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Data Engineering Repository

Welcome to the Data Engineering Repository! This repository is designed to showcase various aspects of data engineering, including tools, frameworks, and end-to-end projects. It covers everything from data ingestion and transformation to data warehousing and cloud-based solutions.

Table of Contents

  1. Introduction
  2. Key Topics Covered
  3. Getting Started
  4. Contributing
  5. License

Introduction

This repository is tailored for data engineers looking to explore, learn, and implement various data engineering concepts. Whether you are a beginner or an experienced professional, you'll find useful examples, tools, and projects to enhance your skills.

Key Topics Covered

1. Data Formats and Storage

  • Handling common data formats: CSV, JSON, Parquet, Avro, ORC.
  • Examples of data format conversions.

2. Data Ingestion

  • Batch data ingestion pipelines.
  • Using tools like Apache spark, AWS S3, or Python scripts to ingest data.
  • Stream Data Processing
  • Using tools like Apache Kafka, AWS Kinesis or Google Pub/Sub.

3. Data Transformation and Cleaning

  • ETL/ELT pipelines with Apache Airflow, AWS Glue, or Python.
  • Data cleaning examples using Pandas and PySpark.

6. Cloud Platforms

  • AWS: S3, Redshift, Glue, Athena.
  • Terraform for Infrastructure as Code.

7. DevOps for Data Engineers

  • Monitoring and logging with data drift, ELK Stack.

8. Machine Learning Engineering Integration

  • Data preparation and feature engineering.
  • Data versioning using DVC or MLflow.

9. Real-world Projects

  • E-commerce Analytics Pipeline.
  • Real-time Fraud Detection.
  • Weather Data Processing.

10. Advanced Topics

  • Data Governance with Apache Atlas or AWS Lake Formation.
  • Data Security: Encryption, IAM roles.
  • Scalable Design Patterns: Partitioning, sharding.

11. Data Warehousing

  • OLAP vs. OLTP concepts.
  • Data warehouse lifecycle: Staging, ETL, presentation layers.
  • Hadoop-based data warehousing with Apache Hive.
  • Cloud solutions: Redshift, BigQuery, Synapse Analytics.
  • Performance optimization techniques.

Getting Started

Setup Virtual Environment

  1. Create a virtual environment:
python -m venv venv
  1. Activate the virtual environment:
  • On macOS/Linux:
source venv/bin/activate
  • On Windows:
.\venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt

Running Examples

Navigate to specific topic directories and run the Python scripts. For example:

# Run data format examples
python data_formats_and_storage/format_examples.py

# Run format conversion examples
python data_formats_and_storage/format_conversions.py
  1. Clone the repository: 
    git clone https://github.com/Murtaza-arif/all-you-need-to-know-for-data-engineer.git
  2. Navigate to the project folder: 
    cd data-engineering-repo
  3. Follow the instructions in each topic folder's README.md file to explore examples and projects.

Contributing

Contributions are welcome! If you have ideas or projects to add, feel free to open an issue or submit a pull request.

License

This repository is licensed under the MIT License. See the LICENSE file for details.

Happy Learning and Building! 🚀