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The project demonstrates an end-to-end data pipeline using Apache Kafka to fetch and stream data from a website. The project is containerized with Docker for streamlined deployment and dependency management.

Python 100.00%
apache-airflow apache-kafka apache-spark apache-zookeeper cassandra data-pipeline docker etl-pipeline postgres

real-time-data-streaming's Introduction

Real-Time-Data-Streaming

Overview

This project showcases a comprehensive end-to-end data pipeline utilizing Apache Kafka to seamlessly fetch and stream data from the randomuser.me API. Embracing containerization through Docker ensures smooth deployment and simplified dependency management. The technology stack encompasses Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, Cassandra, PostgreSQL, and Docker.

Table of Contents

Features

Capabilities of this project:

  • End-to-End Data Pipeline: Demonstrates the setup of a comprehensive data pipeline using Apache Airflow.

  • Real-Time Data Streaming: Leverages Apache Kafka for efficient real-time data streaming from the randomuser.me API.

  • Distributed Synchronization: Utilizes Apache Zookeeper for distributed synchronization within the data pipeline.

  • Advanced Data Processing: Implements data processing techniques with Apache Spark for robust and scalable data handling.

  • Flexible Data Storage: Offers data storage solutions using Cassandra and PostgreSQL, providing flexibility in choosing the appropriate storage system.

  • Containerized Deployment: Streamlines deployment and management by containerizing the entire data engineering setup with Docker.

Getting Started

Follow these steps to set up the project locally.

Prerequisites

Ensure the following tools are installed:

  • Docker
  • Python 3.x
  • Airflow

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/your-project.git
  2. Navigate to the project directory:

    cd your-project
  3. Build and run Docker containers:

    docker-compose up -d

Usage

  1. Start Docker Containers

  2. Access Apache Airflow

  3. Set Up Data Pipeline

  4. Monitor Real-Time Data Streaming

  5. Explore Data Processing with Apache Spark

  6. Utilize Data Storage Solutions

  7. Stop Docker Containers

LICENSE

MIT License

Copyright (c) [2023] [Muskan Raisinghani]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS," WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

REFERENCES

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