Welcome to data-engineer-mini-project. This mini ETL (Extract, Transform, Load) pipeline helps you transform data from a CSV file into a SQLite database. You can then run SQL queries and perform analytics using pandas. It's beginner-friendly and perfect for building your data engineering portfolio.

To get started, follow these steps to download and run the software.

• Operating System: Windows, macOS, or Linux
• Software: Python 3.6 or higher
• Database: SQLite (comes included)
• Packages: pandas, SQLite3 (automatically installed)

To download the software, please visit the Releases page:

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On the Releases page, you will find the latest version. Click on it to download the ZIP file.

1. Download the ZIP file.
2. Extract the files to a folder on your computer. You can use software such as WinZip or the built-in extractor on your operating system.
3. Open a terminal or command prompt.
4. Change your directory to the folder where you extracted the files using the command cd <folder-path>. Replace <folder-path> with the actual path to the folder.

Once you have navigated to the right folder, you can run the pipeline. Here is how:

1. In the terminal or command prompt, type python main.py and press Enter.
2. Follow the on-screen instructions to input the path to your CSV file.

The pipeline supports a sample CSV file that you can use for testing:

• Sample CSV: sample_data.csv (included in the downloaded files)

You can modify the sample CSV or input your own data. The program will guide you through the process.

• CSV to SQLite: Easy import of CSV files.
• SQL Queries: Run queries against the imported data.
• Data Analysis: Use pandas for further analytics.
• User-Friendly: Designed for beginners.

For detailed information about how to use the ETL pipeline, you can check the documentation included in the repository. This includes:

• Explanation of each function in the code.
• Tips for modifying the SQL queries.
• Guidance on troubleshooting common issues.

If you have questions or need assistance, feel free to reach out. Join our community by creating an issue in the GitHub repository. We aim to help you succeed in your data engineering journey.

Q: What is ETL?
A: ETL stands for Extract, Transform, Load. It is a process used to move and transform data from source to destination.

Q: Do I need coding skills?
A: No, this project is designed for anyone, even those with no programming background.

Q: Can I use this for large datasets?
A: The pipeline works well for standard datasets. For very large datasets, additional optimizations may be needed.

If you want to contribute to this project, feel free to submit a pull request. We welcome all contributions, whether they are bug fixes, enhancements, or documentation improvements.

This project is licensed under the MIT License. You are free to use, modify, and distribute the software as needed.

You now have everything you need to download and run the data-engineer-mini-project. Thank you for using our ETL pipeline. Enjoy transforming your data!

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