lambda: Best Practices for Serverless Architectures
73. Creating Highly Available and Scalable Architectures in AWS
74. Automating Infrastructure Management with AWS CloudFormation
75. Advanced Networking with AWS Transit Gateway and Direct Connect
76. Implementing Blue-Green Deployments on AWS
77. Building a Complete DevOps Pipeline with AWS CodePipeline and Jenkins
78. Serverless DevOps with AWS Lambda and API Gateway
79. Advanced Container Management with AWS EKS and Fargate
80. Continuous Security with AWS Inspector and GuardDuty
81. Implementing Zero-Trust Security Models in AWS
82. Optimizing Cost with Reserved and Spot Instances in EC2
83. Scaling CI/CD Pipelines with AWS CodePipeline
84. Scaling Kubernetes Clusters on AWS EKS
85. Advanced Container Security on AWS with Amazon ECR
86. Using AWS Glue for Data Pipeline Automation
87. Serverless Machine Learning with AWS SageMaker
88. Setting Up and Managing AWS AppConfig for Application Management
89. Optimizing AWS Lambda Performance with API Gateway and CloudFront
90. Managing Distributed Tracing with AWS X-Ray
91. CloudFormation Advanced Topics: Nested Stacks and Macros
92. Designing Highly Resilient Applications with AWS Availability Zones
93. Automating AWS Infrastructure with Terraform and CloudFormation
94. Running Highly Available Web Applications with AWS Elastic Load Balancer
95. Advanced Secrets Management with AWS Secrets Manager and KMS
96. Implementing CI/CD for Microservices with AWS
97. Implementing Infrastructure Monitoring and Metrics with CloudWatch
98. Implementing AWS EventBridge for Event-Driven Architectures
99. Managing Hybrid Environments with AWS Outposts and VMware Cloud
100. Preparing for AWS Certifications: DevOps Engineer – Professional Exam