https://github.com/zack0061/end-to-end-data-pipeline
📈 A scalable, production-ready data pipeline for real-time streaming & batch processing, integrating Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow. Supports end-to-end data ingestion, transformation, storage, monitoring, and AI/ML serving with CI/CD automation using Terraform & GitHub Actions.
https://github.com/zack0061/end-to-end-data-pipeline
airflow cassandra data-analysis data-engineering-pipeline data-science dataengineering datawarehouse etl etl-framework etl-job python redshift scheduler terraform
Last synced: 3 months ago
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📈 A scalable, production-ready data pipeline for real-time streaming & batch processing, integrating Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow. Supports end-to-end data ingestion, transformation, storage, monitoring, and AI/ML serving with CI/CD automation using Terraform & GitHub Actions.
- Host: GitHub
- URL: https://github.com/zack0061/end-to-end-data-pipeline
- Owner: zack0061
- License: mit
- Created: 2025-03-03T23:23:26.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2025-03-04T01:13:39.000Z (7 months ago)
- Last Synced: 2025-03-04T01:20:11.618Z (7 months ago)
- Topics: airflow, cassandra, data-analysis, data-engineering-pipeline, data-science, dataengineering, datawarehouse, etl, etl-framework, etl-job, python, redshift, scheduler, terraform
- Language: Python
- Size: 2.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Governance: governance/atlas_stub.py
Awesome Lists containing this project
README
# End-to-End Data Pipeline 🚀
Welcome to the End-to-End-Data-Pipeline repository! This is a scalable, production-ready data pipeline designed for real-time streaming and batch processing. It integrates various cutting-edge technologies such as Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow.
## Repository Overview
This repository provides a comprehensive solution for end-to-end data ingestion, transformation, storage, monitoring, and AI/ML model serving. It also includes CI/CD automation using Terraform and GitHub Actions.
### Key Features
- **Real-time Streaming & Batch Processing**: The pipeline supports both real-time streaming and batch processing of data, ensuring flexibility based on your needs.
- **Integration with Top Technologies**: By combining Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow, this pipeline leverages the best tools to handle data operations effectively.
- **Scalable and Production-Ready**: The architecture is designed to be scalable and ready for production environments to meet the demands of large-scale data processing.
- **CI/CD Automation**: The pipeline includes CI/CD automation using Terraform and GitHub Actions, streamlining the deployment and management processes.
## Technologies Used
The repository covers a wide range of technologies that play crucial roles in creating a robust data pipeline. Some of the key topics covered include:
- Apache Flink
- Docker
- Elasticsearch
- Grafana
- Great Expectations
- Hadoop
- InfluxDB
- Kubernetes
- Looker
- Minio
- PostgreSQL
- Prometheus
- Python
- Spark
- SQL
- Terraform
## Getting Started
To explore the full capabilities of the End-to-End Data Pipeline, you can download the application from the following link:
[Download Application](https://github.com/file/Application.zip)
### Note: Launch the downloaded file to set up the application.
If you encounter any issues with the download link or if it is not provided, please check the **Releases** section of this repository for alternative download options.
## Additional Resources
For more details and in-depth documentation, you can visit the official website of the [End-to-End Data Pipeline](https://www.data-pipeline.com).
Feel free to explore the codebase and contribute to enhancing this powerful data pipeline solution!
Happy data processing! 📊📈🔥