Introduction.

Setting up a Kubernetes environment on AWS might seem daunting at first—but thanks to Amazon EKS (Elastic Kubernetes Service) and infrastructure-as-code tools like Terraform, it’s now easier, faster, and more reliable than ever. In this guide, we’ll walk you through the full process of provisioning a highly available EKS cluster and installing kubectl, the Kubernetes command-line tool, using Terraform. Whether you’re a DevOps engineer, a cloud developer, or simply someone exploring container orchestration, this tutorial will help you get a working EKS setup with minimal manual intervention.

Amazon EKS provides a managed Kubernetes service that takes care of control plane provisioning, scaling, and management, while still giving you the flexibility to configure worker nodes and networking. It is tightly integrated with other AWS services, including IAM for authentication, CloudWatch for logging, and VPC for networking. But despite its convenience, configuring EKS by hand can be time-consuming and error-prone—especially in production environments where repeatability and consistency are crucial.

This is where Terraform comes in. As an open-source infrastructure-as-code tool developed by HashiCorp, Terraform allows you to define cloud resources declaratively in configuration files. By using Terraform to manage EKS, you can version control your infrastructure, reuse code through modules, and automate cluster provisioning with confidence. You can also tear down and recreate environments quickly, making it ideal for both development and testing.

In this tutorial, we’ll start by configuring Terraform to work with AWS. We’ll create a VPC, subnets, and an EKS cluster using community-supported Terraform modules. Then we’ll install kubectl, configure it to communicate with our new cluster, and run basic commands to verify everything works. You’ll learn how to structure your Terraform files, how to manage variables and outputs, and how to avoid common pitfalls.

Even if you’re new to Kubernetes or Terraform, this guide is beginner-friendly and explains each concept along the way. We’ll also highlight best practices, such as using separate files for providers, variables, and outputs, and organizing your infrastructure into reusable modules. You’ll end up with a fully functional Kubernetes environment that’s cloud-native, scalable, and ready for deploying real-world applications.

So if you’re ready to stop clicking through the AWS console and start automating your infrastructure the DevOps way, let’s dive into creating an Amazon EKS cluster with Terraform and managing it effortlessly using kubectl.

Prerequisites

1. AWS CLI installed and configured (aws configure)
2. Terraform installed
3. IAM user/role with permissions for EKS
4. kubectl (installed via Terraform if needed)
5. An S3 bucket and DynamoDB table for Terraform state (recommended for production)

provider.tf

provider "aws" {
  region = var.aws_region
}

variables.tf

variable "cluster_name" {
  default = "my-eks-cluster"
}

variable "aws_region" {
  default = "us-west-2"
}

variable "node_instance_type" {
  default = "t3.medium"
}

variable "desired_capacity" {
  default = 2
}

variable "max_size" {
  default = 3
}

variable "min_size" {
  default = 1
}

main.tf

# Create VPC using module
module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "5.1.2"

  name = "eks-vpc"
  cidr = "10.0.0.0/16"

  azs             = ["${var.aws_region}a", "${var.aws_region}b"]
  private_subnets = ["10.0.1.0/24", "10.0.2.0/24"]
  public_subnets  = ["10.0.3.0/24", "10.0.4.0/24"]

  enable_nat_gateway = true
  single_nat_gateway = true
}

# Create EKS Cluster using module
module "eks" {
  source          = "terraform-aws-modules/eks/aws"
  version         = "20.8.3"

  cluster_name    = var.cluster_name
  cluster_version = "1.29"
  subnet_ids      = module.vpc.private_subnets
  vpc_id          = module.vpc.vpc_id

  eks_managed_node_groups = {
    default = {
      instance_types = [var.node_instance_type]
      min_size       = var.min_size
      max_size       = var.max_size
      desired_size   = var.desired_capacity
    }
  }

  enable_irsa = true
}

outputs.tf

output "cluster_endpoint" {
  value = module.eks.cluster_endpoint
}

output "kubeconfig" {
  value = module.eks.kubeconfig
  sensitive = true
}

Install kubectl Using Terraform (Optional)

If you want to install kubectl via Terraform, use the null_resource + local-exec provisioner:

resource "null_resource" "install_kubectl" {
  provisioner "local-exec" {
    command = <<EOT
      curl -o kubectl https://s3.us-west-2.amazonaws.com/amazon-eks/1.29.0/2024-03-27/bin/linux/amd64/kubectl
      chmod +x ./kubectl
      sudo mv ./kubectl /usr/local/bin/
    EOT
  }
}

Deploy Steps

terraform init
terraform apply

Conclusion.

