GSP192

Overview

In this lab, you will learn how to create a streaming pipeline using one of Google's Cloud Dataflow templates. More specifically, you will use the Cloud Pub/Sub to BigQuery template, which reads messages written in JSON from a Pub/Sub topic and pushes them to a BigQuery table. You can find the documentation for this template in the Get started with Google-provided templates Guide.

You'll be given the option to use the Cloud Shell command line or the Cloud Console to create the BigQuery dataset and table. Pick one method to use, then continue with that method for the rest of the lab. If you want experience using both methods, run through this lab a second time.

Setup

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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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 or private browser window to run this lab. This prevents any 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: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your 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 pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

   • The Open Google 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 Console. 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 from the Lab Details panel and paste it into the Sign in dialog. Click Next.

4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

   Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

5. 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 Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. The output contains a line that declares the PROJECT_ID for this session:

Your Cloud Platform project in this session is set to YOUR_PROJECT_ID

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

2. (Optional) You can list the active account name with this command:

gcloud auth list

3. Click Authorize.

4. Your output should now look like this:

Output:

ACTIVE: * ACCOUNT: student-01-xxxxxxxxxxxx@qwiklabs.net To set the active account, run: $ gcloud config set account `ACCOUNT`

5. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = <project_ID>

Example output:

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Ensure that the Dataflow API is successfully enabled

To ensure access to the necessary API, restart the connection to the Dataflow API.

1. In the Cloud Console, enter "Dataflow API" in the top search bar. Click on the result for Dataflow API.

2. Click Manage.

3. Click Disable API.

If asked to confirm, click Disable.

4. Click Enable.

When the API has been enabled again, the page will show the option to disable.

Task 1. Create a Cloud BigQuery dataset and table Using Cloud Shell

Let's first create a BigQuery dataset and table.

Note: This section uses the bq command-line tool. Skip down if you want to run through this lab using the console.

1. Run the following command to create a dataset called taxirides:

bq mk taxirides

Your output should look similar to:

Dataset '' successfully created

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a BigQuery dataset, you will see an assessment score.

Create a BigQuery Dataset (name: taxirides)

Now that you have your dataset created, you'll use it in the following step to instantiate a BigQuery table.

2. Run the following command to do so:

bq mk \ --time_partitioning_field timestamp \ --schema ride_id:string,point_idx:integer,latitude:float,longitude:float,\ timestamp:timestamp,meter_reading:float,meter_increment:float,ride_status:string,\ passenger_count:integer -t taxirides.realtime

Your output should look similar to:

Table 'myprojectid:taxirides.realtime' successfully created

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a table in BigQuery dataset, you will see an assessment score.

Create a table in BigQuery Dataset

On its face, the bq mk command looks a bit complicated. However, with some assistance from the BigQuery command-line documentation, we can break down what's going on here. For example, the documentation tells us a little bit more about schema:

• Either the path to a local JSON schema file or a comma-separated list of column definitions in the form [FIELD]:[DATA_TYPE], [FIELD]:[DATA_TYPE].

In this case, we are using the latter—a comma-separated list.

Create a storage bucket

Now that we have our table instantiated, let's create a bucket.

Use the Project ID as the bucket name to ensure a globally unique name:

• Run the following commands to do so:

export BUCKET_NAME={{{project_0.project_id | "filled in at lab start"}}} gsutil mb gs://$BUCKET_NAME/

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a Cloud Storage bucket, you will see an assessment score.

Create a storage bucket

Once you've made your bucket, scroll down to the Run the Pipeline section.

Task 2. Create a Cloud BigQuery dataset and table using the Cloud Console

Note: Don't go through this section if you've done the command-line setup!

1. From the left-hand menu, in the Big Data section, click on BigQuery.

2. Then click Done.

3. Click on the three dots next to your project name under the Explorer section, then click Create dataset.

4. Input taxirides as your dataset ID:

5. Select us (multiple regions in United States) in Data location.

6. Leave all of the other default settings in place and click CREATE DATASET.

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a BigQuery dataset, you will see an assessment score.

