GSP604

Overview

This hands-on lab takes you through the process of analyzing the Bitcoin transactions associated the infamous 10,000 bitcoin pizza purchase - the first known exchange of Bitcoin for goods on May 17, 2010.

You will use a Vertex AI Workbench instance to connect to BigQuery and analyze the Bitcoin transaction network related to the exchange.

In this lab you will:

1. Query the BigQuery public dataset to retrieve all Bitcoin transactions within two degrees of separation from the initial pizza exchange.
2. Refine and preprocess the transaction data to accurately define the relationships between Bitcoin addresses for graph construction.
3. Visualize the resulting directed transaction graph to explore the flow of funds and network structure.

This lab will show you how cloud-native tools can be used to handle large-scale data visualization, turning billions of records into actionable visual intelligence for machine learning or data analysis purposes.

Attribution: This lab is based on the code originally written by Allen Day and modified by Sohier Dane and Meg Risdal from these Kaggle kernels (parts 1, 2, 3).

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

Note: Use an Incognito (recommended) or private browser window to run this lab. This prevents conflicts between your personal account and the student account, which may cause extra charges incurred to your personal account.

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Note: Use only the student account for this lab. If you use a different Google Cloud account, you may incur charges to that account.

How to start your lab and sign in to the Google Cloud console

1. Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:

   • The Open Google Cloud console button
   • Time remaining
   • The temporary credentials that you must use for this lab
   • Other information, if needed, to step through this lab

2. Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   The lab spins up resources, and then opens another tab that shows the Sign in page.

   Tip: Arrange the tabs in separate windows, side-by-side.

   Note: If you see the Choose an account dialog, click Use Another Account.

3. If necessary, copy the Username below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details pane.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details pane.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. Click through the subsequent pages:

   • Accept the terms and conditions.
   • Do not add recovery options or two-factor authentication (because this is a temporary account).
   • Do not sign up for free trials.

After a few moments, the Google Cloud console opens in this tab.

Note: To access Google Cloud products and services, click the Navigation menu or type the service or product name in the Search field.

Task 1. Launch Vertex Workbench Notebook

1. In the Google Cloud console, from the Navigation menu (), select Vertex AI > Workbench.

2. Click Enable Notebooks API.

3. At the top of the Workbench page, ensure you are in the Instances view.

4. Click Create New.

5. Configure the Instance:

   • Name: lab-workbench
   • Region: Set the region to
   • Zone: Set the zone to
   • Advanced Options (Optional): If needed, click "Advanced Options" for further customization (e.g., machine type, disk size).

6. Click Create.

This will take a few minutes to create the instance. A green checkmark will appear next to its name when it's ready.

7. Click Open JupyterLab next to the instance name to launch the JupyterLab interface. This will open a new tab in your browser.

8. Click the Python 3 icon to launch a new Python notebook.

9. Right-click on the Untitled.ipynb file in the menu bar and select Rename Notebook to give it a meaningful name.

Your environment is set up. You are now ready to start working with your Vertex AI Workbench notebook.

Wait for the notebook instance to start, this takes a few minutes.

Click Check my progress to verify the objective. Create the Notebook instance

Task 2. Load the data

The GitHub repo contains both the lab file and solutions files for the course.

1. Copy and run the following code in the first cell of your notebook to clone the training-data-analyst repository.

!git clone https://github.com/GoogleCloudPlatform/training-data-analyst

2. Confirm that you have cloned the repository. Double-click on the training-data-analyst directory and ensure that you can see its contents.

Wait for the cloning to complete.

Task 3. Open the notebook

1. Open training-data-analyst > blogs > bitcoin_network > visualizing_the_10000_pizza_bitcoin_network.ipynb.

2. In the notebook interface, click on Edit > Clear All Outputs.

3. Read through the notebook and execute the code to perform the data extraction, cleanup, and visualization.

Click Check my progress to verify the objective. Load the notebook

Task 4. Insights

The resulting visualization moves beyond raw data to offer tangible insights:

Network Structure: The graph illustrates the decentralized nature of the Bitcoin network and the complex, multi-path flow of value as transactions are chained together.

Tracing Activity: By visualizing the transactions, you can see how analytical techniques can be applied to trace and monitor activity on a public ledger, a fundamental concept in blockchain forensics and compliance.

Congratulations!

This lab successfully demonstrated the end-to-end process of extracting, processing, and visualizing complex, real-world blockchain data.

Next Steps

• Read more directly from the Google Cloud blog post Bitcoin in BigQuery: blockchain analytics on public data

Manual Last Updated October 29, 2025

Lab Last Tested October 29, 2025

Copyright 2025 Google LLC. All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.