In today’s tech-driven job market, cloud computing expertise is no longer optional—it’s a fundamental skill that employers actively seek. Whether you’re a seasoned IT professional or a tech beginner, getting certified in Amazon Web Services (AWS) is one of the most effective ways to boost your cloud career. But certification alone isn’t enough. To truly excel, hands-on experience with real AWS environments is essential.

This article outlines some of the most impactful and popular AWS hands-on labs that not only reinforce theoretical concepts but also prepare you for real-world challenges in the cloud.

Exploring AWS Certification Tracks for Cloud Career Advancement

Amazon Web Services (AWS) has established itself as a cornerstone in cloud computing, offering a comprehensive suite of services that support scalable, reliable, and cost-effective digital solutions. To help professionals validate their expertise and showcase their technical capabilities, AWS provides a structured certification program. These credentials serve as benchmarks of proficiency in cloud architecture, development, operations, and foundational cloud concepts.

With businesses increasingly migrating to cloud platforms, AWS certifications are more than just accolades—they are strategic tools for career progression and technical validation. Whether you’re just entering the field or already possess hands-on experience, there’s a certification tailored to your path.

Foundational Knowledge With the AWS Cloud Practitioner Credential

The entry point into the AWS certification journey is the AWS Certified Cloud Practitioner. This credential is designed for individuals seeking a general understanding of cloud computing concepts and the core AWS services that support business use cases.

It’s an ideal choice for professionals in sales, marketing, project management, or entry-level tech roles who need to grasp the fundamentals of AWS without diving deep into technical intricacies. Topics covered include the AWS shared responsibility model, billing and pricing structures, basic security practices, and global infrastructure overview.

This certification sets the groundwork for more advanced paths and is especially useful for those involved in cloud-related decision-making, even if they are not directly involved in implementation or development.

Architecting Scalable Solutions: AWS Solutions Architect – Associate

For those aiming to build robust, fault-tolerant architectures on the AWS platform, the AWS Certified Solutions Architect – Associate certification provides critical validation. This certification is targeted at individuals with hands-on experience designing distributed systems that are both scalable and resilient.

The exam focuses on real-world use cases including selecting appropriate AWS services based on technical requirements, estimating costs, implementing secure applications, and optimizing cloud performance. Professionals pursuing this certification often work in solution design, infrastructure strategy, or technical leadership roles within cloud-native or hybrid environments.

This certification is widely recognized and often considered a gateway to more senior-level roles in cloud architecture and infrastructure strategy.

Application Development in the Cloud: AWS Developer – Associate

The AWS Certified Developer – Associate certification is tailored for software developers who build and maintain cloud-native applications using AWS services. It emphasizes a deep understanding of core AWS services, application lifecycle management, and the practical application of development tools such as the AWS SDKs, CLI, and CI/CD pipelines.

Candidates are expected to demonstrate competence in writing code that interacts with AWS services, deploying applications using Elastic Beanstalk or Lambda, and managing cloud-native APIs. This certification is highly relevant for DevOps engineers, backend developers, and anyone involved in building serverless or containerized applications on the AWS platform.

It validates not only technical fluency but also an ability to innovate within cloud environments using modern development methodologies.

Operational Excellence: AWS SysOps Administrator – Associate

Designed for professionals managing day-to-day AWS environments, the AWS Certified SysOps Administrator – Associate certification targets system administrators and IT operations personnel. This credential validates expertise in deploying, managing, and operating scalable, highly available systems on AWS.

It covers topics such as monitoring and reporting, automation through infrastructure as code (IaC), incident response, and compliance controls. Candidates should be comfortable working with services like Amazon CloudWatch, AWS Config, and Systems Manager.

This certification requires a firm grasp of how to implement operational best practices, making it ideal for professionals overseeing cloud infrastructure, ensuring performance reliability, and maintaining high service availability.

Why AWS Certifications Matter in Today’s Tech Ecosystem

The AWS certification paths are not just about passing exams—they represent a commitment to continuous improvement and alignment with industry standards. Each certification opens new opportunities for career advancement, salary growth, and enhanced credibility across diverse sectors, from finance and healthcare to gaming and logistics.

Employers actively seek certified AWS professionals to ensure their teams are equipped with validated skills and a thorough understanding of AWS architecture, deployment, and management strategies. Holding an AWS credential can differentiate you in a crowded job market and signal a dedication to cloud excellence.

Strategize Your Certification Journey With Resources Like Exam Labs

To effectively prepare for AWS certifications, leveraging high-quality learning platforms such as Exam Labs can significantly improve outcomes. These platforms provide updated practice exams, simulation environments, and targeted study guides that align with AWS’s evolving certification standards.

