# Script for Class 17: AWS Lambda: Introduction to Serverless Computing : What is Serverless

# **Agenda of the Day**

* Understanding Serverless Architecture: Benefits, Real-World Advantages and examples
* AWS Lambda: How It Works, Real-World Use Cases, and Problems It Solves
* Introduction to Boto3: Automating AWS Services with Python
* Overview of AWS Step Functions

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# Understanding Serverless Architecture: Benefits and Real-World Advantages

## Introduction
Serverless architecture is a cloud computing execution model where cloud providers manage infrastructure, allowing developers to focus on writing code without worrying about provisioning or maintaining servers. AWS Lambda is one of the most well-known serverless computing services.

## Key Benefits of Serverless Architecture

### 1. **Reduced Operational Overhead**
- No need to manage servers, patch operating systems, or configure networking.
- Developers can focus on business logic rather than infrastructure.

### 2. **Cost Efficiency**
- Pay only for the execution time of functions, avoiding costs of idle resources.
- No need for pre-provisioned capacity, reducing waste.

### 3. **Automatic Scaling**
- Functions scale automatically based on request load.
- No manual intervention is needed for traffic spikes or sudden demand.

### 4. **Faster Time to Market**
- Simplifies deployment, reducing the time required to launch new features.
- Integrates with CI/CD pipelines for rapid development cycles.

### 5. **Improved Fault Tolerance and Availability**
- Built-in high availability with distributed execution across multiple regions.
- Cloud providers manage failover and redundancy.

## Real-World Use Cases

### **1. Web and Mobile Backends**
- Serverless APIs using AWS Lambda with API Gateway for dynamic content.
- Reduces backend maintenance for mobile applications.

### **2. Data Processing and Analytics**
- Processing real-time data streams from IoT devices or event-driven applications.
- Example: Using AWS Lambda to transform and store streaming data in Amazon S3.

### **3. Automated Event Handling**
- Serverless workflows triggered by AWS events (e.g., file uploads, database updates).
- Example: Automatically resizing images uploaded to S3 using AWS Lambda.

### **4. Chatbots and Voice Assistants**
- Serverless functions process chat and voice commands dynamically.
- Example: Integrating Lambda with Amazon Lex for chatbot responses.

### **5. Continuous Integration and Deployment**
- Automating deployments and testing with serverless pipelines.
- Example: AWS CodePipeline triggering Lambda functions for automated builds.

## Examples of Serverless Architectures (Including DynamoDB)

### **1. Serverless Web Application**

**Services Used:**
- **Amazon API Gateway** – Handles HTTP requests
- **AWS Lambda** – Business logic execution
- **Amazon DynamoDB** – NoSQL database for storing user data
- **Amazon S3** – Hosts static frontend (HTML, CSS, JS)
- **Amazon Cognito** – User authentication and authorization

**Use Case:**
Build a single-page web app (SPA) with a serverless backend that scales automatically and has no infrastructure to manage.

---

### **2. Real-Time Data Processing**

**Services Used:**
- **Amazon Kinesis Data Streams / AWS IoT Core** – Ingest real-time data
- **AWS Lambda** – Processes incoming data events
- **Amazon DynamoDB** – Stores processed output or analytics
- **Amazon SNS / SQS** – Event notification or queueing

**Use Case:**
IoT sensors stream temperature data; Lambda processes each event and stores it in DynamoDB for dashboard visualizations.

---

### **3. Scheduled Automation (Cron Replacement)**

**Services Used:**
- **Amazon EventBridge** – Scheduled rule (like a CRON job)
- **AWS Lambda** – Executes periodic logic
- **Amazon DynamoDB** – Logs job results or state

**Use Case:**
Trigger a daily Lambda function to analyze records from DynamoDB and email a summary report using Amazon SES.

---

### **4. Chatbot or Voice Assistant Backend**

**Services Used:**
- **Amazon Lex / Alexa Skills Kit** – Conversational interface
- **AWS Lambda** – Processes user intents
- **Amazon DynamoDB** – Stores user context or session history

**Use Case:**
A voice assistant that retrieves product details or order status using user input stored and referenced via DynamoDB.

