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Using Your BigQuery Environment

Use the BigQuery UI to create and manage BigQuery resources with your Analytics Environment data.


BigQuery is accessed from the Google Cloud Platform (GCP) console, which you open from the Analytics Environment virtual machine desktop.

BigQuery scripting enables you to send multiple statements to BigQuery in one request, to use variables, and to use control flow statements such as IF and WHILE.

BigQuery scripting feature provides some basic scripting building blocks but it is not adapted for some advanced scripting use cases. For example, table names and column names cannot use variables—although it is possible to store a SQL expression in a string variable and run it with the EXECUTE IMMEDIATE statement.

See Google's instructions.

BigQuery scheduling allows you to set a cadence for recurring queries, such as updating particular tables and views based on refreshes of data from your data sources. For example, you can automatically populate a view based on weekly sales metrics.

See Google's BigQuery scheduling instructions.

You must be assigned the Data Scientist persona to access this feature. Any Data Scientist persona within your tenant account team can create and edit queries within your BigQuery instance. Any queries created by inactive users are still available to other users with the Data Scientist persona.


Do not change dataset metadata.

If you have the Data Scientist persona, you can update datasets in order to schedule queries. This capability also allows you to modify dataset metadata in the BigQuery settings view, such as the dataset name and expiration date. Do not change any dataset metadata because it can negatively impact backend processes and cause errors.

You can schedule a BigQuery job to update a table you want to save to Customer Profiles and automate the job by specifying ALTER TABLE and SET OPTION to apply the sendtocustomerprofiles label. For example:

ALTER TABLE '<table_name>' SET OPTIONS (labels=[("sendtocustomerprofiles", "ready")])

Where: <table_name> is replaced by the name of your table.

BigQuery ML enables users to create and execute machine learning models in BigQuery using standard SQL queries. That allows data scientists to train, evaluate, and predict a machine learning model on a very large dataset within minutes and from the BigQuery console.

Use the _ai dataset of your tenant for storing the model description and output tables.

Supported Machine Learning algorithms:

  • Linear regression

  • Binary logistic regression

  • Multiclass logistic regression

  • K-means clustering

For more information, see Google's Introduction to BigQuery ML.