GSP685

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 Google Cloud 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.

What you'll learn to do

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.

• Query a public dataset
• Create a new dataset
• Load data into a new table
• Query a custom table

Setup and requirements

• Labs are timed and cannot be paused. The timer starts when you click Start Lab.
• The included cloud terminal is preconfigured with the gcloud SDK.
• Use the terminal to execute commands and then click Check my progress to verify your work.

Task 1. 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 the following command in cloud terminal:

bq show bigquery-public-data:samples.shakespeare

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)

Task 2. Run the help command

When you include a command name with the help commands, you get information about that specific command.

1. For example, the following call to bq help retrieves information about the query command:

bq help query

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

Task 3. Run a query

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

1. 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").

2. Run the following standard SQL query in Cloud terminal 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'

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 |

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

Click Check my progress to verify the objective.

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.

3. Run the following search for huzzah, which returns no matches:

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

Click Check my progress to verify the objective.

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

Task 4. 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

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

bq ls

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

2. 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:

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 ...

Next, 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.

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

bq mk babynames

Sample output:

Dataset 'qwiklabs-xxx-xx-xxxxxxxxxxxx:babynames' successfully created..

Click Check my progress to verify the objective.

Create a new dataset (name: babynames)

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

bq ls

Sample output:

datasetId ------------- babynames

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.

5. 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

6. List the file:

ls

See the name of the file added to your project.

7. Now unzip the file:

unzip names.zip

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

ls

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:integer

9. Create your table:

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

Sample output:

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

Click Check my progress to verify the objective.

Load the data into a new table

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

bq ls babynames

Output:

tableId Type ----------- ------- names2010 TABLE

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

bq show babynames.names2010

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: integer

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. Learn more about Character Encodings from the Introduction to loading data guide.

Task 5. Run queries

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

1. 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"

Output:

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

2. 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"

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 | +----------+-------+

Click Check my progress to verify the objective.

Run queries against your dataset table

Task 6. Clean up

1. 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

2. Confirm the delete command by typing Y.

Click Check my progress to verify the objective.

Remove the babynames dataset

Congratulations!

Now you can use the command line to query public tables and load sample data into BigQuery.

Next steps / Learn more

Learn more about the BigQuery and bq command-line tool:

• Introduction to BigQuery
• Read more about bq-command-line-tool

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Manual Last Updated May 26, 2025

Lab Last Tested May 26, 2025

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