Using Exam Labs in combination with AWS’s official training resources ensures a well-rounded preparation approach, helping candidates not only pass the exams but also deeply internalize concepts for real-world application.

AWS certifications serve as stepping stones in a cloud professional’s journey, validating technical expertise and expanding career potential. Whether you’re just starting or aiming to specialize in architecture, development, or operations, these credentials offer structured pathways to mastery.

In a world where cloud technologies are integral to innovation, an AWS certification is more than a technical achievement—it is a strategic investment in your future as a cloud expert.

Top AWS Hands-On Labs to Build Real Skills

Here’s a curated list of popular AWS labs that can accelerate your learning and practical understanding of key cloud services:

Deploying Internet Access for Private Subnets Using Terraform and NAT Gateways

In modern cloud architecture, securing workloads while maintaining required internet connectivity is a fundamental requirement. Amazon Web Services (AWS) provides Network Address Translation (NAT) Gateways to facilitate outbound internet traffic for instances residing in private subnets. Using Terraform, this process can be automated efficiently, ensuring repeatability, scalability, and infrastructure consistency.

This hands-on guide provides a comprehensive walkthrough of configuring a NAT Gateway using Terraform, enabling internet access for instances without exposing them directly to public networks.

Overview of the Environment Configuration

Before implementing the NAT Gateway, it is essential to establish a structured AWS environment that includes the necessary networking components. This infrastructure-as-code approach ensures a reproducible deployment that can be versioned and maintained easily.

Key Environment Components:

• A custom Virtual Private Cloud (VPC)

• Public and private subnets across availability zones

• Route tables for directing traffic

• A NAT Gateway in the public subnet

• Public and private EC2 instances for validation

• Properly associated security groups and internet gateway

Each of these elements will be defined within Terraform modules or resources, using clean and modular code practices.

Step 1: Initialize Your AWS Environment and Key Pairs

Begin by generating key pairs to allow SSH access to your EC2 instances. Use the AWS Management Console or AWS CLI to create your key pair, and store the private key securely on your local machine. The name of the key will be referenced in your Terraform configuration.

Also, ensure your AWS credentials are configured correctly. You can do this by setting up the AWS CLI using aws configure or by defining credentials in Terraform using provider blocks.

provider “aws” {

  region = “us-west-2”

}

Step 2: Define Variables and Provision the Core Infrastructure

Terraform variables allow for flexible and reusable infrastructure definitions. Create a variables.tf file to define parameters such as VPC CIDR blocks, subnet ranges, and instance types.

Example VPC and Subnet Configuration:

resource “aws_vpc” “main” {

  cidr_block = var.vpc_cidr

  enable_dns_support = true

  enable_dns_hostnames = true

}

 

resource “aws_subnet” “public” {

  vpc_id            = aws_vpc.main.id

  cidr_block        = var.public_subnet_cidr

  map_public_ip_on_launch = true

  availability_zone = “us-west-2a”

}

 

resource “aws_subnet” “private” {

  vpc_id            = aws_vpc.main.id

  cidr_block        = var.private_subnet_cidr

  availability_zone = “us-west-2a”

}

Next, define your internet gateway and route tables for the public subnet.

Step 3: Create and Configure NAT Gateway

A NAT Gateway enables private subnet instances to access the internet for updates, patches, or outbound APIs, without exposing them directly.

Allocate an Elastic IP:

hcl

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resource “aws_eip” “nat” {

  vpc = true

}

 

Launch NAT Gateway in the Public Subnet:

hcl

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resource “aws_nat_gateway” “gw” {

  allocation_id = aws_eip.nat.id

  subnet_id     = aws_subnet.public.id

}

Route Table for Private Subnet:

Configure a route table that directs all outbound traffic (0.0.0.0/0) from the private subnet to the NAT Gateway.

resource “aws_route_table” “private” {

  vpc_id = aws_vpc.main.id

}

resource “aws_route” “private_nat” {

  route_table_id         = aws_route_table.private.id

  destination_cidr_block = “0.0.0.0/0”

  nat_gateway_id         = aws_nat_gateway.gw.id

}

resource “aws_route_table_association” “private” {

  subnet_id      = aws_subnet.private.id

  route_table_id = aws_route_table.private.id

}

Step 4: Deploy EC2 Instances for Validation

To verify connectivity, deploy two EC2 instances—one in the public subnet and another in the private subnet.

The instance in the public subnet will be accessible via SSH, while the instance in the private subnet will only access the internet through the NAT Gateway.

