PySpark Data Processing and Machine Learning Repository

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Welcome to my PySpark repository! This repository is a comprehensive collection of PySpark code, Jupyter notebooks, and resources aimed at demonstrating various aspects of data processing, streaming, spark optimizations and machine learning using PySpark. It is designed for both beginners and experienced developers who want to learn and understand the capabilities of PySpark in real-world scenarios.

This repository contains code solutions designed by me, as well as certain material and resources from the internet that provides specific solutions for the specific scenarios.

Table of Contents

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1. Introduction
2. Features
3. Setup Instructions
4. Batch Data Processing
5. Kafka Integration
6. Random Data Generation and Kafka Publishing
7. Streaming Data Processing
8. Machine Learning Use Cases
9. Spark Optimization
10. Resources and References
11. Contributing
12. Author

Introduction

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This repository contains a series of Jupyter notebooks and Python scripts developed as part of my learning process in handling various data processing and machine learning tasks using PySpark. The notebooks are designed to be beginner-friendly with detailed explanations, step-by-step instructions, and accompanying code snippets. The repository includes everything needed to replicate the environment and follow along with the notebooks.

Features

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• Batch Data Processing: Learn how to process large datasets efficiently using PySpark.
• Kafka Integration: Set up and integrate Kafka with PySpark for real-time data processing.
• Random Data Generation: Automate random data generation and publish to Kafka to simulate real-world scenarios.
• Streaming Data Processing: Process streaming data from sources like Kafka and sockets.
• Machine Learning: Implement regression, classification, and other machine learning models using PySpark.
• Spark Optimization: Learn techniques to optimize Spark jobs for better performance.
• Detailed Notes and Code Snippets: Comprehensive explanations and code snippets for each notebook.
• Setup Instructions: Step-by-step setup instructions to replicate the environment.

Setup Instructions

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To get started, follow these steps to set up the environment and run the notebooks:

1. Clone the Repository:

   git clone https://github.com/DebanjanSarkar/pyspark-maestro.git
   cd pyspark-maestro

2. Install Dependencies: Ensure you have Python and Java installed. Then, create a virtual environment and install the required Python packages:

   pip install -r requirements.txt

   Certain more python packages are required to be installed for some specific notebooks. The installation and setup of those packages are described in the notebook itself, and can be done later during specific notebook code execution.

   For execution of these notebooks, Spark and Hadoop must be installed and configured in the local system. These notebooks are tested for Spark v3.3.2. Following environment variables must be set according to the installed path of spark, python, Hadoop and Java:

   SPARK_HOME
   PYSPARK_HOME
   HADOOP_HOME
   JAVA_HOME

3. Set Up Kafka, Sockets and more: For setting up Kafka, sockets, and more, the instructions are given in respective notebooks in details, and following that, environment setup could be done easily.

4. Run Jupyter Notebooks: Start Jupyter Notebook or Jupyter Lab and open the desired notebook:

   jupyter notebook

   OR

   jupyter lab

Batch Data Processing

Explore batch data processing techniques using PySpark with detailed examples and code snippets. View Notebooks

Random Data Generation and Kafka Publishing

Automate the generation of random data and publish it to Kafka topics to simulate real-world data streams. View Scripts

Streaming Data Processing

Process and analyze streaming data from various sources like Kafka and sockets using PySpark. View Notebooks

Machine Learning Use Cases

Implement and evaluate machine learning models such as regression and classification using PySpark MLlib. View Notebooks

Spark Optimization

Learn and apply various Spark optimization techniques to improve the performance of your Spark jobs. View Notebooks

Resources and References

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• PySpark Documentation
• Kafka Documentation
• Jupyter Notebook Documentation

Contributing

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Contributions are welcome! If you have any suggestions or improvements, please open an issue or submit a pull request.

Author

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Debanjan Sarkar

For respective owners of certain code snippets, the respective authors and creators have been cited.