In conclusion, provisioning an Amazon EKS cluster using Terraform and configuring kubectl is a powerful way to automate and simplify your Kubernetes infrastructure on AWS. With just a few well-structured configuration files, you can define, deploy, and manage a highly available Kubernetes environment that integrates seamlessly with the rest of your cloud ecosystem. This approach not only reduces manual errors but also makes your infrastructure reproducible, version-controlled, and easier to manage in the long term. Whether you’re building a development environment, staging cluster, or a full production setup, Terraform and kubectl together give you the control and flexibility needed to operate confidently at scale. As you grow more comfortable with this setup, you can extend it further—adding monitoring tools, CI/CD pipelines, and security policies—all within the same infrastructure-as-code workflow. By embracing automation and best practices early, you’re setting your Kubernetes journey on a solid and scalable foundation.

Introduction.

In today’s digital era, data has become one of the most valuable assets for businesses of all sizes. With the rise of cloud computing, particularly Amazon Web Services (AWS), storing vast amounts of data in the cloud has become more convenient than ever. Among AWS’s most popular services, Amazon S3 (Simple Storage Service) stands out as a widely used solution for storing objects and files. While this convenience is a game-changer, it also introduces significant risks—especially when it comes to the storage of sensitive data. Sensitive information such as personally identifiable information (PII), financial data, credentials, or proprietary business content may accidentally be left unprotected or misclassified in S3 buckets.

The consequences of exposing such data can be severe: data breaches, compliance violations, reputational damage, and financial penalties. Regulations such as GDPR, HIPAA, and CCPA demand strict protection of customer and business data. Yet, in large-scale cloud environments, it becomes increasingly difficult to maintain visibility into exactly what data resides where—and whether it’s at risk. That’s where Amazon Macie steps in.

Amazon Macie is a fully managed data security and data privacy service offered by AWS. It uses machine learning and pattern matching to automatically discover and classify sensitive data within S3 buckets. Macie helps organizations identify where their sensitive data lives, how it’s being accessed, and whether it’s properly protected. It not only scans and reports on the presence of sensitive information but also provides actionable insights that allow security teams to remediate issues before they become breaches.

In this challenge, we will walk through the process of using Amazon Macie to discover sensitive data stored in S3 buckets. Whether you’re a cloud security engineer, DevOps professional, or just an AWS learner, this guide is designed to provide hands-on experience with Macie and its powerful classification features. We will begin by setting up Macie in the AWS Management Console, configuring a classification job, and analyzing the results. This real-world challenge mirrors scenarios that professionals face when securing cloud environments, making it both an educational and practical exercise.

This walkthrough will demonstrate how easy it is to activate Macie, scan your environment, and interpret its findings. By the end of this challenge, you’ll gain a clear understanding of how Macie works, why it’s crucial for cloud security, and how it fits into a broader strategy for securing sensitive data in the cloud. You’ll also learn best practices for reducing data exposure risks, managing permissions, and staying compliant with regulatory standards.

So, whether you’re working in a regulated industry or simply want to improve your data visibility, this challenge is for you. Let’s take a deep dive into the world of Amazon Macie and learn how to uncover sensitive data hiding in plain sight. Get ready to enhance your AWS security skills through this guided, step-by-step challenge that simulates real-world cloud risk management scenarios. It’s time to shine a light on your data and ensure your S3 buckets are as secure as they should be.

Prerequisites

Before you begin:

• You must have an AWS account.
• The S3 bucket you want to scan must exist and contain data.
• You must have necessary IAM permissions (AmazonMacieFullAccess, AmazonS3ReadOnlyAccess, etc.)

