GSP1054

Overview

Bigtable is Google's fully managed, scalable NoSQL database service. Bigtable is ideal for storing large amounts of data in a key-value store and for use cases such as personalization, ad tech, financial tech, digital media, and Internet of Things (IoT). Bigtable supports high read and write throughput at low latency for fast access to large amounts of data for processing and analytics.

For personalization use cases, Bigtable can handle both high writes to store users' interactions with online products and high reads to provide these interactions to models that produce personalized recommendations.

In this lab, you use the Google Cloud Console to create a Bigtable instance with a table to store user interactions with products. Then, you use a Dataflow template to populate the table from pre-generated data files on Cloud Storage. After the Dataflow job has finished, you verify that the table has been successfully populated with data and then complete the lab by deleting the Bigtable data.

Objectives

In this lab, you learn how to create a Bigtable instance and load data from Cloud Storage without writing any code.

• Create a Bigtable instance and a Bigtable table with column families.
• Use a Dataflow template to load SequenceFile files from Cloud Storage into Bigtable.
• Verify the data loaded into Bigtable.
• Delete the Bigtable table and instance.

Prerequisites

• Basic understanding of database concepts and terms such as instances, schemas, and keys
• Completion of GSP1053 Designing and Querying Bigtable Schemas

Setup and requirements

Qwiklabs 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.

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 have a 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.

Cloud Console

Log in to Google Cloud Console

1. Using the browser tab or window you are using for this lab session, copy the Username from the Connection Details panel and click the Open Google Console button.

Note: If you are asked to choose an account, click Use another account.

2. Paste in the Username, and then the Password as prompted.
3. Click Next.
4. Accept the terms and conditions.

Since this is a temporary account, which will last only as long as this lab:

• Do not add recovery options
• Do not sign up for free trials

5. Once the console opens, view the list of services by clicking the Navigation menu () at the top-left.

Activate Cloud Shell

Cloud Shell is a virtual machine that contains development tools. It offers a persistent 5-GB home directory and runs on Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources. gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab completion.

1. Click the Activate Cloud Shell button () at the top right of the console.

2. Click Continue.
   It takes a few moments to provision and connect to the environment. When you are connected, you are also authenticated, and the project is set to your PROJECT_ID.

Sample commands

• List the active account name:

gcloud auth list

(Output)

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

(Example output)

Credentialed accounts: - google1623327_student@qwiklabs.net

• List the project ID:

gcloud config list project

(Output)

[core] project = <project_ID>

(Example output)

[core] project = qwiklabs-gcp-44776a13dea667a6 Note: Full documentation of gcloud is available in the gcloud CLI overview guide.

Check project permissions

Before you begin working on Google Cloud, you must 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}-compute@developer.gserviceaccount.com is present and has the editor role assigned. The account prefix is the project number, which you can find on Navigation menu > Cloud overview.

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

1. In the Google Cloud console, on the Navigation menu, click Cloud overview.

2. From the Project info card, copy the Project number.

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

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

5. For New principals, type:

{project-number}-compute@developer.gserviceaccount.com

Replace {project-number} with your project number.

6. For Select a role, select Basic (or Project) > Editor.

7. Click Save.

Dataflow API

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 Google Cloud Console, enter Dataflow API in the top search bar.

2. Click on the result for Dataflow API.

3. Click Manage.

4. Click Disable API.

   If you are asked to confirm, click Disable.

5. Click Enable.

Click Check my progress to verify the objective. Dataflow API

Task 1. Create a Bigtable instance

To create a new table in Bigtable, you first need to create a Bigtable instance to store your table.

1. In the Google Cloud Console, on the Navigation menu (), under Databases, click Bigtable.

2. Click Create instance.

3. Enter the required information to create a Bigtable instance:

Property	Value
Instance name	Personalized Sales
Instance ID	Leave the default value
Storage Type	SSD
Cluster ID	Leave the default value
Region
Zone
Node scaling mode	Manual allocation
Quantity	Leave the default value

4. Click Create.

Click Check my progress to verify the objective. Create a Bigtable instance.

Task 2. Create a new Bigtable table

In Bigtable, each row in a table has a unique identifier called a row key, and columns in a table are grouped by column family to organize related columns. In this task, you create a new Bigtable table with appropriate row keys and column families for user interactions.

First, you create a table named UserSessions to store user online interactions with various products and confirmed sales, using the user ID combined with the timestamp as the row key. Then you create two column families called Interactions and Sales to help organize the related columns.

1. From the list of Bigtable instances, click on the instance ID named personalized-sales.

2. In the navigation menu, under Instance, click Tables.

3. Click Create table.

4. For Table ID, type UserSessions

5. Click Add column family.

6. For Column family name, type Interactions

Leave the default value for garbage collection policy.

7. Click Done.

8. Repeat steps 5 through 7 to create another column family named Sales.

9. Click Create.

Click Check my progress to verify the objective. Create a new Bigtable table.

Task 3. Load data files from Cloud Storage using a Dataflow template

In this task, you run a Dataflow job to load data from Cloud Storage to Bigtable. In order to run the Dataflow job successfully, you first have to create a Cloud Storage bucket for Dataflow to write temporary files as needed. Then you can successfully create and run a new Dataflow job from a template.

