https://github.com/divithraju/divith-aju-hadoop-pyspark-pipeline

This project demonstrates the creation of a scalable data processing pipeline for handling and analyzing log data from a hypothetical e-commerce platform. Leveraging Hadoop and PySpark, the pipeline is designed to process large volumes of log files, providing meaningful insights into user behavior, system performance, and sales metrics.
https://github.com/divithraju/divith-aju-hadoop-pyspark-pipeline

apache-hadoop-framework apache-spark bigdata client data database dataengineering dataingestionframework datapreprocessing documentation ecommerce-platform hdfs pipeline project project-repository pyspark python3 software-engineering

Last synced: about 1 month ago
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This project demonstrates the creation of a scalable data processing pipeline for handling and analyzing log data from a hypothetical e-commerce platform. Leveraging Hadoop and PySpark, the pipeline is designed to process large volumes of log files, providing meaningful insights into user behavior, system performance, and sales metrics.

• Host: GitHub
• URL: https://github.com/divithraju/divith-aju-hadoop-pyspark-pipeline
• Owner: divithraju
• Created: 2024-08-17T16:17:10.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-08-17T16:23:47.000Z (4 months ago)
• Last Synced: 2024-10-19T03:11:30.192Z (2 months ago)
• Topics: apache-hadoop-framework, apache-spark, bigdata, client, data, database, dataengineering, dataingestionframework, datapreprocessing, documentation, ecommerce-platform, hdfs, pipeline, project, project-repository, pyspark, python3, software-engineering
• Language: Python
• Homepage: https://linktr.ee/divithraju
• Size: 4.88 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Scalable Log Data Processing Pipeline with Hadoop and PySpark

## Project Overview

This project demonstrates a scalable data pipeline for processing and analyzing log data using Hadoop, PySpark, and HDFS. It is designed to handle large volumes of log files and provide insights into user behavior, error rates, and peak traffic times for a hypothetical e-commerce platform.

## Features

- **Data Ingestion**: Load log data into HDFS for scalable storage.

- **Data Processing with PySpark**: Perform filtering, aggregation, and transformation using PySpark.

- **Partitioning and Optimization**: Utilize partitioning strategies to optimize performance.

- **Data Visualization**: Generate insightful visualizations of processed data using Python.

- **Error Handling and Monitoring**: Implement error detection and real-time monitoring.

## Project Structure

- `data/`: Contains sample log files for testing.

- `scripts/`: Includes Python and PySpark scripts for data ingestion, processing, and visualization.

- `notebooks/`: Jupyter notebook for detailed analysis and visualization.

- `README.md`: Project documentation.

- `requirements.txt`: List of required Python libraries.

## How to Run

1. Set up a Hadoop and Spark single-node cluster on your local machine.

2. Clone the repository and navigate to the project folder.

3. Install the required Python libraries:

    ```

    pip install -r requirements.txt

    ```

4. Run the data ingestion script:

    ```

    python scripts/data_ingestion.py

    ```

5. Execute the data processing script with PySpark:

    ```

    spark-submit scripts/data_processing.py

    ```

6. Visualize the results using the Jupyter notebook or the visualization script.

## Conclusion

This project showcases the design of a scalable log data pipeline using industry-standard tools like Hadoop and PySpark. It highlights your ability to work with large datasets, optimize performance, and provide actionable insights, making you a unique candidate for big data and data engineering roles.