Step-by-Step Process

Step 1: Enable Amazon Macie

1. Go to the AWS Management Console.
2. Navigate to Amazon Macie.
3. If not already enabled:
   • Click Enable Macie.
   • Macie will start discovering your S3 buckets and their metadata.

Step 2: Review S3 Buckets

1. After enabling Macie, go to the S3 Buckets section in the Macie dashboard.
2. Macie automatically lists your buckets along with:
   • Region
   • Number of objects
   • Total size
   • Public accessibility
   • Encryption status

Step 3: Create a Classification Job

1. Navigate to the Jobs section.
2. Click Create Job.
3. Name your job and add a description (optional).
4. Select the S3 buckets you want to scan.

Step 4: Configure Scope

1. Choose whether to scan:
   • All objects
   • Objects modified in the last X days
   • Specific folders (prefixes)
2. You can use object criteria filters if needed (e.g., file type, size).

Step 5: Set the Schedule

• Choose one of the following:
  • One-time job
  • Recurring job (daily, weekly, monthly)

Step 6: Choose Custom Data Identifiers (Optional)

1. Macie uses managed data identifiers (e.g., PII, financial data).
2. You can add custom identifiers using regex if you need to detect unique patterns (like internal employee IDs).

Step 7: Review and Create

1. Review your configuration.
2. Click Create Job.
3. Macie will start analyzing data based on your configuration.

Step 8: View Results

1. Go to the Jobs dashboard.
2. Click on your job name.
3. View results:
   • Number of sensitive items found
   • Categories (e.g., email addresses, credit card numbers)
   • Risk level
   • Object path (S3 key)

Step 9: Take Action

• Based on findings, consider:
  • Applying encryption
  • Adjusting bucket permissions
  • Removing sensitive data
  • Creating alerts via Amazon EventBridge or AWS Security Hub

Conclusion.

In conclusion, discovering and protecting sensitive data within your Amazon S3 buckets is no longer a daunting task, thanks to the capabilities of Amazon Macie. As cloud adoption continues to rise, so do the challenges of data visibility and compliance. This hands-on challenge has shown how Amazon Macie empowers you to automate the discovery of sensitive information, classify it effectively, and take swift action to secure it. From enabling the service to configuring classification jobs and analyzing results, each step in this workflow adds a critical layer of awareness and control over your cloud data assets. By using Macie proactively, organizations can reduce their risk of data exposure, meet regulatory requirements, and foster a culture of security-first cloud practices. Whether you’re securing a single S3 bucket or an enterprise-scale environment, Amazon Macie offers the intelligence and automation needed to protect what matters most. Make it a regular part of your cloud security toolkit—and stay one step ahead of potential threats.

Introduction.

In today’s digital age, even your neighborhood cafe needs more than just great coffee—it needs a reliable, secure, and scalable network infrastructure to support online orders, mobile payments, customer loyalty programs, and real-time inventory management. Small businesses like cafes are increasingly moving to the cloud to stay competitive, but cloud networking isn’t always as easy as brewing a fresh cup of espresso. That’s where this challenge begins: creating a Virtual Private Cloud (VPC) environment tailored specifically for a cafe. Whether you’re a beginner in cloud architecture or a professional seeking a practical use case, this challenge offers hands-on experience with designing and deploying a secure VPC on AWS. It’s more than just a theoretical exercise—it’s a real-world scenario that reflects the needs of modern small businesses.

Imagine a cafe that wants to launch a simple web app for customers to browse menus, place orders, and leave feedback. Behind the scenes, the app will need a web server exposed to the internet and a private database server protected from public access. How do you design a network to support this, keeping performance high and security tight? Enter the world of VPCs: a logically isolated section of the AWS cloud where you can launch and manage your resources. With subnets, route tables, security groups, and gateways at your disposal, the goal is to build a custom, secure, and cost-efficient networking environment.