Create a Cloud Storage bucket

1. In the Google Cloud Console, on the Navigation menu (), click Cloud overview > Dashboard.

2. Under Project info, copy the value for Project ID (such as ).

   You will use your Qwiklabs Project ID, which is already globally unique, as the Cloud Storage bucket name.

3. In the Google Cloud Console, on the Navigation menu (), click Cloud Storage > Buckets.

4. Click Create Bucket.

5. Enter the required information to create a Cloud Storage bucket, replacing project-id with the project ID you copied in step 2.

Property	Value
Name	project-id
Location type	Multi-region
Location	us (multiple regions in United States)

Leave the default values for the remaining parameters.

6. Click Create.

7. If prompted Public access will be prevented, click Confirm.

Create a Dataflow job using a template

1. In the Google Cloud Console, on the Navigation menu (), under Analytics, click Dataflow > Jobs.

2. Click Create job from template.

3. Enter the required information to create a Dataflow job from a template, replacing project-id with the project ID you previously copied.

Property	Value
Job name	import-usersessions
Regional endpoint
Dataflow template	SequenceFile Files on Cloud Storage to Cloud Bigtable
Project ID	project-id
Instance ID	personalized-sales
Table ID	UserSessions
Source path pattern	gs://cloud-training/OCBL377/retail-interactions-sales-00000-of-00001
Temporary location	gs://project-id/temp

Leave the default values for the remaining parameters.

4. Click Run Job.

5. On the Job Graph page, under Job steps view, select Graph view.

The Graph view displays a graph of how the job progresses to complete the following steps:

• Read the SequenceFiles on Cloud Storage.
• Mutate the data for loading into Bigtable.
• Write the data to Bigtable.

6. To see a table view of the same steps, select Table view.

When the job has successfully completed, a green check mark for a Succeeded status is displayed next to each task in the Job Graph. This job will take approximately 3 to 5 minutes to run successfully.

Click Check my progress to verify the objective. Load data files from Cloud Storage using a Dataflow template.

Task 4. Verify data loaded into Bigtable

After your Dataflow job has successfully completed, you can use cbt (Cloud Bigtable command-line tool) commands to connect to your Bigtable instance and verify that the table has been populated with data.

Configure the Bigtable CLI

To connect to Bigtable using cbt CLI commands, you first need to update the .cbtrc configuration file with your project ID and your Bigtable instance ID using Cloud Shell.

For a review of how to access Cloud Shell, click Setup and Requirements on the right-side menu of this page.

1. To modify the .cbtrc file with the project ID and instance ID, run the following commands in Cloud Shell:

echo project = `gcloud config get-value project` \ >> ~/.cbtrc echo instance = personalized-sales \ >> ~/.cbtrc

2. To verify that you successfully modified the .cbtrc file, run the following command:

cat ~/.cbtrc

The output should resemble the following:

project = <project-id> instance = personalized-sales

Query data in the table

After you configure the .cbtrc configuration file in Cloud Shell, you can run a simple cbt CLI command to query the first ten records of the table.

• To see the data for the first ten rows of the table, run the following command:

cbt read UserSessions \ count=10

The output is structured as follows:

---------------------------------------------- ROW KEY COLUMN_FAMILY:COLUMN_QUALIFIER @ TIMESTAMP VALUE

The output values will resemble the following:

---------------------------------------------- blue1#1638940844409 Interactions:blue_blouse @ 2022/06/07-18:34:28.858000 "purchased" Interactions:blue_jacket @ 2022/06/07-18:34:28.858000 "viewed details" Interactions:blue_shoes @ 2022/06/07-18:34:28.858000 "viewed details" Interactions:green_skirt @ 2022/06/07-18:34:28.858000 "seen" Interactions:orange_dress @ 2022/06/07-18:34:28.858000 "seen" Interactions:purple_dress @ 2022/06/07-18:34:28.858000 "seen" Interactions:purple_skirt @ 2022/06/07-18:34:28.858000 "seen" Interactions:user_id @ 2022/06/07-18:34:28.858000 "blue1" Interactions:yellow_blouse @ 2022/06/07-18:34:28.858000 "seen" Sales:sale @ 2022/06/07-18:34:28.858000 "blue_blouse"

Task 5. Delete a Bigtable table and instance

1. In the Google Cloud Console, on the Navigation menu (), under Databases, click Bigtable.

2. From the list of Bigtable Instances, click on the Instance ID named personalized-sales.

3. In the navigation menu, under Instance, click Tables.

4. Click on the table named UserSessions.

5. Click Delete table.

6. In the confirmation dialog, type UserSessions

7. Click Delete.

8. In the navigation menu, under Instance, click Overview.

9. Click Delete instance.

10. In the confirmation dialog, type personalized-sales

11. Click Delete.

Click Check my progress to verify the objective. Delete a Bigtable table and instance.

Congratulations!

You have now used Bigtable to create a new instance and table, loaded data into the table using a Dataflow template, and confirmed that the data was successfully loaded by running simple cbt CLI commands. Then you completed the lab by deleting the Bigtable table and instance.

Finish your quest

This self-paced lab is part of the Manage Bigtable on Google Cloud 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 and get immediate completion credit if you have successfully completed this lab. Search for other available quests in the Cloud Skills Boost catalog.

Take your next lab

Continue your quest with GSP1055 Streaming Data to Bigtable.

Manual Last Updated February 17, 2023

Lab Last Tested February 17, 2023

Copyright 2022 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.