Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/murtaza-arif/all-you-need-to-know-for-data-engineer
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.
https://github.com/murtaza-arif/all-you-need-to-know-for-data-engineer
cassandra data data-engineering data-science kafka kafka-consumer kafka-streams pyarrow spark
Last synced: 6 days ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/murtaza-arif/all-you-need-to-know-for-data-engineer
- Owner: Murtaza-arif
- Created: 2024-12-12T22:57:20.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-01-12T15:02:02.000Z (about 1 month ago)
- Last Synced: 2025-01-12T16:18:12.597Z (about 1 month ago)
- Topics: cassandra, data, data-engineering, data-science, kafka, kafka-consumer, kafka-streams, pyarrow, spark
- Language: Python
- Homepage:
- Size: 156 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](#introduction)
2. [Key Topics Covered](#key-topics-covered)
- [Data Formats and Storage](#data-formats-and-storage)
- [Data Ingestion](#data-ingestion)
- [Data Transformation and Cleaning](#data-transformation-and-cleaning)
- [Data Storage and Management](#data-storage-and-management)
- [Big Data Tools](#big-data-tools)
- [Cloud Platforms](#cloud-platforms)
- [DevOps for Data Engineers](#devops-for-data-engineers)
- [Machine Learning Engineering Integration](#machine-learning-engineering-integration)
- [Real-world Projects](#real-world-projects)
- [Advanced Topics](#advanced-topics)
- [Data Warehousing](#data-warehousing)
3. [Getting Started](#getting-started)
4. [Contributing](#contributing)
5. [License](#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:
```bash
python -m venv venv
```
2. Activate the virtual environment:
- On macOS/Linux:
```bash
source venv/bin/activate
```
- On Windows:
```bash
.\venv\Scripts\activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
### Running Examples
Navigate to specific topic directories and run the Python scripts. For example:
```bash
# 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:
```bash
git clone https://github.com/Murtaza-arif/all-you-need-to-know-for-data-engineer.git
```
2. Navigate to the project folder:
```bash
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](LICENSE) file for details.
---
Happy Learning and Building! 🚀