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