---

### **5. File Upload and Metadata Processing**

**Services Used:**
- **Amazon S3** – User uploads files
- **S3 Event Notification** – Triggers Lambda on upload
- **AWS Lambda** – Extracts metadata (e.g., file type, timestamp)
- **Amazon DynamoDB** – Stores file metadata records

**Use Case:**
Image uploader that automatically tags and catalogs images in DynamoDB when files are uploaded to S3.

---

### **6. Serverless E-commerce Backend**

**Services Used:**
- **API Gateway** – Handles customer interactions
- **AWS Lambda** – Manages product, cart, and order logic
- **Amazon DynamoDB** – Stores product catalog, user carts, and orders
- **Stripe/Payment Gateway** – External payment processing
- **Amazon SES/SNS** – Sends order confirmation or updates

**Use Case:**
An online store backend that supports viewing products, placing orders, and sending notifications — all without managing servers.

---

### **7. Serverless CI/CD Notification System**

**Services Used:**
- **AWS CodePipeline / CodeBuild** – CI/CD process
- **Amazon EventBridge** – Captures build events
- **AWS Lambda** – Parses event details
- **Amazon DynamoDB** – Logs build history
- **Amazon SNS** – Notifies stakeholders

**Use Case:**
Send real-time alerts to developers when a pipeline fails and store metadata about past deployments in DynamoDB.

## Conclusion
Serverless architecture provides a scalable, cost-efficient, and maintenance-free approach to running applications. It eliminates infrastructure management, allowing teams to focus on delivering value through code. By leveraging AWS Lambda and other serverless services, organizations can build modern applications with high availability, automatic scaling, and lower costs.

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# AWS Lambda: How It Works, Real-World Use Cases, and Problems It Solves

## Introduction
AWS Lambda is a serverless computing service that automatically executes code in response to events without requiring server management. It enables developers to build scalable applications by running event-driven functions that scale automatically.

## How AWS Lambda Works

1. **Event-Driven Execution**
- Lambda functions are triggered by events from various AWS services (e.g., S3 file uploads, API Gateway requests, DynamoDB updates).
- It runs only when needed, reducing idle resource costs.

2. **Stateless Function Execution**
- Each execution is independent, and no state is maintained between function invocations.
- For persistent storage, AWS services like S3, DynamoDB, or RDS can be used.

3. **Automatic Scaling**
- AWS Lambda scales automatically by creating multiple instances of a function as needed.
- There is no need to configure or provision capacity.

4. **Pay-Per-Use Pricing**
- Users are charged only for the execution time of the function, measured in milliseconds.
- No cost is incurred when the function is idle.

## Real-World Use Cases

### 1. **Web and Mobile Application Backend**
- Lambda can handle user authentication, API processing, and backend logic.
- Example: Processing API requests using AWS Lambda with API Gateway.

### 2. **Data Processing and Transformation**
- Lambda can process and transform data in real-time from different sources.
- Example: Extracting metadata from images uploaded to an S3 bucket.

### 3. **Automated File Processing**
- Lambda can trigger operations based on file uploads or modifications.
- Example: Converting video files into different formats when uploaded to S3.

### 4. **Serverless CRON Jobs and Task Scheduling**
- Lambda can run scheduled tasks using Amazon EventBridge (formerly CloudWatch Events).
- Example: Generating daily reports and storing them in an S3 bucket.

### 5. **IoT and Real-Time Data Processing**
- Lambda can process sensor data in real-time from IoT devices.
- Example: Triggering alerts when an IoT device reports abnormal data.

## Problems AWS Lambda Solves

### 1. **Eliminates Server Management**
- No need to maintain, update, or scale physical or virtual servers.
- AWS handles provisioning, patching, and scaling automatically.

### 2. **Reduces Infrastructure Costs**
- Traditional applications require servers running continuously, increasing costs.
- Lambda charges only for execution time, significantly lowering expenses.

### 3. **Improves Scalability**
- Traditional applications require manual scaling or autoscaling configuration.
- Lambda scales instantly to meet demand without user intervention.

### 4. **Simplifies Event-Driven Architectures**
- Applications needing event-based workflows can be implemented easily.
- Example: Sending email notifications when an order is placed in an e-commerce system.

