Excel Data Processor

A Python application for processing Excel sheets uploaded to an S3 bucket and storing data in DynamoDB using AWS CDK and Lambda.

Table of Contents

• Introduction
• Features
• Prerequisites
• Getting Started
  • Deployment
  • Usage

Introduction

This application demonstrates how to create an AWS CDK stack that automates the process of processing Excel sheets uploaded to an S3 bucket and storing the parsed data in a DynamoDB table. The project uses AWS Lambda to perform the data processing when new Excel files are added to the S3 bucket.

Features

• Automated processing of Excel sheets in an S3 bucket.
• Storage of parsed data in a DynamoDB table.
• Scalable and serverless architecture.

Prerequisites

Before you begin, ensure you have met the following requirements:

• AWS account with appropriate permissions.
• AWS CLI configured with your credentials.
• Python 3.8 or higher installed on your local machine.
• Node.js and npm installed (for AWS CDK).
• Git for version control.

Getting Started

To get started with this project, follow these steps:

Deployment

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/your-repo.git
   cd your-repo
   

2. Set up your AWS CDK environment:

   cd cdk-app
   npm install -g aws-cdk
   npm install
   

3. Deploy the AWS CDK Stack:

   cdk deploy
   

4. Deploy the Lambda Function:

   cd lambda
   pip install -r requirements.txt -t .
   

5. Run the deployment script:

   ./deploy_lambda.sh
   

Usage

• Upload Excel sheets to the specified S3 bucket.
• The Lambda function will automatically process the Excel sheets and store the data in the DynamoDB table.