GSP290

Overview

In this lab you build several Data Pipelines that ingest data from a publicly available dataset into BigQuery, using these Google Cloud services:

• Cloud Storage
• Dataflow
• BigQuery

You will create your own Data Pipeline, including the design considerations, as well as implementation details, to ensure that your prototype meets the requirements. Be sure to open the python files and read the comments when instructed to.

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 Qwiklabs 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.

What you need

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).
• Time to complete the lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab.

Note: If you are using a Pixelbook, open an Incognito window to run this 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 pop-up opens for you to select your payment method. On the left is a panel populated with the temporary credentials that you must use for this lab.

   

2. Copy the username, and then click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

   

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

3. In the Sign in page, paste the username that you copied from the Connection Details panel. Then copy and paste the password.

   Important: You must use the credentials from the Connection Details panel. Do not use your Qwiklabs credentials. If you have your own Google Cloud account, do not use it for this lab (avoids incurring charges).

4. 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.

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.

In the Cloud Console, in the top right toolbar, click the Activate Cloud Shell button.

Click Continue.

It takes a few moments to provision and connect to the environment. When you are connected, you are already authenticated, and the project is set to your PROJECT_ID. For example:

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

You can list the active account name with this command:

gcloud auth list
content_copy

(Output)

Credentialed accounts:
 - <myaccount>@<mydomain>.com (active)content_copy

(Example output)

Credentialed accounts:
 - google1623327_student@qwiklabs.netcontent_copy

You can list the project ID with this command:

gcloud config list project
content_copy

(Output)

[core]
project = <project_ID>content_copy

(Example output)

[core]
project = qwiklabs-gcp-44776a13dea667a6content_copy

Check project permissions

Before you begin your work on Google Cloud, you need to ensure that your project has the correct permissions within Identity and Access Management (IAM).

1. In the Google Cloud console, on the Navigation menu (), click IAM & Admin > IAM.

2. Confirm that the default compute Service Account {project-number}[email protected] is present and has the editor role assigned. The account prefix is the project number, which you can find on Navigation menu > Home.

If the account is not present in IAM or does not have the editor role, follow the steps below to assign the required role.

• In the Google Cloud console, on the Navigation menu, click Home.

• Copy the project number (e.g. 729328892908).

• On the Navigation menu, click IAM & Admin > IAM.

• At the top of the IAM page, click Add.

• For New members, type:

{project-number}[email protected]
content_copy

Replace {project-number} with your project number.

• For Role, select Project (or Basic) > Editor. Click Save.

Download the starter code

Open a session in Cloud Shell and run the following command to get Dataflow Python Examples from Google Cloud's professional services GitHub:

gsutil -m cp -R gs://spls/gsp290/dataflow-python-examples .content_copy

Now set a variable equal to your project id, replacing <YOUR-PROJECT-ID> with your lab Project ID:

export PROJECT=<YOUR-PROJECT-ID>content_copy

gcloud config set project $PROJECT
content_copy

Create Cloud Storage Bucket

Use the make bucket command to create a new regional bucket in the us-central1 region within your project:

gsutil mb -c regional -l us-central1 gs://$PROJECTcontent_copy

Test Completed Task

Click Check my progress to verify your performed task.

Create a Cloud Storage Bucket

Copy Files to Your Bucket

Use the gsutil command to copy files to the Cloud Storage bucket you just created:

gsutil cp gs://spls/gsp290/data_files/usa_names.csv gs://$PROJECT/data_files/
gsutil cp gs://spls/gsp290/data_files/head_usa_names.csv gs://$PROJECT/data_files/content_copy

Test Completed Task

Click Check my progress to verify your performed task.

Copy Files to Your Bucket

Create the BigQuery Dataset

Create a dataset in BigQuery called lake. This is where all of your tables will be loaded in BigQuery:

bq mk lakecontent_copy

Test Completed Task

Click Check my progress to verify your performed task.

Create the BigQuery Dataset (name: lake)

Build a Dataflow Pipeline

In this section you will create an append-only Dataflow which will ingest data into the BigQuery table. You can use the built-in code editor which will allow you to view and edit the code in the Google Cloud console.

Open Code Editor

Navigate to the source code by clicking on the Open Editor icon in Cloud Shell:

If prompted click on Open in New Window. It will open the code editor in new window.

Data Ingestion

You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, it will:

• Ingest the files from Cloud Storage.
• Filter out the header row in the files.
• Convert the lines read to dictionary objects.
• Output the rows to BigQuery.

Review pipeline python code

In the Code Editor navigate to dataflow-python-examples > dataflow_python_examples and open the data_ingestion.py file. Read through the comments in the file, which explain what the code is doing. This code will populate the data in BigQuery.

Run the Apache Beam Pipeline

Return to your Cloud Shell session for this step. You will now do a bit of set up for the required python libraries.

Run the following to set up the python environment:

cd dataflow-python-examples/
# Here we set up the python environment.
# Pip is a tool, similar to maven in the java world
sudo pip install virtualenv

#Dataflow requires python 3.7
virtualenv -p python3 venv

source venv/bin/activate
pip install apache-beam[gcp]==2.24.0content_copy

You will run the Dataflow pipeline in the cloud.

The following will spin up the workers required, and shut them down when complete:

python dataflow_python_examples/data_ingestion.py --project=$PROJECT --region=us-central1 --runner=DataflowRunner --staging_location=gs://$PROJECT/test --temp_location gs://$PROJECT/test --input gs://$PROJECT/data_files/head_usa_names.csv --save_main_sessioncontent_copy

Return to the Cloud Console and open the Navigation menu > Dataflow to view the status of your job.

Click on the name of your job to watch it's progress. Once your Job Status is Succeeded.

