GSP071

Overview

Storing and querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. BigQuery is a serverless, highly scalable cloud data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. Simply move your data into BigQuery and let us handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

You can access BigQuery by using the Console, Web UI or a command-line tool using a variety of client libraries such as Java, .NET, or Python. There are also a variety of solution providers that you can use to interact with BigQuery.

This hands-on lab shows you how to use bq, the python-based command line tool for BigQuery, to query public tables and load sample data into BigQuery.

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 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
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(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
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(Output)

[core]
project = <project_ID>content_copy

(Example output)

[core]
project = qwiklabs-gcp-44776a13dea667a6content_copy

Examine a table

BigQuery offers a number of sample tables that you can run queries against. In this lab, you'll run queries against the shakespeare table, which contains an entry for every word in every play.

To examine the schema of the Shakespeare table in the samples dataset, run:

bq show bigquery-public-data:samples.shakespeare
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In this command you're doing the following:

• bq to invoke the BigQuery command line tool
• show is the action
• then you're listing the name of the project:public dataset.table in BigQuery that you want to see.

Output:

   Last modified                  Schema                 Total Rows   Total Bytes   Expiration   Time Partitioning   Clustered Fields   Labels
 ----------------- ------------------------------------ ------------ ------------- ------------ ------------------- ------------------ --------
  14 Mar 13:16:45   |- word: string (required)           164656       6432064
                    |- word_count: integer (required)
                    |- corpus: string (required)
                    |- corpus_date: integer (required)content_copy

Run the help command

When you include a command name with the help commands, you get information about that specific command. For example, the following call to bq help retrieves information about the query command.

bq help query
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To see a list of all of the commands bq uses, run just bq help.

Run a query

Now you'll run a query to see how many times the substring "raisin" appears in Shakespeare's works.

To run a query, run the command bq query "[SQL_STATEMENT]".

• Escape any quotation marks inside the [SQL_STATEMENT] with a \ mark, or

• Use a different quotation mark type than the surrounding marks ("versus").

Run the following standard SQL query in Cloud Shell to count the number of times that the substring "raisin" appears in all of Shakespeare's works:

bq query --use_legacy_sql=false \
'SELECT
   word,
   SUM(word_count) AS count
 FROM
   `bigquery-public-data`.samples.shakespeare
 WHERE
   word LIKE "%raisin%"
 GROUP BY
   word'
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In this command:

• --use_legacy_sql=false makes standard SQL the default query syntax

Output:

Waiting on job_e19 ... (0s) Current status: DONE
+---------------+-------+
|     word      | count |
+---------------+-------+
| praising      |     8 |
| Praising      |     4 |
| raising       |     5 |
| dispraising   |     2 |
| dispraisingly |     1 |
| raisins       |     1 |content_copy

The table demonstrates that although the actual word raisin doesn't appear, the letters appear in order in several of Shakespeare's works.

Test Completed Task

Click Check my progress to verify your performed task. If you have successfully run query against public dataset, you will see an assessment score.

Run a query (dataset: samples, table: shakespeare, substring: raisin)

If you search for a word that isn't in Shakespeare's works, no results are returned.

Run following search for "huzzah", returns no matches:

bq query --use_legacy_sql=false \
'SELECT
   word
 FROM
   `bigquery-public-data`.samples.shakespeare
 WHERE
   word = "huzzah"'
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Test Completed Task

Click Check my progress to verify your performed task. If you have successfully run query against public dataset, you will see an assessment score.

Run a query (dataset: samples, table: shakespeare, substring: huzzah)

Create a new table

Now create your own table. Every table is stored inside a dataset. A dataset is a group of resources, such as tables and views.

Create a new dataset

Use the bq ls command to list any existing datasets in your project:

bq ls
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You will be brought back to the command line since there aren't any datasets in your project yet.

Run bq ls and the bigquery-public-data Project ID to list the datasets in that specific project, followed by a colon (:).

bq ls bigquery-public-data:
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Output:

           datasetId
 -----------------------------
  austin_311
  austin_bikeshare
  austin_crime
  austin_incidents
  austin_waste
  baseball
  bitcoin_blockchain
  bls
  census_bureau_construction
  census_bureau_international
  census_bureau_usa
  census_utility
  chicago_crime
  ...content_copy

Now create a dataset. A dataset name can be up to 1,024 characters long, and consist of A-Z, a-z, 0-9, and the underscore, but it cannot start with a number or underscore, or have spaces.