Define your EC2 resources:

resource “aws_instance” “public” {

  ami           = var.ami_id

  instance_type = var.instance_type

  subnet_id     = aws_subnet.public.id

  key_name      = var.key_name

  associate_public_ip_address = true

}

resource “aws_instance” “private” {

  ami           = var.ami_id

  instance_type = var.instance_type

  subnet_id     = aws_subnet.private.id

  key_name      = var.key_name

}

Ensure the appropriate security groups are in place, allowing SSH and ICMP for connectivity tests.

Step 5: Validate Internet Access From the Private Subnet

Once deployed, SSH into the public instance using your private key. From there, establish an internal SSH connection to the private instance. Use tools like curl or ping to validate external internet access from the private EC2 instance. If successful, the NAT Gateway is functioning correctly.

Step 6: Clean Up and Tear Down Resources

After validation, it’s important to destroy the created resources to avoid unnecessary charges. Use the following Terraform command to remove all infrastructure:

terraform destroy

This ensures you leave no residual components such as EIPs, which can incur charges if left unused.

Implementing a NAT Gateway using Terraform is a powerful way to control and secure internet traffic for private instances. It provides a repeatable blueprint for teams managing scalable cloud infrastructure. By separating public-facing and internal workloads, and managing connectivity via automation, you enhance both security and performance.

This strategy is a cornerstone of well-architected AWS environments and is vital for any professional aiming to master cloud networking concepts. Whether you’re building production workloads or learning cloud architecture fundamentals, this approach offers practical insights into secure and efficient design principles.

Creating a Cloud-Connected Flutter CRUD Application With AWS Amplify

Developing modern mobile applications requires a seamless integration between the frontend and robust cloud services. AWS Amplify provides a comprehensive suite of tools that allow developers to build scalable, secure, and responsive applications quickly. In this hands-on guide, you’ll explore the process of constructing a Flutter-based Todo application integrated with Amplify DataStore for real-time and offline-capable CRUD operations.

This walkthrough is ideal for developers looking to bridge mobile development with cloud infrastructure without manually configuring complex backend systems.

Preparing Your Environment for Flutter and Amplify Development

Before initiating the application, ensure that your development environment includes the necessary tooling for both Flutter and AWS Amplify. Begin by installing the following:

• Flutter SDK (latest stable release)

• AWS Amplify CLI

• Node.js (for Amplify CLI support)

• An IDE such as Visual Studio Code or Android Studio

Once installed, configure the Amplify CLI using:

amplify configure

This command will guide you through setting up your AWS credentials and region preferences.

Step 1: Initialize Your Flutter Project and Amplify Backend

Start by creating a new Flutter application using:

flutter create todo_flutter_amplify

cd todo_flutter_amplify

Then, initialize Amplify within the project directory:

amplify init

You’ll be prompted to select a frontend framework. Choose “Flutter” and proceed with the default options or customize based on your environment.

Step 2: Define Your Data Models With Amplify DataStore

Amplify DataStore enables structured data modeling and storage using a GraphQL-based schema definition.

Create a schema file named schema.graphql in the amplify/backend/api/ directory:

type Todo @model {

  id: ID!

  title: String!

  isComplete: Boolean!

  createdAt: AWSDateTime

}

Generate the necessary Dart classes by executing:

amplify codegen models

This command translates your GraphQL schema into platform-native models used in your Flutter app.

Step 3: Configure Authentication and Real-Time Sync

To secure your application and enable user-level data access, add authentication with:

amplify add auth

Choose the default configuration or tailor it with multi-factor authentication, user groups, or social providers.

Afterward, enable real-time sync and offline access:

amplify add api

# Choose GraphQL and select the existing schema

Push all changes to AWS to provision the backend:

amplify push

This command deploys your API, authentication module, and database resources to the cloud.

Step 4: Build Flutter UI and Implement CRUD Operations

Import the necessary Amplify libraries into your Flutter application:

import ‘package:amplify_flutter/amplify_flutter.dart’;

import ‘package:amplify_datastore/amplify_datastore.dart’;

import ‘models/Todo.dart’;

Create New Todo Items:

Future<void> addTodo(String title) async {

  final todo = Todo(title: title, isComplete: false);

  await Amplify.DataStore.save(todo);

}

Read Todos:

Future<List<Todo>> fetchTodos() async {

  return await Amplify.DataStore.query(Todo.classType);

}

Update an Existing Todo:

Future<void> updateTodo(Todo item) async {

  final updatedItem = item.copyWith(isComplete: !item.isComplete);

  await Amplify.DataStore.save(updatedItem);

}

Delete a Todo:

 

Future<void> deleteTodo(Todo item) async {

  await Amplify.DataStore.delete(item);

}

These operations interact with a local store and sync automatically with the cloud when connectivity is available.