Navigate to BigQuery (Navigation menu > BigQuery) see that your data has been populated.

Click on your project name to see the usa_names table under the lake dataset.

Click on the table then navigate to the Preview tab to see examples of the usa_names data.

Note: If you don't see the usa_names table, try refreshing the page or view the tables using the classic BigQuery UI.

Test Completed Task

Click Check my progress to verify your performed task.

Build a Data Ingestion Dataflow Pipeline

Data Transformation

You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, you will:

• Ingest the files from Cloud Storage.
• Convert the lines read to dictionary objects.
• Transform the data which contains the year to a format BigQuery understands as a date.
• Output the rows to BigQuery.

Review pipeline python code

Navigate to data_transformation.py and open it in Code Editor. Read through the comments in the file which explain what the code is doing.

Run the Apache Beam Pipeline

You will run the Dataflow pipeline in the cloud. This will spin up the workers required, and shut them down when complete.

Run the following commands to do so:

python dataflow_python_examples/data_transformation.py --project=$PROJECT --region=us-central1 --runner=DataflowRunner --staging_location=gs://$PROJECT/test --temp_location gs://$PROJECT/test --input gs://$PROJECT/data_files/head_usa_names.csv --save_main_sessioncontent_copy

Navigate to Navigation menu > Dataflow and click on the name of this job view the status.

When your Job Status is Succeeded in the Dataflow Job Status screen, navigate to BigQuery to check to see that your data has been populated.

You should see the usa_names_transformed table under the lake dataset.

Click on the table and navigate to the Preview tab to see examples of the usa_names_transformed data.

Note: If you don't see the usa_names_transformed table, try refreshing the page or view the tables using the classic BigQuery UI.

Test Completed Task

Click Check my progress to verify your performed task.

Build a Data Transformation Dataflow Pipeline

Data Enrichment

You will now build a Dataflow pipeline with a TextIO source and a BigQueryIO destination to ingest data into BigQuery. More specifically, you will:

• Ingest the files from Cloud Storage.
• Filter out the header row in the files.
• Convert the lines read to dictionary objects.
• Output the rows to BigQuery.

Review pipeline python code

Navigate to data_enrichment.py and open it in Code Editor. Check out the comments which explain what the code is doing. This code will populate the data in BigQuery.

Line 83 currently looks like:

values = [x.decode('utf8') for x in csv_row]
content_copy

Edit it so it looks like the following:

values = [x for x in csv_row]
content_copy

Run the Apache Beam Pipeline

Here you'll run the Dataflow pipeline in the cloud. Run the following to spin up the workers required, and shut them down when complete:

python dataflow_python_examples/data_enrichment.py --project=$PROJECT --region=us-central1 --runner=DataflowRunner --staging_location=gs://$PROJECT/test --temp_location gs://$PROJECT/test --input gs://$PROJECT/data_files/head_usa_names.csv --save_main_sessioncontent_copy

Navigate to Navigation menu > Dataflow to view the status of your job.

Once your Job Status is Succeed in the Dataflow Job Status screen, navigate to BigQuery to check to see that your data has been populated.

You should see the usa_names_enriched table under the lake dataset.

Click on the table and navigate to the Preview tab to see examples of data for the data.

Note: If you don't see the usa_names_enriched table, try refreshing the page or view the tables using the classic BigQuery UI.

Test Completed Task

Click Check my progress to verify your performed task.

Build a Data Enrichment Dataflow Pipeline

Data lake to Mart

Now build a Dataflow pipeline that reads data from 2 BigQuery data sources, and then joins the data sources. Specifically, you:

• Ingest files from 2 BigQuery sources.
• Join the 2 data sources.
• Filter out the header row in the files.
• Convert the lines read to dictionary objects.
• Output the rows to BigQuery.

Review pipeline python code

Navigate to data_lake_to_mart.py and open it in Code Editor. Read through the comments in the file which explain what the code is doing. This code will populate the data in BigQuery.

Run the Apache Beam Pipeline

Now you'll run the Dataflow pipeline in the cloud. Run the following to spin up the workers required, and shut them down when complete:

python dataflow_python_examples/data_lake_to_mart.py --worker_disk_type="compute.googleapis.com/projects//zones//diskTypes/pd-ssd" --max_num_workers=4 --project=$PROJECT --runner=DataflowRunner --staging_location=gs://$PROJECT/test --temp_location gs://$PROJECT/test --save_main_session --region=us-central1content_copy

Navigate to Navigation menu > Dataflow and click on the name of this new job to view the status.

Once you've Job Status is Succeeded in the Dataflow Job Status screen, navigate to BigQuery to check to see that your data has been populated.

You should see the orders_denormalized_sideinput table under the lake dataset.

Click on the table and navigate to the Preview section to see examples of orders_denormalized_sideinput data.

Note: If you don't see the orders_denormalized_sideinput table, try refreshing the page or view the tables using the classic BigQuery UI.

Test Completed Task

Click Check my progress to verify your performed task.

Build a Data lake to Mart Dataflow Pipeline

Test your Understanding

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

Congratulations!

You have used python files to ingest data into BigQuery using Dataflow.

Finish your Quest

This self-paced lab is part of the Qwiklabs Data Engineering Quest. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, 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 and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

Take Your Next Lab

Continue your Quest with Predict Visitor Purchases with a Classification Model in BQML, or check out these suggestions:

• Predict Housing Prices with Tensorflow and AI Platform

• Cloud Composer: Copy BigQuery Tables Across Different Locations

Next steps / learn more

Looking for more? Check out official documentation on:

• Google Dataflow
• BigQuery
• Also review the Apache Beam Programming Guide for more advanced concepts.

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Manual Last Updated October 26, 2020

Lab Last Tested October 26, 2020

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