Cloud to Intelligence #02: EC2, S3, Lambda, and IAM Explained
Explore four essential AWS services for building cloud applications: EC2 for computing, S3 for storage, Lambda for serverless execution, and IAM for access control. Understand their core use cases and how they work together in real-world application architectures.
Series: Cloud to Intelligence (2 articles)
- 2Cloud to Intelligence #02: EC2, S3, Lambda, and IAM Explained This article
Cloud to Intelligence #02: EC2, S3, Lambda, and IAM Explained
Introduction
In the first article of my Cloud to Intelligence series, I explored what AWS is and why cloud computing plays an important role in building modern AI applications.
Now, let's take the next step.
Before building AI-powered applications, it's important to understand the cloud services that help applications run, store data, execute code, and remain secure.
In this article, we'll explore four foundational AWS services: Amazon EC2, Amazon S3, AWS Lambda, and AWS Identity and Access Management (IAM).
1. Amazon EC2: Computing in the Cloud
Amazon Elastic Compute Cloud (EC2) provides virtual servers that allow developers to run applications without purchasing physical hardware.
Think of EC2 as a computer that you can configure and access remotely.
Developers can select computing resources based on their requirements, including processing power, memory, storage, and networking.
Where can EC2 be useful?
- Hosting web applications and backend services.
- Running scripts and data-processing workloads.
- Hosting machine learning applications.
- Running services that need more control over their computing environment.
- Running scripts and data-processing workloads.
- Hosting machine learning applications.
- Running services that need more control over their computing environment.
For example, if you're building an AI application with a custom backend, an EC2 instance could host that backend and process requests from users.
However, EC2 requires you to manage aspects of the server environment, operating system, updates, and security configuration.
2. Amazon S3: Storing Data in the Cloud
Amazon Simple Storage Service (S3) is an object storage service designed to store and retrieve data.
You can use it to store documents, images, datasets, application files, backups, and machine learning artifacts.
S3 organizes data into buckets and objects.
Where can S3 be useful?
- Storing datasets for machine learning.
- Keeping documents uploaded to an AI application.
- Saving generated reports and processed files.
- Storing backups and application assets.
- Keeping documents uploaded to an AI application.
- Saving generated reports and processed files.
- Storing backups and application assets.
Imagine creating a document-question-answering application. Users upload PDF files, and your application needs a reliable place to store them. An S3 bucket can serve as that storage layer.
Security matters here: buckets should use appropriate access controls, and sensitive files should not be made public unless there is a specific, justified requirement.
3. AWS Lambda: Running Code Without Managing Servers
AWS Lambda is a serverless compute service that runs code in response to events.
With Lambda, you don't need to provision or manage a server for every function you want to execute. AWS manages the underlying compute infrastructure, while you focus on your code and its configuration.
Where can Lambda be useful?
- Processing an event when a file is uploaded.
- Running backend functions for an application.
- Responding to API requests.
- Automating small, event-driven tasks.
- Connecting different cloud services.
- Running backend functions for an application.
- Responding to API requests.
- Automating small, event-driven tasks.
- Connecting different cloud services.
For example, when a user uploads a document to S3, an event can trigger a Lambda function to validate the file or initiate a processing workflow.
Lambda is especially useful for event-driven workloads, although execution limits, runtime requirements, and workload patterns should be considered when choosing it.
4. AWS IAM: Managing Access and Permissions
AWS Identity and Access Management (IAM) helps control who can access AWS resources and which actions they can perform.
Cloud applications often interact with several services. Without proper access controls, a configuration mistake could expose data or allow unauthorized operations.
IAM helps you define permissions using policies and assign them to users, groups, and roles.
Why is IAM important?
- Controlling access to AWS resources.
- Granting applications only the permissions they need.
- Separating responsibilities between people and services.
- Reducing the risk of unauthorized actions.
- Granting applications only the permissions they need.
- Separating responsibilities between people and services.
- Reducing the risk of unauthorized actions.
For example, if a Lambda function needs to read documents from a specific S3 bucket, its execution role can be configured with the required permissions rather than granting broad access to the entire AWS account.
A useful security principle is least privilege: give each identity only the permissions necessary to perform its task.
5. How These Services Work Together
Let's consider a simple file-processing application.
A user uploads a document, and the application processes it before making the results available.
A possible workflow is:
1. Amazon S3: Stores the uploaded document.
2. AWS Lambda: Responds to the upload event and starts processing.
3. Amazon EC2: Runs a separate backend or processing service if the workload requires a persistent server or more control.
4. AWS IAM: Controls which services can access the document and perform specific operations.
2. AWS Lambda: Responds to the upload event and starts processing.
3. Amazon EC2: Runs a separate backend or processing service if the workload requires a persistent server or more control.
4. AWS IAM: Controls which services can access the document and perform specific operations.
Not every application needs all four services. For example, a small event-driven application might use S3 and Lambda without EC2.
The important lesson is that AWS services are building blocks. You choose and connect them according to the requirements of your application.
6. How Do These Services Support AI Applications?
These services can also support the infrastructure around AI systems.
For example:
- EC2 can host a custom inference server or AI application backend.
- S3 can store datasets, documents, and model artifacts.
- Lambda can coordinate event-driven tasks and connect application components.
- IAM can control access to data and AI-related resources.
- S3 can store datasets, documents, and model artifacts.
- Lambda can coordinate event-driven tasks and connect application components.
- IAM can control access to data and AI-related resources.
If you use a managed service such as Amazon Bedrock, you may not need to host the foundation model yourself. Your application can call the service through its APIs while AWS manages the underlying model infrastructure.
The right architecture depends on performance requirements, data sensitivity, expected traffic, operational complexity, and cost.
7. What Should You Try Next?
If you're learning AWS, start with the fundamentals rather than deploying a complicated application immediately.
Here are a few practical exercises:
- Learn how S3 buckets and object permissions work.
- Explore IAM policies and understand why least privilege matters.
- Read about Lambda triggers and event-driven workflows.
- Compare EC2 with serverless computing and identify when each approach is appropriate.
- Review the pricing model before creating resources.
- Explore IAM policies and understand why least privilege matters.
- Read about Lambda triggers and event-driven workflows.
- Compare EC2 with serverless computing and identify when each approach is appropriate.
- Review the pricing model before creating resources.
Use a test environment, avoid storing sensitive information, and clean up resources you no longer need. Some AWS resources can incur charges even when you're not actively using them.
Conclusion
Amazon EC2, Amazon S3, AWS Lambda, and IAM solve different problems, but together they form part of the foundation for many cloud applications.
EC2 provides configurable computing, S3 stores objects, Lambda executes code in response to events, and IAM manages permissions.
Understanding these building blocks makes it easier to design secure, maintainable applications and eventually integrate AI capabilities into them.
This is another step in my Cloud to Intelligence journey as I continue exploring AWS, cloud architecture, and Generative AI.
Coming next in Part 3: Learning how to manage AWS costs and experiment with cloud services more safely as a student.
Which of these four services have you used, and which one would you recommend learning first?
#AWS #CloudComputing #AmazonEC2 #AmazonS3 #AWSLambda #CloudSecurity #AWSBuilderCenter
Series: Cloud to Intelligence (2 articles)
- 2Cloud to Intelligence #02: EC2, S3, Lambda, and IAM Explained This article
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