Loading data into Google BigQuery

This guide explains how to load data into Google BigQuery for further analysis.

Prerequisites

Before you complete the procedure in this guide, perform all of the following actions:

• Create a datastream whose data you want to load into Google BigQuery. For more information on creating a datastream, see Creating a datastream.

• Apply the correct Data Mapping to your datastream.

Procedure

To load data from a datastream into Google BigQuery, follow these steps:

1. Add Google BigQuery as a destination to the workspace which contains the datastream or to one of its parent workspaces.

2. Assign the Google BigQuery destination to the datastream.

   You can assign as many destinations to a datastream as you want.

   Some destinations require specific Data Mapping, such as Hubspot and Facebook Offline Conversions. If these Data Mapping requirements conflict, the destinations cannot be assigned to the same datastream.

3. Configure load settings.

Adding Google BigQuery as a destination

To add Google BigQuery as a destination to a workspace, follow these steps:

1. Select the workspace you work with in Adverity and then, in the platform navigation menu, click Destinations.

2. Click + Add Destination.

3. Search for and click Google BigQuery.

4. Choose how to authorize Adverity to access Google BigQuery:

   • To use your details, click Access Google BigQuery using your credentials.

   • To ask someone else to use their details, click Access Google BigQuery using someone else's credentials.

     If you choose this option, the person you ask to create the authorization will need to go through the following steps.

5. Click Next.

6. Select one of the following options:

   • To connect to Google BigQuery with your own account, click Google BigQuery (OAuth2), and log in with your user credentials.

   • To connect to Google BigQuery with a service account, click Google BigQuery (Service Account), upload the JSON service account key.

7. (Optional) When connecting to Google BigQuery with a service account, select Retrieve projects from Google Cloud API to retrieve several projects that the service account can access.

8. Click Authorize.

9. In the Configuration page, fill in the following fields:

   Name

   (Optional) Rename the destination.

   Cloud Storage

   If you load data into Google BigQuery as a batch operation, select the storage you have created using the Google Cloud Storage authorization.

   Project

   Specify the name of the project into which you want to load the data.

   Dataset

   Specify the name of the dataset into which you want to load the data.

   For more information on advanced configuration settings, see Advanced Google BigQuery tips.

10. Click Create.

Assigning Google BigQuery as a destination

To assign the Google BigQuery destination to a datastream, follow these steps:

1. Select the workspace you work with in Adverity and then, in the platform navigation menu, click Datastreams.

2. Open the chosen datastream by clicking on its name.

3. In the External destinations section, click + Assign destination.

4. Click Assign existing destination.

5. Select the Google BigQuery checkbox in the list.

6. Click Save.

Configuring settings for loading data into Google BigQuery

To configure the settings for loading data into Google BigQuery, follow these steps:

1. Select the workspace you work with in Adverity and then, in the platform navigation menu, click Datastreams.

2. Open the chosen datastream by clicking on its name.

3. In the Destinations section, find the Google BigQuery destination in the list, and click on the right.

4. Click Destination settings.

5. Fill in the following fields:

   Table name

   Specify the target table in the destination into which to load data from the datastream. The name can contain alphanumeric characters and underscores. For example, target_table.

   By default, Adverity saves data from each datastream in a different table named {datastream_type}_{datastream_id} (for example, mailgun_83).

   You can specify the same target table for several datastreams. If a column is shared between datastreams, Adverity performs a full outer join and concatenates values. If a column is not shared between datastreams, Adverity writes null values in the relevant cells.

   • To create a new Google BigQuery spreadsheet containing the data you load into Google BigQuery, enter a name for the new spreadsheet into this field.

   You can use the following placeholders when creating new table names in the destination:

   Placeholder	Description
   {app_label}	The data source's short name.
   {datastream_id}	The datastream ID.
   {datastream_type}	The data source.
   {extract_id}	The data extract ID.
   {id}	The datastream ID.
   {meta[*]}	Replace * with a metadata placeholder to use metadata in the table name. For example, {meta[datastream_URI]} uses the datastream URI as the table name. For more information on metadata and placeholders, see Using placeholders.
   {name}	The automatically generated filename of the data extract.
   {scheduled_day}	The day when the data fetch was scheduled to run.
   {scheduled_month}	The month when the data fetch was scheduled to run.
   {scheduled_year}	The year when the data fetch was scheduled to run.
   {upload_day}	The day when the data extract is loaded into the Google BigQuery destination.
   {upload_hour}	The hour when the data extract is loaded into the Google BigQuery destination.
   {upload_minute}	The minute when the data extract is loaded into the Google BigQuery destination.
   {upload_month}	The month when the data extract is loaded into the Google BigQuery destination.
   {upload_second}	The second when the data extract is loaded into the Google BigQuery destination.
   {upload_year}	The year when the data extract is loaded into the Google BigQuery destination.

   Action for existing tables

   To specify how Adverity loads data from a data extract to the destination if the target table already exists, select one of the following options:

   • (Default) Select Truncate to remove all data from the relevant table in the destination before loading the corresponding data extract into your destination.

   • Select Append to add the data from the data extract to the relevant table in the destination without removing any existing data.

   • Select Error (no action) not to load data from the data extract to the destination if the target table already exists. Google BigQuery displays an error message each time Adverity cancels the data load into the destination because the target table already exists.

   Partition by date

   (Recommended) If selected, the target table is partitioned by a date column, and data is only replaced based on the date. This means that if you import data into a table with data already present for certain dates, the existing data for these overlapping dates is overwritten, and the data for other, unique dates remains unchanged.

   When this option is selected, select the Partition Date Column from the drop-down list.

   Table expiration

   Specify the number of days for which to keep data in the destination. To keep the table indefinitely, leave this field empty. If you want to remove the expiration period, change the value to 0. Changes to the expiration period will take place the next time you load data into Google BigQuery.

6. Click Save.

Advanced Google BigQuery tips

Configuration

To configure the Google BigQuery destination, follow these steps:

1. Select the workspace you work with in Adverity and then, in the platform navigation menu, click Destinations.

2. Click the Google BigQuery destination in the list.

3. In the secondary menu, click Data Mapping.

4. Fill in the following fields:

   Data Mapping

   By default, Adverity applies the Data Mapping conventions to the data extracts when loading them into the destination. For more information on Data Mapping, see Applying Data Mapping to a datastream.

   To load data into the destination without applying the Data Mapping conventions, clear this checkbox.

   Force string type

   By default, Adverity automatically recognizes the data type of each field. Select this checkbox to convert all fields to the String data type.

5. Click Save.

Video guide: How to transfer your data to Google BigQuery

This video guide explains how to create an authorization and configure your settings to load your data into Google BigQuery.