Use the bq mk command to create a new dataset named babynames in your Qwiklabs project:

bq mk babynames
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Sample output:

Dataset 'qwiklabs-gcp-ba3466847fe3cec0:babynames' successfully created.content_copy

Test Completed Task

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

Create a new dataset (name: babynames)

Run bq ls to confirm that the dataset now appears as part of your project:

bq ls
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Sample output:

 datasetId
 -------------
  babynamescontent_copy

Upload the dataset

Before you can build the table, you need to add the dataset to your project. The custom data file you'll use contains approximately 7 MB of data about popular baby names, provided by the US Social Security Administration.

Run this command to add the baby names zip file to your project, using the URL for the data file:

wget http://www.ssa.gov/OACT/babynames/names.zip
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List the file:

ls
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You can see the name of the file added to your project.

Now unzip the file:

unzip names.zip
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That's a pretty big list of text files! List the files again:

ls
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The bq load command creates or updates a table and loads data in a single step.

You will use the bq load command to load your source file into a new table called names2010 in the babynames dataset you just created. By default, this runs synchronously, and will take a few seconds to complete.

The bq load arguments you'll be running are:

datasetID: babynames
tableID: names2010
source: yob2010.txt
schema: name:string,gender:string,count:integercontent_copy

Create your table:

bq load babynames.names2010 yob2010.txt name:string,gender:string,count:integer
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Sample output:

Waiting on job_4f0c0878f6184119abfdae05f5194e65 ... (35s) Current status: DONEcontent_copy

Test Completed Task

Click Check my progress to verify your performed task. If you have successfully load data into dataset table, you will see an assessment score.

Load the data into a new table

Run bq ls and babynames to confirm that the table now appears your dataset:

bq ls babynames
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Output:

  tableId    Type
 ----------- -------
  names2010   TABLEcontent_copy

Run bq show and your dataset.table to see the schema:

bq show babynames.names2010
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Output:

   Last modified         Schema         Total Rows   Total Bytes     Expiration      Time Partitioning   Clustered Fields   Labels
 ----------------- ------------------- ------------ ------------- ----------------- ------------------- ------------------ --------
  13 Aug 14:37:34   |- name: string     34073        654482        12 Oct 14:37:34
                    |- gender: string
                    |- count: integercontent_copy

By default, when you load data, BigQuery expects UTF-8 encoded data. If you have data that is in ISO-8859-1 (or Latin-1) encoding and are having problems with your loaded data, you can tell BigQuery to treat your data as Latin-1 explicitly, using the -E flag. For more information, see Character Encodings.

Run queries

Now you're ready to query the data and return some interesting results.

Run the following command to return the top 5 most popular girls names:

bq query "SELECT name,count FROM babynames.names2010 WHERE gender = 'F' ORDER BY count DESC LIMIT 5"
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Output:

Waiting on job_58c0f5ca52764ef1902eba611b71c651 ... (0s) Current status: DONE
+----------+-------+
|   name   | count |
+----------+-------+
| Isabella | 22913 |
| Sophia   | 20643 |
| Emma     | 17345 |
| Olivia   | 17028 |
| Ava      | 15433 |
+----------+-------+content_copy

Run the following command to see the top 5 most unusual boys names.

bq query "SELECT name,count FROM babynames.names2010 WHERE gender = 'M' ORDER BY count ASC LIMIT 5"
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Note: The minimum count is 5 because the source data omits names with fewer than 5 occurrences.

Output:

Waiting on job_556ba2e5aad340a7b2818c3e3280b7a3 ... (1s) Current status: DONE
+----------+-------+
|   name   | count |
+----------+-------+
| Aaqib    |     5 |
| Aaidan   |     5 |
| Aadhavan |     5 |
| Aarian   |     5 |
| Aamarion |     5 |
+----------+-------+content_copy

Test Completed Task

Click Check my progress to verify your performed task. If you have successfully run query against custom dataset, you will see an assessment score.

Run queries against your dataset table

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.

Clean up

Run the bq rm command to remove the babynames dataset with the -r flag to delete all tables in the dataset.

bq rm -r babynames
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Confirm the delete command by typing "y".

Test Completed Task

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

Remove the babynames dataset

Congratulations!

Now you can use the command line with BigQuery to manipulate data.

Finish Your Quest

This self-paced lab is part of the Qwiklabs BigQuery for Data Warehousing, NCAA® March Madness®: Bracketology with Google Cloud, BigQuery Basics for Data Analysts, and Using the Cloud SDK Command Line Quests. A Quest is a series of related labs that form a learning path. Completing a 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 a Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

Next Steps / Learn More

This lab is also 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 lab catalog to find the next lab you'd like to take!

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Manual Last Updated September 04, 2020

Lab Last Tested September 04, 2020

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