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https://github.com/codehub001/ai-driven-automation-for-data-quality-monitoring-in-cloud-data-warehouses
This project focuses on leveraging AI to automate data quality monitoring in cloud data warehouses. Traditional data validation methods often require manual intervention and fail to scale with increasing data complexity. By integrating machine learning models, this approach enables real-time anomaly detection, automated data cleansing.
https://github.com/codehub001/ai-driven-automation-for-data-quality-monitoring-in-cloud-data-warehouses
csv-export csv-import dashboard data datacleaning lib modeltraining python testing-library visualization
Last synced: 4 days ago
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This project focuses on leveraging AI to automate data quality monitoring in cloud data warehouses. Traditional data validation methods often require manual intervention and fail to scale with increasing data complexity. By integrating machine learning models, this approach enables real-time anomaly detection, automated data cleansing.
- Host: GitHub
- URL: https://github.com/codehub001/ai-driven-automation-for-data-quality-monitoring-in-cloud-data-warehouses
- Owner: codehub001
- Created: 2025-02-15T18:45:19.000Z (5 days ago)
- Default Branch: main
- Last Pushed: 2025-02-15T19:02:24.000Z (5 days ago)
- Last Synced: 2025-02-15T19:34:01.990Z (5 days ago)
- Topics: csv-export, csv-import, dashboard, data, datacleaning, lib, modeltraining, python, testing-library, visualization
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI-Driven-Automation-for-Data-Quality-Monitoring-in-Cloud-Data-Warehouses
## 📌 Overview
Ensuring high-quality data is crucial for accurate decision-making. This project leverages AI and machine learning to automate data quality monitoring in cloud data warehouses. By detecting anomalies, inconsistencies, and missing data in real time, this system enhances data integrity and reduces manual intervention.
## 🚀 Features
✅ **Real-time Anomaly Detection** – Identify incorrect, missing, or inconsistent data automatically.
✅ **Automated Data Cleansing** – AI-powered correction of errors in datasets.
✅ **Scalability** – Efficiently monitors large datasets in cloud-based environments.
✅ **Proactive Alerts** – Notifies users of potential data quality issues before they impact operations.
✅ **Seamless Integration** – Compatible with cloud data warehouses like Snowflake, BigQuery, and Redshift.
## 🏗️ Tech Stack
- **Programming Languages**: Python, SQL
- **Machine Learning**: Scikit-learn, TensorFlow
- **Cloud Services**: AWS, GCP, Azure
- **Data Engineering**: Apache Spark, Airflow
- **Databases**: Snowflake, BigQuery, Redshift
## 🔧 Installation
1. **Clone the repository**
```sh
git clone https://github.com/codehub001/AI-Driven-Automation-for-Data-Quality-Monitoring-in-Cloud-Data-Warehouses.git
cd AI-Driven-Automation-for-Data-Quality-Monitoring-in-Cloud-Data-Warehouses
```
2. **Create a virtual environment & install dependencies**
```sh
python -m venv venv
source venv/bin/activate # For macOS/Linux
venv\Scripts\activate # For Windows
pip install -r requirements.txt
```
3. **Run the application**
```sh
python main.py
```
## 📂 Project Structure
```
📦 AI-Driven-Automation-for-Data-Quality-Monitoring-in-Cloud-Data-Warehouses
├── 📂 data # Sample datasets for testing
├── 📂 models # Machine learning models for data monitoring
├── 📂 scripts # Scripts for data preprocessing and analysis
├── 📄 requirements.txt # Dependencies
├── 📄 main.py # Entry point of the project
├── 📄 README.md # Project documentation
```
## 🤝 Contribution
We welcome contributions! 🚀 Feel free to fork the repository, create a branch, and submit a pull request.
## 📜 License
This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.
## 📞 Contact
For questions or suggestions, reach out via [GitHub Issues](https://github.com/codehub001/AI-Driven-Automation-for-Data-Quality-Monitoring-in-Cloud-Data-Warehouses/issues).
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✨ Happy Coding! 🚀