Step 5: Test, Deploy, and Monitor Your Application

After implementing the logic, run the application on an emulator or device:

flutter run

Create, view, update, and delete todo items. Monitor real-time changes and ensure data syncs correctly between local and cloud storage.

To view your cloud resources and test the backend, use the AWS Console or Amplify Admin UI. These tools provide dashboards for API monitoring, authentication management, and data visualization.

Step 6: Clean Up AWS Resources

Once you’ve completed development and testing, you should delete unused AWS resources to prevent unnecessary charges:

amplify delete

This command deprovisions all services associated with your Amplify app, including APIs, authentication modules, and databases.

By combining Flutter’s UI toolkit with AWS Amplify’s powerful backend-as-a-service offerings, you can build fully integrated cloud applications that are both scalable and user-friendly. From defining data models to deploying authentication and syncing data in real time, this workflow empowers developers to focus on features rather than infrastructure.

This hands-on project not only strengthens your understanding of full-stack mobile development but also prepares you for building production-ready apps that leverage cloud-native architectures.

Quickstart Guide: Running Docker on an AWS EC2 Instance

Docker has revolutionized how applications are developed, packaged, and deployed across environments. Running Docker containers on Amazon EC2 offers developers flexibility and control, especially when prototyping, testing, or hosting lightweight microservices.

This step-by-step tutorial introduces the essential process of setting up Docker on an AWS EC2 instance, deploying a containerized application, and verifying its functionality. It serves as an excellent starting point for beginners who want hands-on experience with containerization in a cloud environment.

Step 1: Provision and Access Your EC2 Instance

Begin by launching an Amazon EC2 virtual machine using the AWS Management Console or AWS CLI.

Key Configuration Points:

• Choose Amazon Linux 2 as the base image (AMI) for compatibility.

• Select a t2.micro instance (or larger, depending on your needs).

• Create or use an existing key pair to allow secure SSH access.

• Ensure your security group allows inbound SSH (port 22) and, later, HTTP (port 80) access if your container serves a web app.

Once launched, connect to your EC2 instance via terminal:

bash

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ssh -i /path/to/your-key.pem ec2-user@your-ec2-public-ip

 

Replace /path/to/your-key.pem with the actual path to your key file and your-ec2-public-ip with your EC2 instance’s public IP address.

Step 2: Install and Start Docker on EC2

After logging into your instance, you need to install Docker. Amazon Linux 2 provides easy access to Docker through its package manager.

Execute the Following Commands:

bash

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sudo yum update -y

sudo amazon-linux-extras install docker -y

sudo service docker start

sudo usermod -a -G docker ec2-user

 

To apply the group changes, log out and reconnect via SSH. Then, verify Docker is working:

docker version

This confirms both the Docker client and daemon are running correctly on your instance.

Step 3: Deploy and Test a Sample Docker Container

With Docker installed and ready, the next step is to launch a containerized application to validate the setup.

Start by pulling a test image from Docker Hub:

docker run -d -p 80:80 nginx

This command runs the NGINX web server in detached mode and maps container port 80 to the host’s port 80, making it accessible via the public IP of your EC2 instance.

Test the Container:

Open a web browser and navigate to:

http://your-ec2-public-ip

You should see the default NGINX welcome page, confirming that your container is running and publicly accessible.

Step 4: Explore Container Operations

Experiment with Docker commands to deepen your understanding of container operations.

• List running containers:

docker ps

 

• Stop a container:

docker stop <container-id>

 

• Remove a container:

docker rm <container-id>

• Remove an image:

docker rmi nginx

 

These commands help you manage container lifecycles and understand the basic operations involved in maintaining Docker-based environments.

Deploying Docker on AWS EC2 is a practical and powerful way to learn the fundamentals of containerized development. With just a few commands, you can spin up virtual machines, run isolated applications, and experiment with real-world cloud scenarios.

This experience not only introduces you to container workflows but also lays the groundwork for more advanced topics like orchestration with Kubernetes, CI/CD integration, and microservice architectures.

Ready to go further? Consider integrating Amazon ECS or AWS Fargate for managed container hosting, or automate deployments using Terraform and CI pipelines.

Design and Deploy a Scalable Feedback Application Using Hybrid Cloud Architecture

Modern web applications often demand a mix of high availability, elasticity, and efficient data handling. To meet these expectations, cloud architects increasingly adopt a hybrid architecture approach—leveraging both serverless and server-based services in a unified system. This guide walks you through building a feedback application that enables users to submit text messages and image files through a highly scalable and cloud-optimized infrastructure on AWS.