### 5. **Faster Development and Deployment**
- Deploying new features in traditional applications requires modifying servers and infrastructure.
- With Lambda, developers deploy individual functions without affecting the entire system.

## Conclusion
AWS Lambda simplifies application development by removing infrastructure concerns, reducing costs, and enabling automatic scaling. It is a powerful tool for building event-driven applications, processing real-time data, and automating workflows without managing servers.

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# Introduction to Boto3: Automating AWS Services with Python

## Introduction
Boto3 is the official AWS SDK (Software Development Kit) for Python, allowing developers to interact with AWS services programmatically. It simplifies automation, enabling users to manage AWS resources such as EC2, S3, DynamoDB, and Lambda through Python scripts.

## Why Use Boto3?
- Automates AWS tasks without using the AWS Management Console.
- Provides an easy-to-use API for interacting with AWS services.
- Supports resource-level and client-level operations for flexibility.
- Works seamlessly with AWS Identity and Access Management (IAM) for secure access.

## Key Features of Boto3

### 1. **AWS Service Access**
- Supports nearly all AWS services, including S3, EC2, Lambda, and DynamoDB.
- Enables programmatic resource provisioning and management.

### 2. **Automation and Scripting**
- Automates repetitive tasks such as instance creation, file uploads, and database management.
- Helps in managing infrastructure as code (IaC).

### 3. **Resource and Client Abstractions**
- **Clients:** Provide a low-level interface to AWS services.
- **Resources:** Offer a higher-level abstraction, making it easier to work with AWS objects.

### 4. **Integration with AWS IAM**
- Uses IAM credentials to authenticate API requests securely.
- Supports role-based access control for better security.

## Real-World Use Cases

### 1. **Managing EC2 Instances**
- Automate the creation, termination, and monitoring of EC2 instances.
- Example: Starting and stopping instances based on usage patterns.

### 2. **Working with S3 Buckets**
- Upload, download, and manage files in Amazon S3.
- Example: Automating backups by copying files to an S3 bucket.

### 3. **Interacting with DynamoDB**
- Read, write, and update data in DynamoDB tables.
- Example: Storing user session data for a web application.

### 4. **Automating AWS Lambda Functions**
- Invoke and manage AWS Lambda functions programmatically.
- Example: Triggering Lambda functions based on custom event rules.

### 5. **Monitoring AWS Resources**
- Fetch CloudWatch metrics and logs for analysis.
- Example: Setting up automated alerts when EC2 CPU usage exceeds a threshold.

## Conclusion
Boto3 is a powerful SDK that enables developers to automate AWS operations using Python. Whether managing infrastructure, automating deployments, or interacting with cloud resources, Boto3 simplifies AWS service management while ensuring flexibility and security.

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# Overview of AWS Step Functions

### **What is AWS Step Functions?**

AWS Step Functions is a **serverless orchestration service** that enables you to coordinate multiple AWS services into **automated workflows**, using **visual workflows** called state machines. It helps break down complex tasks into **manageable, sequential steps** without managing servers.

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### **Key Features**

- **Visual Workflow Design:** Define workflows using a JSON-based Amazon States Language (ASL), and visualize execution in the AWS Console.
- **Service Orchestration:** Integrates with AWS Lambda, DynamoDB, ECS, SQS, SNS, Glue, SageMaker, and more.
- **Error Handling and Retry:** Built-in retry logic and error-catching mechanisms.
- **Execution History:** Detailed logs and visual execution flow for easy debugging.
- **Stateful Coordination:** Keeps track of the execution state between steps.
- **Express & Standard Workflows:**
- **Standard Workflows:** Long-running, durable, auditable
- **Express Workflows:** High-volume, short-duration executions

---

### **Core Concepts**

| Concept | Description |
|---------------------|-----------------------------------------------------------------------------|
| **State Machine** | A series of steps (states) that define the workflow |
| **State** | A step in the workflow (Task, Choice, Wait, etc.) |
| **Task** | A unit of work performed by a service (e.g., Lambda, Glue) |
| **Transition** | Movement from one state to another |
| **Amazon States Language** | JSON-based language used to define workflows |

