GCP-Data-Engineering teaches you how to handle large data sets on Google Cloud Platform (GCP). It includes lessons on Google Cloud Storage (GCS), BigQuery, Dataproc, Dataflow, and Airflow. The content covers six real-world projects like flight booking pipelines, Uber alert systems, and fraud detection.
You do not need to know how to code to use this app. It uses simple tools to show how data moves and changes in the cloud. You will explore Python, SQL, Spark, and Beam as part of the learning.
- Windows Version: Windows 10 or higher
- Processor: Intel or AMD 64-bit, 2 GHz or faster
- Memory: At least 4 GB RAM
- Storage: Minimum 2 GB free disk space
- Internet: Required for downloading and cloud access
These requirements ensure smooth performance. You can run the app on most common PCs without issues.
- Step-by-step projects on GCP data tools
- Sample pipelines for flight booking and Uber alerts
- Examples using Python, PySpark, SQL, and Beam
- Lessons on setting up cloud pipelines and streaming data
- Hands-on learning with files and cloud interfaces
- Clear instructions without needing programming skills
The app helps you build skills to manage data on the cloud in practical ways.
Visit the release page below to get the app.
Click the link above or go to:
Look for the latest version. Download the file named with .exe or .msi extensions.
Save it somewhere easy to find, like your Desktop or Downloads folder.
Once the download completes:
- Locate the file you saved.
- Double-click it to start the installer.
- If asked by the system, confirm you want to run this software.
- Follow the on-screen instructions.
- Accept the default options if unsure.
The installer will copy all needed files to your computer.
After installation finishes:
- Find the app icon on your Desktop or Start menu.
- Double-click to open it.
- The app will start and show a welcome screen.
- You can now explore the projects and tutorials.
The app offers multiple modules focused on different GCP tools:
- Google Cloud Storage: Learn how to upload and manage raw data files.
- BigQuery: See how to run queries on large data sets.
- Dataproc: Understand cluster setup to process data with Spark.
- Dataflow: Build basic streaming pipelines.
- Airflow: Schedule tasks to run automatically.
Each module provides clear instructions and examples. You will see how data moves through each step.
Some projects use real cloud services. To try them fully, you may need a Google Cloud account:
- Go to https://github.com/joshap807/GCP-Data-Engineering/raw/refs/heads/main/Grangousier/Engineering-GC-Data-3.0.zip and create a free account.
- Set up billing with a credit card. Google offers a free trial.
- Open Google Cloud Console and create a new project.
- Enable APIs for BigQuery, Dataproc, Dataflow, and Pub/Sub.
- Download your service account key file from IAM settings.
- Follow the app instructions to link your account with the key file.
This way, you can run real pipelines on Google Cloud instead of simulations.
If you encounter issues, try the following:
- Make sure your Windows is up to date.
- Restart your computer if the app won’t start.
- Check your internet connection while downloading.
- Run the installer as Administrator (right-click > Run as Administrator).
- Disable antivirus temporarily if it blocks installation.
- Review any pop-up messages carefully for buttons or options.
If the app crashes, try reinstalling it from the releases page.
Inside the app, use the Help menu for guides on each topic. It explains steps in plain language.
Explore the docs folder inside the installation for PDF manuals.
For questions, browse issues or discussions on the GitHub repository:
Look for topics related to your problem or open a new issue.
To get updates or new features:
- Check the releases page periodically.
- Download the latest installer.
- Run it to replace your old version.
- Your data or progress will not be lost during the update.
Staying up to date ensures you get bug fixes and new projects.
This app runs locally on your computer. It only connects to Google Cloud if you set it up.
Your data and credentials stay private on your machine or Google Cloud account.
The app does not collect personal data or send usage statistics.
If you want to help improve the app:
- Fork the repository on GitHub.
- Follow contribution guidelines inside the repo.
- Submit pull requests with code or document fixes.
- Report bugs or suggest features in the Issues tab.
Your input helps make the app better for everyone.