This challenge guides you through each step—starting from creating the VPC itself, configuring public and private subnets, assigning routing rules, setting up internet and NAT gateways, and deploying EC2 instances to host the app and database. You’ll also learn how to apply security group rules to allow only necessary traffic, ensuring that the database remains shielded from the public while the web server stays accessible. Along the way, you’ll gain a deeper understanding of cloud network design principles and best practices.

The cafe scenario provides an approachable yet realistic challenge. It’s not just about clicking buttons in the AWS console—it’s about thinking like a cloud architect. How would you ensure high availability? What if the cafe expands and needs more servers? What if you need logging, monitoring, or VPN access later on? These considerations help build your skills beyond just basic setup and push you to design scalable infrastructure. Whether you’re prepping for an AWS certification, working on a personal project, or teaching cloud networking fundamentals, this challenge delivers value through practical experience.

By the end of this guide, you’ll have built a fully functional, production-grade VPC environment tailored for a small business. You’ll know how each component fits together, why it matters, and how to troubleshoot issues when they arise. Ready to get started? Let’s dive in and build a cloud network that’s as strong as your favorite dark roast—welcome to the VPC networking challenge for the cafe.

Step 1: Create the VPC

• Go to the AWS VPC dashboard.
• Click Create VPC > Choose VPC with public and private subnets (VPC Wizard) or custom VPC.
• Enter:
  • Name: CafeVPC
  • IPv4 CIDR block: 10.0.0.0/16
  • Enable DNS hostnames: ✅
  • Click Create VPC

Step 2: Create Subnets

Create two subnets:

• Public Subnet:
  • Name: Cafe-Public-Subnet
  • CIDR block: 10.0.1.0/24
  • Availability Zone: e.g., us-east-1a
• Private Subnet:
  • Name: Cafe-Private-Subnet
  • CIDR block: 10.0.2.0/24
  • Availability Zone: us-east-1a

Step 3: Create an Internet Gateway

• Go to VPC > Internet Gateways > Create Internet Gateway
• Name: CafeIGW
• Attach it to your CafeVPC

Step 4: Configure Route Tables

• Public Route Table:
  • Create a route table named Cafe-Public-RT
  • Add route: 0.0.0.0/0 → Target: Internet Gateway CafeIGW
  • Associate this route table with the Public Subnet
• Private Route Table:
  • Create a route table named Cafe-Private-RT
  • Initially, no route to the internet (optional: NAT in future)
  • Associate this with the Private Subnet

Step 5: Launch EC2 Instances

• Public Instance (Web Server):
  • Network: CafeVPC
  • Subnet: Cafe-Public-Subnet
  • Security Group: allow HTTP (80), HTTPS (443), and SSH (22)
  • Elastic IP: Allocate and associate to instance
• Private Instance (Database Server):
  • Network: CafeVPC
  • Subnet: Cafe-Private-Subnet
  • Security Group: allow MySQL (3306) from the Public Instance’s IP only

Step 6: Security Groups

• Web SG:
  • Inbound: allow HTTP (80), HTTPS (443), SSH (22) from 0.0.0.0/0
  • Outbound: allow all (default)
• DB SG:
  • Inbound: allow port 3306 only from Web SG
  • Outbound: allow all (default)

Step 7: Test Connectivity

• Connect via SSH to the web server using the Elastic IP.
• Ensure you can:
  • Access the web server from your browser.
  • The web server can reach the database (test via internal IP).

Optional: Add NAT Gateway (If Private Instance Needs Internet)

• Create NAT Gateway in Public Subnet.
• Add route 0.0.0.0/0 in Private Route Table via NAT Gateway.

Conclusion.

Creating a VPC networking environment for a cafe may seem like a small task, but it reflects the foundational skills needed to build secure, scalable cloud infrastructure for any modern business. Through this challenge, we’ve explored how to design a basic yet production-ready network architecture using AWS VPC, including public and private subnets, routing, internet access, and security best practices. From launching EC2 instances to configuring gateways and security groups, every step reinforced real-world cloud principles that apply far beyond just a cafe setup. More importantly, you’ve learned to think critically about network design, balancing accessibility with security and simplicity with scalability. Whether you’re just starting out or sharpening your skills for certification or client work, mastering these fundamentals prepares you for far more complex deployments in the future. So the next time you sip your coffee, remember—cloud architecture, like a good brew, is all about thoughtful preparation, careful execution, and consistent improvement. Keep experimenting, keep learning, and keep building. Your next challenge awaits.