---

### **Supported State Types**

- **Task:** Calls a Lambda function or other supported service
- **Choice:** Branching logic (like if-else)
- **Wait:** Delays the workflow for a specific time
- **Parallel:** Runs steps concurrently
- **Map:** Iterates over items in an array (looping)
- **Pass / Fail / Succeed:** Used for testing or marking final steps

---

### **Example Use Case: Image Processing Workflow**

1. **User uploads an image to S3**
2. Step Function starts:
- Validate file type using Lambda
- Trigger AI analysis with Amazon Rekognition
- Store metadata in DynamoDB
- Notify user via Amazon SNS

---

### **Benefits**

- No server management
- Visualize and debug workflows easily
- Scales automatically
- Integrates natively with most AWS services
- Improves code modularity and maintainability

---

### **Comparison with Traditional Code**

| Aspect | Traditional Code | Step Functions |
|------------------------|------------------------------|-----------------------------------------|
| Workflow Definition | Handwritten logic | Declarative JSON (ASL) |
| Error Handling | Manual try/catch | Built-in retry & error states |
| Visibility | Limited logs | Visual execution history |
| Parallel Processing | Complex threads | Built-in parallel state |
| Scalability | Developer-managed | Fully managed and scalable |

# Wrap-up and Q&A:

- **Understanding Serverless Architecture: Benefits and Real-World Advantages**:

Serverless architecture enables developers to build and run applications without managing servers, reducing costs and improving scalability. It enhances agility by automatically handling provisioning, scaling, and maintenance, making it ideal for modern cloud-based applications.

- **AWS Lambda: How It Works, Real-World Use Cases, and Problems It Solves**:

AWS Lambda provides a serverless computing solution that automatically scales and executes code in response to events, eliminating the need for infrastructure management. Its real-world use cases, such as real-time data processing and backend automation, make it a powerful tool for building cost-efficient and event-driven applications.

- **Introduction to Boto3: Automating AWS Services with Python**:

Boto3 simplifies AWS automation by providing a Python-based SDK to manage cloud resources efficiently. It enables developers to interact programmatically with services like EC2, S3, and Lambda, making cloud operations more scalable and automated.

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# Multiple Choice Questions (MCQs)

## Understanding Serverless Architecture: Benefits and Real-World Advantages

**1. What is a key advantage of serverless architecture?**
- A) Requires manual server provisioning
- B) Automatically scales based on demand
- C) Increases infrastructure management complexity
- D) Runs only on on-premise servers
**✅ Answer: B) Automatically scales based on demand**

**2. Which of the following services is commonly used in a serverless architecture?**
- A) Amazon EC2
- B) AWS Lambda
- C) Amazon RDS
- D) Elastic Load Balancer
**✅ Answer: B) AWS Lambda**

**3. How does serverless architecture help reduce costs?**
- A) Charges only for the exact amount of compute resources used
- B) Requires continuous server management
- C) Uses fixed pricing regardless of usage
- D) Requires upfront investment in hardware
**✅ Answer: A) Charges only for the exact amount of compute resources used**

**4. Which of the following is NOT a common use case for serverless computing?**
- A) Event-driven applications
- B) Auto-scaling backend processing
- C) Running virtual machines persistently
- D) Real-time file processing
**✅ Answer: C) Running virtual machines persistently**

---

## AWS Lambda: How It Works, Real-World Use Cases, and Problems It Solves

**5. AWS Lambda is best suited for which type of applications?**
- A) Long-running batch jobs
- B) Event-driven and real-time processing applications
- C) Applications that require dedicated servers
- D) Database-intensive applications
**✅ Answer: B) Event-driven and real-time processing applications**

**6. Which programming languages are supported by AWS Lambda?**
- A) Only Python
- B) Python, Java, Node.js, and more
- C) Only Java
- D) Only C++
**✅ Answer: B) Python, Java, Node.js, and more**

**7. What triggers an AWS Lambda function?**
- A) Only manual execution
- B) Various AWS services like S3, API Gateway, and DynamoDB
- C) Only scheduled cron jobs
- D) Only user login events
**✅ Answer: B) Various AWS services like S3, API Gateway, and DynamoDB**

**8. What is the maximum execution time for an AWS Lambda function?**
- A) 5 seconds
- B) 15 minutes
- C) 1 hour
- D) Unlimited
**✅ Answer: B) 15 minutes**

