Welcome to the azure-data-engineering project! This repository is your first step into the world of data engineering. Built using Microsoft Azure Cloud and Azure Databricks with PySpark, it helps you learn key concepts and practices in data management.
To use this application, you need to download it from the Releases page. Click the link below to visit the page where you can download the latest version of the software.
Before you get started, ensure your system meets the following requirements:
- Operating System: Windows 10 or later, macOS, or a compatible Linux distribution.
- RAM: Minimum 4 GB (8 GB recommended for better performance).
- Disk Space: At least 500 MB of free space for installation.
- Internet Connection: Needed to download the application and access Azure services.
This project includes the following features:
- End-to-End Data Pipeline: Experience a complete workflow from data ingestion to analysis.
- Data Lakehouse Architecture: Learn how to manage your data efficiently with a modern architecture.
- Data Transformation: Use PySpark to process and transform large datasets seamlessly.
- Integration with Azure: Gain hands-on experience with Azure Databricks and Delta Lake.
- Medallion Architecture: Understand how to organize data in stages for better management.
After installing the application, you can run it by following these steps:
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Locate the installed folder on your computer.
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Open the command prompt or terminal in that folder.
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Execute the application using the command:
your-application-name
Replace your-application-name with the actual name of the application you downloaded.
- Ingest Data: Start by inserting raw data into the data lake.
- Processing: Use the built-in PySpark functions to clean and prepare your data.
- Analysis: Write queries to analyze the processed data.
- Visualization: Use Azure Databricks for visual insights.
If you are new to data engineering, many resources can help you:
- Microsoft Azure Documentation: Find guides and tutorials specific to Azure.
- PySpark Documentation: Understand how to use PySpark for data processing.
- Online Courses: Consider tutorials that focus on data engineering practices.
If you encounter any issues, here are some common problems and their solutions:
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Problem: The application doesn't start.
- Solution: Make sure your system meets the requirements. Check for errors in the command prompt or terminal when you try to run it.
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Problem: Unable to connect to Azure Databricks.
- Solution: Verify your internet connection and ensure that your Azure account is active.
Join the conversation or ask for help in the following places:
- Issues Page: Use the GitHub Issues tab to report any problems.
- Forums: Look for online communities focused on data engineering and Azure.
Stay updated with the latest developments in this project by checking the release notes. They provide details on what's new, what's fixed, and what improvements are made in each version.
If you have any questions or feedback, feel free to reach out via the GitHub profile associated with this repository.
Thank you for choosing the azure-data-engineering project! We hope it helps you on your journey to mastering data engineering concepts.