Introduction.

In today’s rapidly evolving Internet of Things (IoT) landscape, the ability to efficiently process and respond to data generated by connected devices is critical for building smart applications and services. Amazon Web Services (AWS) provides a comprehensive suite of tools that allow developers to build scalable, secure, and responsive IoT solutions. One of the most powerful and widely used combinations in this toolkit is AWS IoT Core integrated with AWS Lambda. Together, they enable serverless, real-time data processing without the need to manage infrastructure manually.

AWS IoT Core is a managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices. It supports millions of devices and billions of messages, and it routes those messages to AWS endpoints and other devices reliably and securely. Lambda, on the other hand, is AWS’s event-driven compute service that lets you run code in response to triggers without provisioning or managing servers. Integrating AWS IoT Core with AWS Lambda allows developers to automatically process incoming messages from IoT devices using serverless functions, which can filter, transform, route, and store data in real-time.

The core idea is simple yet powerful: devices send messages to AWS IoT Core over MQTT, HTTP, or WebSockets. Once received, these messages are evaluated against rules defined in AWS IoT Rules Engine. If a rule condition is met, the message is passed to the target action, such as invoking a Lambda function. This process is entirely managed, scalable, and secure, making it ideal for handling high volumes of device messages with minimal operational overhead.

To implement this setup, a few foundational steps are required. First, you define your IoT devices in AWS, known as “Things,” and securely connect them using X.509 certificates. Next, you create a Lambda function that contains your logic for processing messages — for example, filtering data, storing records in DynamoDB, or sending alerts via SNS. Then, using the AWS IoT Rules Engine, you create a rule that listens to a specific MQTT topic and triggers the Lambda function when a message arrives on that topic. Permissions are handled via AWS Identity and Access Management (IAM), ensuring secure interaction between IoT Core and Lambda.

One major advantage of using Lambda is its scalability and pay-per-use model, which means you only pay for the compute time your function uses, making it highly cost-effective for IoT workloads that can be spiky or unpredictable. Furthermore, since Lambda supports multiple languages like Python, Node.js, Java, and others, developers have the flexibility to write processing logic in the language of their choice.

Monitoring and debugging are streamlined through AWS CloudWatch, where logs and metrics provide visibility into both the IoT messages and Lambda executions. This integration enables rapid development cycles and fast troubleshooting. Moreover, Lambda functions can be connected downstream to a variety of AWS services like S3, DynamoDB, SNS, SQS, and more, enabling powerful IoT workflows without managing any servers.

In summary, using AWS Lambda to handle AWS IoT messages allows developers to build highly responsive, serverless applications that can scale with demand and respond in real time to events from connected devices. This integration is ideal for smart home systems, industrial monitoring, agriculture tech, logistics, and many other IoT domains. It reduces complexity, enhances security, and accelerates development by letting AWS manage the underlying infrastructure, freeing developers to focus on creating innovative solutions.

Step 1: Set Up Your AWS IoT Thing

1. Go to AWS IoT Core Console.
2. Create a Thing (represents your device):
   • Navigate to Manage > Things.
   • Click Create Things.
   • Choose Create a single thing or Create many things.
   • Give it a name (e.g., MyIoTDevice), and optionally define type and groups.
3. Generate Certificates for authentication.
   • Download the certificate, private key, and Amazon root CA.
4. Attach a Policy that allows the device to connect, publish, subscribe, etc.

Step 2: Create an AWS Lambda Function

1. Go to the AWS Lambda Console.
2. Click Create function.
3. Choose:
   • Author from scratch
   • Function name (e.g., ProcessIoTMessage)
   • Runtime (e.g., Python, Node.js)
4. Click Create function.

