🌟 CSV File Processing System 🚀

Welcome to the CSV File Processing System! If you're ready to dive into the world of cloud computing, metadata extraction, and AWS-like local environments, you're in the right place. Let's explore how this project makes file handling easy and efficient in a cloud-like local environment using LocalStack.

✨ What is this Project? 🤔

This project is a cloud-based file processing system designed to handle CSV files with ease. It automatically:

1. Uploads CSV files to an S3 bucket.
2. Extracts metadata like:
   • Row count 📊
   • Column count 📏
   • Column names 📝
3. Stores this metadata in a database (DynamoDB or RDS).
4. (Optional) Sends a notification upon completion. 🛎️

What’s awesome? This entire setup is running locally using LocalStack – an open-source tool that simulates AWS services on your machine. No AWS account? No problem. 🙌

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🔧 Technologies Used

Here’s what powers this project:

• S3: Cloud storage for CSV files.
• Lambda: Serverless function to process CSV files and extract metadata.
• DynamoDB or RDS: Choose a database to store metadata (you can use whichever suits your project best).
• LocalStack: Local AWS cloud stack emulator to develop and test without AWS costs.
• Python: The programming language that ties everything together! 🐍

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🚀 Getting Started

Let’s get this system up and running in your local environment. Follow these steps to get started:

🛠️ Prerequisites

Before you begin, make sure you have the following installed on your machine:

• Python 3.x
• LocalStack (this simulates the AWS cloud locally)
• Docker (required by LocalStack)
• AWS CLI (Optional, for interacting with LocalStack services)

1️⃣ Install LocalStack

LocalStack is the backbone of this project. It allows you to simulate AWS services locally without the need for real AWS credentials.

• Installation via Docker:
  If you don’t have Docker installed, get it here.

  Then, run LocalStack with the following command:

  docker-compose up

• Alternatively, you can install LocalStack with pip:

  pip install localstack

2️⃣ Set Up the Project

Clone this repository to your local machine:

git clone https://github.com/KritikaK21/FileProcessingSystem.git
cd FileProcessingSystem

Now, you’re ready to go!

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⚡ How It Works

The magic happens with just a few simple steps! Here’s the entire flow:

🗂️ Step 1: Upload a CSV to S3

Once you run the system, it automatically uploads the CSV to an S3 bucket.

• Simply place your CSV file in the designated folder or use the provided UI (if available).
• The Lambda function will be triggered once the file is uploaded to the bucket.

📊 Step 2: Metadata Extraction

Here’s where things get interesting. Our Lambda function will automatically:

• Count the rows and columns in the CSV file.
• Get the column names.

Example metadata:

{
  "filename": "example.csv",
  "upload_timestamp": "2024-12-14 10:00:00",
  "file_size_bytes": 1048576,
  "row_count": 1000,
  "column_count": 5,
  "column_names": ["id", "name", "age", "city", "date"]
}

🗄️ Step 3: Store Metadata in Database

Next, the extracted metadata is stored in your chosen database. You can either go with:

• DynamoDB: A NoSQL option for easy and fast metadata storage.
• RDS (PostgreSQL or MySQL): A relational database for more complex needs.

🔔 Step 4: Get a Notification (Optional)

If you want, the system will send a notification once the process is complete. This could be done using AWS SNS (Simple Notification Service) for real-time updates.

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🔍 Explore the Code

The heart of this system is in the code files:

• localstack_s3.py: Responsible for interacting with S3.
• s3_client.py: The script that interacts with the LocalStack S3 service to upload and retrieve files.
• metadata_extractor.py: The magic that extracts metadata from your CSV files.
• lambda_function.py: Handles the Lambda function that triggers upon file upload.
• db_connector.py: Connects to the database and stores metadata.

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📝 Example Workflow

Let’s break down the workflow using an example:

1. Upload your CSV file, e.g., data.csv, to the LocalStack S3 bucket.
2. The Lambda function is triggered automatically. It processes the file and extracts metadata.
3. The extracted metadata (like row count, column names) is stored in DynamoDB (or RDS).
4. You get a notification (optional) about the successful upload and processing.

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💡 Why Use LocalStack?

🎯 Test AWS Services Locally:

LocalStack emulates many AWS services locally, allowing you to develop and test your cloud-based applications without incurring any costs.

⏱️ Speed Up Development:

Instead of waiting for cloud resources to spin up on AWS, LocalStack provides an instant local environment for faster development and testing.

💸 No AWS Costs:

When developing and testing locally with LocalStack, you won’t be charged for any AWS resources.

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📈 Future Enhancements

• UI Dashboard: A simple web UI to upload CSV files, view metadata, and check the status of your files.
• More File Formats: Support for JSON, XML, and other file types.
• Cloud Deployment: After local testing, deploy the system to real AWS services!

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🏆 Conclusion

This project combines the best of cloud computing and local testing to make file processing easier and more efficient. Whether you're learning cloud technologies or building a robust system, this project gives you a strong foundation.

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Ready to dive in? 🎉

• Clone the repo, set up LocalStack, and get started with cloud-based CSV processing today! 🚀# CSV_file_processing_system csv file processing system