You’ll learn how to integrate managed services like S3, API Gateway, Lambda, and DynamoDB with traditional compute resources such as EC2 instances behind a load balancer with auto scaling. The deployment process is streamlined using AWS DevOps tools like CodePipeline and CodeDeploy, ensuring seamless updates and continuous delivery.

Overview of the Architecture

The application is designed with a dual-purpose backend—handling media uploads through serverless services and maintaining core application logic on EC2. This hybrid setup provides flexibility, cost-efficiency, and resilience.

Core Components:

• Amazon S3 for storing user-submitted images

• Amazon DynamoDB for persisting feedback messages and metadata

• AWS Lambda integrated with API Gateway for uploading content

• Amazon EC2 behind a Load Balancer for handling main application logic

• Auto Scaling Groups to maintain application performance under load

• AWS CodePipeline and CodeDeploy for automated CI/CD workflows

Step 1: Configure S3 and DynamoDB for Media and Data Management

Start by creating an S3 bucket for storing uploaded image files. Ensure that it has proper permissions and lifecycle policies to optimize storage cost.

aws s3api create-bucket –bucket feedback-app-images –region us-west-2

Next, set up a DynamoDB table for storing feedback entries. The table should include attributes for feedback ID, user input, image link, timestamp, and metadata.

aws dynamodb create-table \

  –table-name FeedbackData \

  –attribute-definitions AttributeName=FeedbackID,AttributeType=S \

  –key-schema AttributeName=FeedbackID,KeyType=HASH \

  –billing-mode PAY_PER_REQUEST

Step 2: Create Serverless APIs with Lambda and API Gateway

For the image upload and feedback submission endpoints, use AWS Lambda functions to interact with both S3 and DynamoDB. Define Lambda functions in Python or Node.js, with IAM roles that allow access to the required services.

# Pseudocode for image upload handler

def lambda_handler(event, context):

    decoded_image = base64.b64decode(event[‘body’][‘image’])

    s3.put_object(Bucket=’feedback-app-images’, Key=’image-id.jpg’, Body=decoded_image)

    return {‘statusCode’: 200, ‘body’: ‘Image uploaded successfully’}

Use Amazon API Gateway to expose these Lambda functions as HTTPS endpoints. Create REST APIs or HTTP APIs depending on the use case, and enable CORS if your frontend app is hosted on a separate domain.

Step 3: Deploy EC2 Instances with Load Balancing and Auto Scaling

To handle application logic, use an EC2-based architecture backed by a load balancer and Auto Scaling Group.

Launch Configuration and Auto Scaling:

• Create a launch template that defines your EC2 instance AMI, security groups, and startup scripts

• Attach an Application Load Balancer (ALB) for routing requests

• Define scaling policies based on CPU usage or network traffic

aws autoscaling create-auto-scaling-group \

  –auto-scaling-group-name FeedbackAppASG \

  –launch-template LaunchTemplateName=FeedbackAppTemplate \

  –min-size 2 –max-size 5 \

  –vpc-zone-identifier subnet-xyz123

This ensures your application can dynamically adjust to user demand while maintaining availability.

Step 4: Automate CI/CD With CodePipeline and CodeDeploy

For continuous integration and deployment, configure AWS CodePipeline to orchestrate your build and release process. Connect the pipeline to a source control repository such as GitHub or AWS CodeCommit.

CodePipeline Workflow:

• Source Stage: Pulls code from a GitHub branch or CodeCommit repo

• Build Stage: Uses AWS CodeBuild to compile and package the application

• Deploy Stage: Triggers AWS CodeDeploy to push changes to EC2 instances or Lambda functions

Define an appspec.yml file for EC2 deployments that handles lifecycle hooks such as BeforeInstall, AfterInstall, and ApplicationStart.

version: 0.0

os: linux

files:

  – source: /

    destination: /var/www/feedbackapp

hooks:

  AfterInstall:

    – location: scripts/restart.sh

      timeout: 180

This automation minimizes manual overhead and speeds up iteration cycles.

Step 5: Test and Validate Application Behavior

Once deployed, test your application end-to-end:

• Upload feedback and images via the API Gateway endpoints

• Verify media appears in the S3 bucket and data in DynamoDB

• Check application response through EC2 load-balanced endpoints

• Trigger auto scaling by simulating load using tools like Apache JMeter or Artillery

Monitor logs using CloudWatch and ensure that all components are reporting health metrics correctly.

Step 6: Resource Cleanup

After the lab or project is complete, remember to deprovision unused AWS resources to avoid incurring charges:

• Terminate EC2 instances

• Delete S3 buckets and DynamoDB tables

• Remove Lambda functions and API Gateway endpoints

• Delete the CodePipeline and CodeDeploy configurations

Use the AWS Console or AWS CLI to systematically delete each component.