Create a BigQuery Dataset (name: taxirides)

7. You should now see the taxirides dataset underneath your project ID in the left-hand console.

8. Click on the three dots next to taxirides dataset and select Open.

9. Then select CREATE TABLE in the right-hand side of the console.

10. In the Destination > Table Name input, enter realtime.

11. Under Schema, toggle the Edit as text slider and enter the following:

ride_id:string,point_idx:integer,latitude:float,longitude:float,timestamp:timestamp, meter_reading:float,meter_increment:float,ride_status:string,passenger_count:integer

Your console should look like the following:

12. Now, click Create table.

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a table in BigQuery dataset, you will see an assessment score.

Create a table in BigQuery Dataset

Create a storage bucket

1. Go back to the Cloud Console and navigate to Cloud Storage > Buckets > Create bucket.

2. Use the Project ID as the bucket name to ensure a globally unique name:

3. Leave all other default settings, then click Create.

Test completed task

Click Check my progress to verify your performed task. If you have successfully created a Cloud Storage bucket, you will see an assessment score.

Create a storage bucket

Task 3. Run the pipeline

1. From the Navigation menu, find the Analytics section and click on Dataflow.

2. Click on + Create job from template at the top of the screen.

3. Enter iotflow as the Job name for your Cloud Dataflow job and select for Regional Endpoint.

4. Under Dataflow Template, select the Pub/Sub Topic to BigQuery template.

5. Under Input Pub/Sub topic, click Enter Topic Manually and enter:

projects/pubsub-public-data/topics/taxirides-realtime

6. Under BigQuery output table, enter the name of the table that was created:

{{{project_0.project_id | "filled in at lab start"}}}:taxirides.realtime

7. Add your bucket name as Temporary Location after gs://:

{{{project_0.project_id | "filled in at lab start"}}}/temp

8. Click the Run job button.

Test completed task

Click Check my progress to verify your performed task. If you have successfully run the Dataflow pipeline, you will see an assessment score.

Run the Pipeline.

You'll watch your resources build and become ready for use.

Now, let's go view the data written to BigQuery by clicking on BigQuery found in the Navigation menu.

• When the BigQuery UI opens, you'll see the taxirides dataset added under your project name and realtime table underneath that.

Task 4. Submit a query

You can submit queries using standard SQL.

1. In the BigQuery Editor, add the following to query the data in your project:

SELECT * FROM `{{{project_0.project_id | "filled in at lab start"}}}.taxirides.realtime` LIMIT 1000

2. Now click RUN.

If you run into any issues or errors, run the query again (the pipeline takes a minute to start up.)

3. When the query runs successfully, you'll see the output in the Query Results panel as shown below:

Great work! You just pulled 1000 taxi rides from a Pub/Sub topic and pushed them to a BigQuery table. As you saw firsthand, templates are a practical, easy-to-use way to run Dataflow jobs. Be sure to check out, in the Dataflow Documentation, some other Google Templates in the Get started with Google-provided templates Guide.

Task 5. Test your understanding

Below are multiple choice questions to reinforce your understanding of this lab's concepts. Answer them to the best of your abilities.

Congratulations!

Finish your quest

This self-paced lab is part of the Baseline: Data, ML, AI quest. A quest is a series of related labs that form a learning path. Completing this quest earns you a badge to recognize your achievement. You can make your badge or badges public and link to them in your online resume or social media account. Enroll in this quest or any quest that contains this lab and get immediate completion credit. See the Google Cloud Skills Boost catalog to see all available quests.

Next steps / Learn more

This lab is part of a series of labs called Qwik Starts. These labs are designed to give you a little taste of the many features available with Google Cloud. Search for "Qwik Starts" in the Google Cloud Skills Boost catalog to find the next lab you'd like to take!

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Manual Last Updated April 7, 2023

Lab Last Tested April 7, 2023

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