Add basic handler code, for example (Python):

def lambda_handler(event, context):
    print("Received event: ", event)
    return {
        'statusCode': 200,
        'body': 'Message processed.'
    }

Step 3: Add Permission for AWS IoT to Invoke Lambda

AWS IoT needs permission to invoke the Lambda function.

• Go to the Lambda function > Configuration > Permissions.
• Edit the execution role to include AWS IoT permissions:

{
  "Effect": "Allow",
  "Action": "lambda:InvokeFunction",
  "Resource": "arn:aws:lambda:<region>:<account-id>:function:ProcessIoTMessage"
}

Or let AWS IoT automatically create the permission in the next step.

Step 4: Create an AWS IoT Rule

This defines how incoming messages are routed.

1. Go to AWS IoT Core > Act > Rules.
2. Click Create a rule.
3. Configure:
   • Name: e.g., InvokeLambdaRule
   • SQL Statement:

SELECT * FROM 'iot/topic'

• Replace 'iot/topic' with the actual topic you publish to.

1. Set action → Choose Lambda Function.
   • Select the Lambda function you created.
   • AWS will prompt you to grant IoT permission to invoke Lambda – accept it.
2. Click Create rule.

Step 5: Test the Setup

1. Publish a message to the topic via MQTT test client in AWS:
   • Go to AWS IoT Core > MQTT Test Client.
   • Choose “Publish to a topic”.
   • Enter the topic (e.g., iot/topic).
   • Enter a test payload: jsonCopyEdit{ "temperature": 25, "humidity": 50 }
   • Click Publish.

1. Check CloudWatch Logs for Lambda execution:
   • Go to CloudWatch > Logs > Log groups > /aws/lambda/ProcessIoTMessage.

Conclusion.

In conclusion, integrating AWS IoT Core with AWS Lambda offers a powerful, serverless approach to processing and responding to IoT device messages in real time. This setup allows developers to build intelligent, scalable, and event-driven applications without the complexity of managing infrastructure. By leveraging the MQTT protocol, the IoT Rules Engine, and Lambda’s event-based compute model, businesses can transform raw device data into actionable insights, trigger workflows, and seamlessly connect to other AWS services like DynamoDB, S3, and SNS. The flexibility, security, and cost-efficiency of this architecture make it ideal for a wide range of use cases — from smart cities and home automation to industrial monitoring and logistics. As the number of connected devices continues to grow, mastering this integration will be essential for developers and organizations looking to build the next generation of IoT solutions with speed, agility, and confidence.

Introduction.

The integration of AWS IoT Core with Amazon Simple Notification Service (SNS) represents a powerful solution for enabling real-time, event-driven messaging from IoT devices to end-users or systems. AWS IoT Core is a managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices. It facilitates the collection, processing, and analysis of data generated by sensors, devices, and microcontrollers that are part of the Internet of Things (IoT). On the other hand, Amazon SNS is a fully managed pub/sub messaging and mobile notification service that enables the decoupling of microservices and the distribution of information to multiple subscribers efficiently. By integrating AWS IoT Core with Amazon SNS, developers can create intelligent IoT applications that send alerts or notifications in real-time based on the data or state changes reported by devices. This integration is particularly useful in industrial monitoring, smart homes, fleet tracking, health monitoring systems, and more—any use case where timely alerts are crucial. For example, if a temperature sensor reports a value above a certain threshold, an SNS topic can trigger an SMS or email notification to a technician.

The process starts by configuring an SNS topic to act as the destination for alerts or messages. After setting up the topic and subscribing endpoints (such as email addresses or phone numbers), the next step involves defining a rule in AWS IoT Core. These rules evaluate device messages using a SQL-like syntax to determine whether a particular action should be taken. If the criteria match, the action invokes the SNS topic using a predefined IAM role with sufficient permissions. This IAM role acts as a secure bridge that allows AWS IoT Core to publish messages to the SNS topic. Messages are then pushed to the subscribed users or services instantly.

