Working with BigQuery in Google Cloud Shell

30 minutes
  • 5 Learning Objectives

About this Hands-on Lab

Cloud BigQuery boasts extremely fast processing—terabytes of information in seconds, petabytes in minutes—while remaining straightforward to query via standard SQL. Best of all, BigQuery is accessible through a number of methods, including web console and API. In this hands-on lab, you’ll use another pathway (Google Cloud Shell) to perform a series of BigQuery operations, including creating a dataset, defining a table and its schema, importing data into that table and, finally, running a series of SQL queries on the BigQuery platform.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Activate Cloud Shell
  1. Click the Activate Cloud Shell icon at the top of the console page.
Retrieve the Data Files
  1. In the Cloud Shell, run the following command:
    gsutil cp gs://acg-gcp-labs-resources/essentials/MetObjects_BQ_Lab.csv .
Create a BigQuery Dataset
  1. Create a BigQuery dataset with the following command:
    bq mk metobjects
  2. List the current datasets with the following command:
    bq ls
Load the Data
  1. Import the data into the dataset with the following command:
    bq load –source_format=CSV –skip_leading_rows=1 metobjects.linkedObjects MetObjects_BQ_Lab.csv title:STRING,artist:STRING,year:INTEGER,link:STRING
  2. View the dataset with the following commands:
    bq ls metobjects
    bq show metobjects.linkedObjects
Query the Dataset
  1. Execute a SQL query with the following command:
    bq query "SELECT year,title,artist FROM metobjects.linkedObjects WHERE year > 2000 ORDER BY year DESC LIMIT 15"
  2. Execute a second SQL query:
    bq query "SELECT title,link FROM metobjects.linkedObjects WHERE year > 2000 ORDER BY year DESC LIMIT 15"
  3. Test the available links.

Additional Resources

Your company is handling data analysis for a major metropolitan museum. You’ve been asked to set up the preliminary BigQuery dataset and table and load a large CSV file into the dataset. Naturally, you’ll need to run a few queries to verify that the data has been successfully imported.

You’ll need to complete the following steps to accomplish your task:

  1. Activate Cloud Shell.
  2. Copy the data file from the Cloud Storage bucket.
  3. Create a BigQuery dataset.
  4. Load data into the dataset.
  5. Query the data.

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

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