Big Data Analytics Project

Overview

This project focuses on performing big data analytics using a combination of MySQL, Pandas, Hadoop, Sqoop, Hive, and Tableau. The goal is to process and analyze large datasets efficiently, extracting meaningful insights for decision-making.

Technologies Used

• MySQL: For storing and managing structured data.
• Pandas: A Python library for data manipulation and analysis.
• Hadoop: A distributed storage and processing framework for handling large-scale data.
• Sqoop: Used for transferring data between Hadoop and relational databases like MySQL.
• Hive: Enables querying and managing large datasets stored in Hadoop.
• Tableau: A powerful data visualization tool for creating interactive and insightful dashboards.

Project Structure

• Data Collection: Describes how data was collected, sources, and formats.
• Data Processing: Explains the steps taken to clean, preprocess, and prepare the data.
• Analytics: Details the analytical methods and techniques applied to extract insights.
• Visualization: Demonstrates the visual representation of analytics results using Tableau.

Getting Started

Prerequisites

• Install MySQL, Pandas, Hadoop, Sqoop, Hive, and Tableau as per project requirements.

Setup

1. Clone the repository:

   git clone https:/arunerram/BigDataAnalysis.git
   cd big-data-analytics