By combining serverless agility with the control of EC2-based compute resources, you unlock a hybrid approach that scales both vertically and horizontally. This architectural pattern empowers developers to optimize for performance, cost, and security—all while leveraging the full breadth of AWS services.

Whether you’re building enterprise-grade systems or experimenting with cloud-native design patterns, this feedback application serves as a foundational blueprint for mixed-infrastructure web development.

Implement Automated EBS Snapshots with Terraform, CloudWatch, SNS, and Lambda

Data durability and recovery are critical elements in cloud operations, especially for systems running on Amazon EC2. To safeguard against data loss, it’s important to establish an automated snapshot mechanism for your Elastic Block Store (EBS) volumes. In this project, you will learn how to orchestrate recurring EBS snapshots using a fully automated and scalable infrastructure defined with Terraform.

This hands-on deployment uses AWS CloudWatch Events (now called EventBridge), Simple Notification Service (SNS), and AWS Lambda—all provisioned and managed using infrastructure as code (IaC) via Terraform. By the end, you’ll have a robust system for automatically backing up EBS volumes on a scheduled basis, reducing the risk of data unavailability and improving compliance with backup policies.

Architectural Components and Workflow

This automated snapshot solution integrates the following AWS services:

• EC2 Instance: Hosts the EBS volume to be backed up.

• Lambda Function: Performs snapshot creation using the AWS SDK.

• SNS Topic: Distributes notifications about snapshot activity.

• CloudWatch Rule: Acts as a time-based trigger for the Lambda function.

• IAM Roles and Policies: Control access between services.

Step 1: Launch an EC2 Instance with an EBS Volume

Begin by defining a Terraform configuration that provisions an EC2 instance along with a separately attached EBS volume.

Terraform Snippet for EC2 and EBS:

resource “aws_instance” “web” {

  ami           = “ami-0c55b159cbfafe1f0”  # Replace with a region-specific AMI

  instance_type = “t2.micro”

  availability_zone = “us-west-2a”

  tags = {

    Name = “SnapshotInstance”

  }

}

resource “aws_ebs_volume” “data” {

  availability_zone = aws_instance.web.availability_zone

  size              = 8

  tags = {

    Name = “DataVolume”

  }

}

 

resource “aws_volume_attachment” “ebs_att” {

  device_name = “/dev/sdh”

  volume_id   = aws_ebs_volume.data.id

  instance_id = aws_instance.web.id

}

Step 2: Create an SNS Topic for Notifications

Next, use Terraform to define an SNS topic and optionally add email or Lambda subscriptions. This provides visibility into snapshot creation activities.

resource “aws_sns_topic” “snapshot_alerts” {

  name = “ebs-snapshot-notifications”

}

 

Step 3: Write a Lambda Function to Create EBS Snapshots

Develop a Lambda function in Python that receives an event (from CloudWatch) and triggers the snapshot of the associated volume.

Python Code for Lambda:

import boto3

import datetime

def lambda_handler(event, context):

    ec2 = boto3.client(‘ec2’)

    volume_id = ‘vol-xxxxxxxx’  # Replace with actual volume ID

    timestamp = datetime.datetime.utcnow().strftime(‘%Y-%m-%d_%H-%M-%S’)

    description = f”Automated snapshot from Lambda at {timestamp}”

    ec2.create_snapshot(VolumeId=volume_id, Description=description)

    return {‘status’: ‘Snapshot initiated’}

Package this script and upload it to an S3 bucket or zip it locally for Lambda deployment.

Terraform to Deploy Lambda:

resource “aws_lambda_function” “ebs_snapshot” {

  filename         = “lambda_snapshot.zip”

  function_name    = “EBSVolumeSnapshotter”

  runtime          = “python3.9”

  handler          = “lambda_function.lambda_handler”

  role             = aws_iam_role.lambda_exec.arn

}

Step 4: Set Up IAM Roles for Lambda Access

You’ll need to grant the Lambda function permissions to create EBS snapshots and write logs to CloudWatch:

resource “aws_iam_role” “lambda_exec” {

  name = “LambdaSnapshotExecution”

 

  assume_role_policy = jsonencode({

    Version = “2012-10-17”,

    Statement = [{

      Action = “sts:AssumeRole”,

      Principal = {

        Service = “lambda.amazonaws.com”

      },

      Effect = “Allow”

    }]

  })

