GSP1345

Overview

Imagine you are a data engineer for Cymbal Gaming. You and the development team are building a new e-sports game, "Galactic Grand Prix." The game produces real-time data based on heads-up games between two players from different teams. For example, two players compete in an event, a winner is determined, and points are awarded to the winning player and team. You are tasked with creating recommendations for DLC purchase data. If a player purchases DLC for their character in Galactic Grand Prix, you will offer them other items based on what other competitors are purchasing.

You have read about Pub/Sub and know that it can be used with streaming data in Google Cloud. You have also read about Dataflow (Google Cloud's managed Apache Beam service), and Bigtable a NoSQL database on Google Cloud. You suspect that these services can be used with a Jupyter notebook to build a solution to offer other DLC items to customers based on purchases, but you are not sure where to start.

When you start the lab, the environment will contain the resources that are shown in the following diagram.

By the end of the lab, you will have used the architecture to perform several tasks.

The following table provides a detailed explanation of each task in relation to the lab architecture.

Objectives

In this lab, you will:

• Train a PyTorch model with purchase data, represented as tensors.
• Enrich new purchases from Pub/Sub with demographic data.
• Recommend products to new customers using a Dataflow Pipeline.

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).

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

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: Create the Google Cloud resources

In this task, you create the Google Cloud resources that support the lab, including the Pub/Sub Topic and subscription and Bigtable instance. Note: you will be using Cloud Shell commands to create these resources to expedite the process.

To use gcloud CLI commands effectively, first access Google Cloud Shell and set environment variables.

Access Cloud Shell and set environment variables.

1. Open Cloud Shell, by clicking Activate Cloud Shell.

2. You need to authorize its use, as this is the first time you use Cloud Shell in this lab. To do this, click Continue and then Authorize in the pop-up window.

3. Run the following commands.

   export PROJECT_ID="{{{project_0.project_id|set at lab start}}}"

4. Run the commands below to confirm the variables are stored.

   echo ${PROJECT_ID}

   You will use these variables later in the lab.

Create the Bigtable instance

1. Run the following command to create the Bigtable instance.

   gcloud bigtable instances create instance \ --display-name="My Bigtable Instance" \ --cluster-config=id=my-cluster,zone={{{project_0.default_zone | Zone}}},nodes=1

   You should see a confirmation message indicating the instance was created.

   Note: You will create the Bigtable table later within your Jupyter notebook.

Create the Pub/Sub topic and subscription

1. Run the following command to create the Pub/Sub topic.

   gcloud pubsub topics create customer-data

   You should see a confirmation message indicating the Pub/Sub topic was created.

2. Run the following command to create the Pub/Sub subscription for your topic.

   gcloud pubsub subscriptions create customer-data_sub --topic=customer-data

   You should see a confirmation message indicating the subscription was added to the topic.

Click Check my progress to verify the objective. Create the Google Cloud resources.

Task 2. Open the notebook in Vertex AI Workbench and choose kernel

Task 2a. Open the notebook

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

2. Find the instance and click on the Open JupyterLab button.

The JupyterLab interface for your Workbench instance opens in a new browser tab.

Task 2b. Choose the kernel

1. Open the file.

2. In the Select Kernel dialog, choose Python 3 from the list of available kernels.

Task 3. Import Python packages and set environment variables

In this task, you import the necessary Python packages to use this notebook and set environment variables.

1. Complete the steps in Task 3, Install packages, and configure the notebook section of the notebook.

   For Project ID, use , for instance_id, use instance, and for table_id use customer_data. Recall that you created the Bigtable instance in Task 1. Setting these values now will ensure you can successfully complete later tasks in this notebook and failure to do so may generate errors.

Click Check my progress to verify the objective. Install packages and configure the notebook.

Task 4. Train the model

In this task, you create the dataset and convert the data within it to tensors. You will then build the PyTorch model and train it with these tensors.

1. Complete the Train the model section of the notebook.

Click Check my progress to verify the objective. Train the model.

Task 5. Configure the Bigtable table and publish messages to Pub/Sub

In this task, you will create and configure the Bigtable table with the column families, then load data to the table. You will also publish messages to Pub/Sub.

1. Run through the Configure the Bigtable table and publish messages to Pub/Sub section of the notebook.

Click Check my progress to verify the objective. Configure the Bigtable table and publish messages to Pub/Sub.

Task 6. Use the Bigtable enrichment handler and transform

In this task, you will use the Bigtable enrichment handler, a specialized connector used within your Dataflow pipeline, that performs the enrichment process using Bigtable as the data source. You will also use the Bigtable enrichment transform. Think of the Bigtable enrichment handler as the tool and the transform as the verb that initiates it in your code.

1. Run through the Use the Bigtable enrichment handler and transform section of the notebook.

Click Check my progress to verify the objective. Use the Bigtable enrichment handler and transform.

Task 7. Run inference and the pipeline

In this task, you will run inference with the PyTorch model and run the dataflow pipeline.

1. Run through the Run inference and the pipeline section of the notebook.

Click Check my progress to verify the objective. Run inference and the pipeline.

Congratulations!

In this lab, you learned how to train a PyTorch model with purchase data represented as tensors. You also learned how to enrich new purchases from Pub/Sub with demographic data and then recommend new DLC products to customers with your Dataflow pipeline.

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Manual Last Updated January 22, 2026

Lab Last Tested January 22, 2026

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