Data Engineering Fundamentals

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This repository is designed to explore the core concepts of data engineering through hands-on, small mini-projects. These projects will cover fundamental aspects of data engineering, from database schema design to big data technologies, cloud platforms, and more. The primary programming language for this exploration will be Python, with opportunities to incorporate Java and Scala where relevant.

Objectives

• Practical Exploration: Each project will focus on a specific concept, providing both theoretical explanations and practical implementations.
• Language Proficiency: Emphasis on Python for most tasks, with some exploration into Java and Scala for relevant topics.
• End-to-End Pipeline: Learn how to move data from raw formats to well-structured, optimized systems ready for analysis.
• Industry-Relevant Skills: Focus on real-world tools and platforms used in the data engineering field.

Core Concepts

This repository will dive into the following 10 key data engineering areas:

1. Data Modeling:

   • Designing efficient and scalable database schemas.
   • Explore normalization, denormalization, and entity-relationship diagrams.

2. ETL Pipelines:

   • Building efficient Extract, Transform, Load (ETL) processes.
   • Automating data ingestion, transformation, and loading into target systems.

3. SQL:

   • Writing complex queries and managing relational databases.
   • Focus on query optimization and database performance tuning.

4. Data Warehousing:

   • Exploring cloud-based data warehousing solutions like Snowflake and Redshift.
   • Structuring data warehouses for efficient analytics.

5. Big Data Technologies:

   • Introduction to distributed systems like Hadoop, Spark, and Kafka.
   • Working with large datasets in a distributed environment.

6. Cloud Platforms:

   • Data engineering in the cloud using platforms like AWS, Google Cloud, and Azure.
   • Understanding the best practices for scalability and cost-efficiency.

7. Data Governance:

   • Implementing data governance strategies, including data security, privacy, and quality.
   • Ensuring compliance with industry standards.

8. Programming in Python/Java/Scala:

   • Using Python as the primary language for building data pipelines.
   • Exploring Java and Scala for distributed systems and big data technologies.

9. APIs and Data Integration:

   • Designing and consuming APIs for data integration.
   • Handling batch and real-time data processing using REST and streaming APIs.

10. Version Control and Automation:

• Using Git for version control.
• Automating workflows and deployment using CI/CD tools.

Project Structure

Each folder in this repository represents a mini-project focused on one of the core data engineering concepts. Each project includes:

• A brief explanation of the concept.
• A step-by-step implementation using Python (and occasionally Java/Scala).
• Instructions for setting up and running the project.
• An overview of the challenges and best practices related to the concept.

Example Projects

• Schema Design & SQL Optimization: Explore designing and optimizing schemas for efficient querying.
• Building an ETL Pipeline: Implement an end-to-end ETL process using Python to transform and load data into a database.
• Spark for Big Data Processing: Use Apache Spark to process large datasets in a distributed environment.
• Data Warehouse on Snowflake: Set up and query a cloud-based data warehouse.
• Real-time Data Streaming with Kafka: Learn how to process real-time streaming data using Kafka.
• Data Governance Strategies: Best practices for ensuring data quality and security in cloud environments.

Directory Structure

data_engineering_fundamentals/
├── 01_data_modeling/
│ ├── 1_README.md # Explanation of data modeling concepts
│ ├── 2_er_diagrams/ # Sample Entity-Relationship diagrams
│ ├── 3_examples/ # Examples of data models in different scenarios
│ └── 4_data/ # Data files for modeling examples
├── 02_etl_pipelines/
│ ├── 1_README.md # Overview of ETL pipelines
│ ├── 2_python/ # Python ETL scripts
│ ├── 3_airflow/ # Airflow DAG examples for ETL
│ ├── 4_tools/ # Tools for ETL (e.g., dbt, Fivetran)
│ └── 5_data/ # Data to use in ETL pipelines
├── 03_sql/
│ ├── 1_README.md # SQL introduction and best practices
│ ├── 2_queries/ # SQL query examples
│ ├── 3_optimization/ # Tips for optimizing SQL queries
│ ├── 4_exercises/ # SQL practice exercises
│ └── 5_data/ # SQL databases or datasets for practice
├── 04_data_warehousing/
│ ├── 1_README.md # Data warehousing fundamentals
│ ├── 2_snowflake/ # Examples for Snowflake setup and usage
│ ├── 3_redshift/ # Redshift examples
│ ├── 4_design_patterns/ # Common data warehousing design patterns
│ └── 5_data/ # Datasets for warehouse examples
├── 05_big_data_technologies/
│ ├── 1_README.md # Introduction to big data technologies
│ ├── 2_hadoop/ # Hadoop use cases and setup
│ ├── 3_spark/ # Apache Spark examples
│ ├── 4_kafka/ # Kafka for data streaming
│ └── 5_data/ # Sample big data datasets
├── 06_cloud_platforms/
│ ├── 1_README.md # Cloud platform overview
│ ├── 2_aws/ # AWS services for data engineering (e.g., S3, Redshift)
│ ├── 3_azure/ # Azure services
│ ├── 4_gcp/ # Google Cloud Platform services
│ └── 5_data/ # Cloud-based data examples
├── 07_data_governance/
│ ├── 1_README.md # Data governance concepts
│ ├── 2_privacy/ # Data privacy guidelines
│ ├── 3_security/ # Data security best practices
│ ├── 4_compliance/ # Compliance with regulations like GDPR, HIPAA
│ └── 5_data/ # Anonymized datasets for privacy and governance examples
├── 08_programming_languages/
│ ├── 1_README.md # Overview of programming languages for data engineering
│ ├── 2_python/ # Python scripts
│ ├── 3_java/ # Java code for data pipelines
│ ├── 4_scala/ # Scala code for distributed systems
│ └── 5_data/ # Data used for programming exercises
├── 09_apis_data_integration/
│ ├── 1_README.md # API and data integration concepts
│ ├── 2_rest_api/ # REST API examples
│ ├── 3_data_formats/ # Examples of working with JSON, XML, etc.
│ └── 4_data/ # Data for API integration exercises
├── 10_version_control_automation/
│ ├── 1_README.md # Version control and automation overview
│ ├── 2_git/ # Git best practices and workflows
│ ├── 3_ci_cd/ # CI/CD pipeline examples
│ └── 4_data/ # Configuration files, workflows, etc.
└── 11_docs/
└── 1_resources.md # Learning resources and additional references

Installation & Setup

1. Clone the repository:

   git clone https://github.com/your_username/data_engineering_fundamentals.git
   cd data_engineering_fundamentals

2. Install the necessary dependencies:

   pip install -r requirements.txt

3. For projects involving Java or Scala, ensure you have the respective development environments set up.

How to Use

1. Navigate to the specific project folder that aligns with the concept you are exploring.
2. Follow the provided instructions to set up the project and run the code.
3. Each project is designed to be independent, so you can pick and choose topics of interest.

Contributing

Feel free to contribute by:

• Suggesting new topics or tools to cover.
• Reporting issues or bugs.
• Enhancing existing projects with optimizations or additional features.

Future Goals

• Cross-language Projects: Expand projects to provide both Python and Java/Scala implementations.
• Advanced Topics: Explore more advanced data engineering topics such as data lake architectures, batch vs. streaming processing, and serverless data processing.

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License

This project is licensed under the MIT License - see the LICENSE file for details.