}

 

resource “aws_iam_policy_attachment” “snapshot_policy” {

  name       = “lambda-snapshot-policy”

  roles      = [aws_iam_role.lambda_exec.name]

  policy_arn = “arn:aws:iam::aws:policy/AmazonEC2FullAccess”

}

Step 5: Trigger Lambda Using CloudWatch Events

Configure an EventBridge (CloudWatch) rule that triggers the Lambda function at regular intervals (e.g., daily at midnight UTC).

resource “aws_cloudwatch_event_rule” “snapshot_schedule” {

  name                = “DailySnapshotRule”

  schedule_expression = “cron(0 0 * * ? *)”

}

resource “aws_cloudwatch_event_target” “trigger_lambda” {

  rule      = aws_cloudwatch_event_rule.snapshot_schedule.name

  target_id = “InvokeSnapshotLambda”

  arn       = aws_lambda_function.ebs_snapshot.arn

}

Grant CloudWatch permissions to invoke your Lambda:

resource “aws_lambda_permission” “allow_cloudwatch” {

  statement_id  = “AllowExecutionFromCloudWatch”

  action        = “lambda:InvokeFunction”

  function_name = aws_lambda_function.ebs_snapshot.function_name

  principal     = “events.amazonaws.com”

  source_arn    = aws_cloudwatch_event_rule.snapshot_schedule.arn

}

Step 6: Validate and Observe the Snapshot Automation

Deploy the full Terraform configuration:

terraform init

terraform apply

Wait for the scheduled time or manually trigger the Lambda function from the AWS Console to test its functionality. Check the EC2 console under “Snapshots” to confirm creation.

Monitor logs through Amazon CloudWatch Logs for Lambda function execution details.

Step 7: Cleanup and Resource Decommissioning

When the lab is complete, clean up to avoid ongoing charges:

terraform destroy

Ensure all resources—EC2, EBS, Lambda, SNS, and CloudWatch rules—are successfully removed.

By automating EBS snapshot creation using Terraform and native AWS services, you’ve created a system that not only preserves data integrity but also enhances operational efficiency. This infrastructure serves as a template for broader automation initiatives, including disaster recovery, compliance enforcement, and lifecycle management.

You can extend this solution to support tagging, snapshot retention policies, or multi-volume environments for even greater robustness and customization.

Deploy EC2 Instances Dynamically Using AWS Lambda and Terraform

Event-driven compute is revolutionizing cloud infrastructure workflows. With AWS Lambda, you can trigger complex provisioning tasks, including launching EC2 instances, without maintaining always-on resources. This guide demonstrates how to deploy EC2 instances dynamically in response to events, using a Lambda function configured and managed entirely via Terraform.

This hands-on project is ideal for infrastructure engineers, DevOps professionals, or cloud architects who want to learn how to combine the declarative power of Terraform with the event-centric paradigm of AWS Lambda for dynamic compute provisioning.

Project Objective and Architecture Overview

You’ll build a Lambda function that launches EC2 instances programmatically, and define the entire architecture using Terraform. This structure promotes clean, repeatable deployments and can be extended to auto-remediation, on-demand environments, or scalable backend systems.

Key Components:

• Terraform-managed Lambda function (Node.js or Python)

• IAM roles with EC2 provisioning capabilities

• Lambda execution permissions

• Test event to validate real-time EC2 instance creation

Step 1: Define IAM Role for Lambda Execution

Start by provisioning an IAM role that grants the Lambda function permissions to interact with the EC2 API. This role is essential for managing compute resources programmatically.

resource “aws_iam_role” “lambda_ec2_role” {

  name = “LambdaEC2LaunchRole”

  assume_role_policy = jsonencode({

    Version = “2012-10-17”,

    Statement = [{

      Action = “sts:AssumeRole”,

      Principal = {

        Service = “lambda.amazonaws.com”

      },

      Effect = “Allow”

    }]

  })

}

resource “aws_iam_role_policy_attachment” “attach_ec2_policy” {

  role       = aws_iam_role.lambda_ec2_role.name

  policy_arn = “arn:aws:iam::aws:policy/AmazonEC2FullAccess”

}

Step 2: Create a Lambda Function to Launch EC2

Develop the function logic in Python or Node.js. Here’s a sample Python script to launch an EC2 instance:

import boto3

def lambda_handler(event, context):

    ec2 = boto3.client(‘ec2’)

    ec2.run_instances(

        ImageId=’ami-0c55b159cbfafe1f0′,  # Replace with a region-specific AMI

        InstanceType=’t2.micro’,

        MinCount=1,

        MaxCount=1,

        KeyName=’your-key-pair’,         # Ensure this key exists in the region

        SecurityGroupIds=[‘sg-0123456789abcdef0’],  # Replace with your SG ID

        SubnetId=’subnet-0123456789abcdef0′         # Replace with your subnet ID

    )

    return {‘status’: ‘EC2 instance launched’}

Package the script in a zip archive named lambda_ec2.zip.