This integration offers a scalable and secure architecture that supports millions of connected devices and numerous concurrent notifications. It offloads the complexity of managing infrastructure and messaging queues, letting developers focus on business logic and innovation. Additionally, with built-in support for MQTT, HTTP, and WebSockets, AWS IoT Core ensures that devices can communicate efficiently using lightweight protocols, even over unreliable networks. Amazon SNS supports multiple notification formats, including SMS, email, Lambda function calls, and HTTP endpoints, giving developers the flexibility to build versatile notification systems. Furthermore, fine-grained access control via AWS IAM and message filtering via SNS subscription policies allows for precise targeting and security. The result is a robust, real-time alerting and notification system that bridges the physical world of devices with the digital world of users and cloud applications. Overall, integrating AWS IoT Core with SNS greatly simplifies the process of building reactive, event-driven IoT solutions that respond instantly to changes in the environment.

Prerequisites

• AWS account
• An IoT Thing registered in AWS IoT Core
• Basic familiarity with AWS Console

Step 1: Create an SNS Topic

1. Go to the Amazon SNS console: https://console.aws.amazon.com/sns/
2. Click Topics > Create topic
3. Choose Standard as the type
4. Name the topic (e.g., IoTAlertTopic)
5. Click Create topic

Step 2: Subscribe to the SNS Topic

1. Open the topic you just created
2. Click Create subscription
3. Choose Protocol (e.g., Email, SMS, Lambda, etc.)
4. Enter the endpoint (e.g., your email address for email)
5. Click Create subscription
6. Confirm the subscription (for email/SMS, check your inbox and click the confirmation link)

Step 3: Create an IAM Role for AWS IoT to Publish to SNS

1. Go to IAM Console > Roles > Create role
2. Trusted entity type: Choose AWS service
3. Use case: Choose IoT
4. Click Next
5. Attach the following policy (or create a custom one):

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sns:Publish",
      "Resource": "arn:aws:sns:REGION:ACCOUNT_ID:IoTAlertTopic"
    }
  ]
}

Name the role (e.g., IoT_SNS_Publish_Role) and click Create role

Step 4: Create an AWS IoT Rule

1. Go to AWS IoT Core Console
2. Navigate to Message Routing > Rules
3. Click Create rule
4. Enter a Name (e.g., PublishToSNSRule)
5. Rule query statement: Use an SQL-like expression, e.g.:

SELECT * FROM 'sensors/temperature'

1. Under Set one or more actions, click Add action
2. Choose Send a message as an SNS push notification
3. Select the SNS topic you created
4. Choose the role you created (IoT_SNS_Publish_Role)
5. Click Add action
6. Click Create rule

Step 5: Test the Integration

1. Publish a message from your device or the MQTT test client in AWS:
   • Topic: sensors/temperature
   • Message: { "temperature": 85 }
2. If the rule matches, AWS IoT will invoke the SNS action.
3. You’ll receive a notification (e.g., an email or SMS) through SNS.

Done! You now have AWS IoT Core sending notifications through Amazon SNS.

Conclusion.

In conclusion, integrating AWS IoT Core with Amazon SNS offers a highly effective, scalable, and secure way to build real-time notification systems driven by IoT data. This setup empowers organizations to automate alerts, monitor device states, and respond to events as they happen—without the need for constant manual oversight. By using AWS IoT rules to filter and process messages and leveraging SNS to distribute those messages across a wide range of protocols (such as email, SMS, mobile push, or HTTP endpoints), developers can ensure that critical information reaches the right people or systems instantly. The use of IAM roles ensures secure communication between services, while SNS’s flexibility allows for multiple subscription types and easy expansion as the application grows.

Whether used in smart homes, industrial automation, logistics, agriculture, or healthcare, this integration enables actionable intelligence and reduces operational latency. It streamlines the flow of information from edge devices to decision-makers or automated processes, enhancing responsiveness and reliability. As organizations continue to deploy IoT devices at scale, the combination of AWS IoT Core and Amazon SNS provides a future-proof foundation for building reactive, data-driven applications that can alert, inform, and act without delay. Ultimately, this integration highlights the power of combining IoT connectivity with cloud-based messaging to create dynamic, event-aware systems that improve efficiency and user experience.