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## Introduction to Boto3: Automating AWS Services with Python

**9. What is Boto3 used for?**
- A) Managing AWS services using Python
- B) Deploying applications on Kubernetes
- C) Automating database queries
- D) Writing front-end applications
**✅ Answer: A) Managing AWS services using Python**

**10. Which of the following AWS services can be managed using Boto3?**
- A) Only EC2
- B) EC2, S3, Lambda, and many more
- C) Only IAM
- D) Only DynamoDB
**✅ Answer: B) EC2, S3, Lambda, and many more**

**11. What credentials are required to authenticate Boto3 with AWS?**
- A) SSH keys
- B) AWS IAM access key and secret key
- C) A database password
- D) A hardware token
**✅ Answer: B) AWS IAM access key and secret key**

**12. What is the main benefit of using Boto3 for AWS automation?**
- A) Reduces the need for AWS CLI commands
- B) Increases manual intervention in AWS services
- C) Only works with AWS Lambda
- D) Requires an on-premise setup
**✅ Answer: A) Reduces the need for AWS CLI commands**

---

# Scenario Based Questions

## Understanding Serverless Architecture: Benefits and Real-World Advantages

### **Scenario 1: Cost Optimization with Serverless Computing**
**Question:**
A startup is building an application that experiences unpredictable traffic spikes. They want to minimize infrastructure costs while ensuring high availability. How can a serverless architecture help them?

**Step-by-Step Answer:**
1. **Adopt AWS Lambda:** The startup can use AWS Lambda to execute code only when needed, reducing costs by paying only for execution time.
2. **Use API Gateway:** They can use Amazon API Gateway to handle incoming HTTP requests and trigger Lambda functions, reducing the need for dedicated web servers.
3. **Implement Event-Driven Processing:** Services like S3 and DynamoDB Streams can trigger Lambda functions to process data when events occur, eliminating idle compute time.
4. **Leverage Auto-Scaling:** Serverless automatically scales based on demand, ensuring smooth operation during traffic spikes without manual intervention.
5. **Eliminate Server Maintenance:** Since AWS manages the underlying infrastructure, the startup does not need to worry about provisioning, patching, or scaling servers.

---

## AWS Lambda: How It Works, Real-World Use Cases, and Problems It Solves

### **Scenario 2: Automating Image Processing with AWS Lambda**
**Question:**
A company wants to process user-uploaded images automatically and generate thumbnails without running dedicated servers. How can AWS Lambda be used to solve this problem?

**Step-by-Step Answer:**
1. **Use Amazon S3 for Storage:** Configure an S3 bucket where users upload images.
2. **Set Up an S3 Event Trigger:** Create an event notification that triggers an AWS Lambda function whenever a new image is uploaded.
3. **Write the Lambda Function:** The function will use a library like Pillow (Python) to generate thumbnails and store them back in another S3 bucket.
4. **Optimize Execution Time:** Lambda functions have a maximum execution time of 15 minutes, so ensure that processing is efficient and within limits.
5. **Reduce Costs:** Since Lambda charges based on execution time, the company only pays for actual processing rather than running an always-on server.

---

## Introduction to Boto3: Automating AWS Services with Python

### **Scenario 3: Automating S3 File Uploads with Boto3**
**Question:**
A developer needs to automate the process of uploading log files to an S3 bucket daily. How can they achieve this using Boto3 in Python?

**Step-by-Step Answer:**
1. **Install Boto3:** Ensure that Boto3 is installed by running `pip install boto3`.
2. **Configure AWS Credentials:** Store AWS credentials in `~/.aws/credentials` or use environment variables.
3. **Write a Python Script:** Use Boto3 to upload files to S3 automatically.
4. **Schedule Execution:** Use a cron job (Linux) or Task Scheduler (Windows) to run the script daily.
5. **Verify Uploads:** Ensure the files are successfully stored in the designated S3 bucket.

Example Python Script:

import boto3

s3 = boto3.client('s3')
bucket_name = "my-log-bucket"
file_name = "/path/to/logfile.log"
s3.upload_file(file_name, bucket_name, "logfile.log")
print("File uploaded successfully")