Step 3: Provision Lambda with Terraform

Define the Lambda resource in Terraform and link it to the IAM role:

resource “aws_lambda_function” “ec2_launcher” {

  filename         = “lambda_ec2.zip”

  function_name    = “LaunchEC2Instance”

  role             = aws_iam_role.lambda_ec2_role.arn

  handler          = “lambda_function.lambda_handler”

  runtime          = “python3.9”

  timeout          = 30

}

Make sure the zip file and handler path align with your Lambda source code structure.

Step 4: Test Lambda Execution Manually

After deployment, test the Lambda function either through the AWS Console or programmatically with a test payload. Terraform does not trigger the function automatically, so manual execution ensures that your logic behaves as expected.

If the EC2 instance launches successfully, verify it by navigating to the EC2 Dashboard. Check that the instance has the correct configuration and resides in the designated subnet with the specified key pair.

Step 5: Optional – Automate Lambda Invocation via EventBridge

To turn this into a fully event-driven workflow, create an EventBridge rule to trigger the Lambda function on a custom schedule or based on specific AWS events.

resource “aws_cloudwatch_event_rule” “lambda_trigger” {

  name                = “EC2LaunchSchedule”

  schedule_expression = “rate(1 day)”

}

resource “aws_cloudwatch_event_target” “invoke_lambda” {

  rule      = aws_cloudwatch_event_rule.lambda_trigger.name

  target_id = “LaunchEC2”

  arn       = aws_lambda_function.ec2_launcher.arn

}

resource “aws_lambda_permission” “allow_eventbridge” {

  statement_id  = “AllowEventBridgeInvocation”

  action        = “lambda:InvokeFunction”

  function_name = aws_lambda_function.ec2_launcher.function_name

  principal     = “events.amazonaws.com”

  source_arn    = aws_cloudwatch_event_rule.lambda_trigger.arn

}

This setup enables automatic EC2 provisioning based on your defined schedule.

Step 6: Validate Provisioning and Review Logs

Use the AWS EC2 console to confirm instance creation. Review the Lambda execution logs in CloudWatch for visibility into the function’s success or failure.

Check for the following:

• Correct instance type and AMI

• Proper subnet and security group

• Lambda execution status in CloudWatch Logs

• Error messages if instance creation failed

Step 7: Teardown Resources with Terraform

When testing is complete, clean up all resources to avoid unnecessary charges:

terraform destroy

Ensure all EC2, Lambda, IAM, and EventBridge resources are fully removed.

This lab demonstrates a compelling use case of blending declarative infrastructure provisioning with event-driven automation. Launching EC2 instances through Lambda offers unmatched flexibility, allowing you to spin up environments only when needed—perfect for dynamic workloads, auto-remediation, or on-demand development sandboxes.

Create Docker Images Using Dockerfile

Understand how to define a Dockerfile and build images directly on an AWS-hosted EC2 instance.

Main Activities:

• Set up Docker on Linux EC2 instance.

• Write a Dockerfile and build an image.

• Run and test the resulting container.

Host a Static Web App Using a Serverless Stack

This lab walks you through creating a fully serverless feedback app, leveraging only AWS managed services.

Lab Components:

• DynamoDB for data storage.

• S3 for hosting static content and storing images.

• API Gateway + Lambda for backend logic.

• Deploy, test, and validate your architecture.

Master Docker Compose in a Cloud Environment

Learn to orchestrate multi-container Docker applications using Docker Compose on AWS EC2 instances.

Steps to Practice:

• Install Docker Compose.

• Create a docker-compose.yml file.

• Launch multi-container applications and verify performance.

Secure APIs with Cognito User Pools

Protect your RESTful APIs using Amazon Cognito, ensuring access control and secure user authentication.

What You’ll Learn:

• Set up a user pool and create test accounts.

• Create a secure API using Lambda and API Gateway.

• Link Cognito as the authorizer.

• Test authenticated and unauthenticated access.

Final Thoughts: 

These hands-on AWS labs are excellent for building technical fluency and reinforcing concepts from certification study paths. Whether you’re preparing for foundational or professional-level credentials, interactive labs offer the most effective way to build confidence and real-world experience.

While many platforms offer cloud labs, it’s crucial to choose ones that are up-to-date, guided, and aligned with current AWS features. Examlabs offers a vast repository of such labs—each curated by cloud experts to help you learn by doing, not just watching.