Create a Google BigQuery Connection
To access your data stored on a Google BigQuery database, you will need to know the server and database name that you want to connect to, and you must have access credentials. Once you have created a connection to a Google BigQuery database, you can select data from the available tables and then load that data into your app or document.
In Qlik Sense, you connect to a Google BigQuery database through the Add data dialog or the Data load editor.
In Qlik Cloud Data Integration, you connect to a Google BigQuery database when setting a target platform for a data project.
Creating a trusted storage certificate
If you do not have an appropriate storage certificate to use with the connector in your Windows environment, you will need to download one.
Do the following:
Go to Google Trust Services - Repository and navigate to the Download CA certificate section.
Select the GTS Root R2 certificate with fingerprint SHA1: 8d:25:cd:97:22:9d:bf:70:35:6b:da:4e:b3:cc:73:40:31:e2:4c:f0:0f:af:cf:d3:2d:c7:6e:b5:84:1c:7e:a8 .
Click Action and download the certificate as a PEM file.
Authenticating the Google account
The first step is to authenticate your Google account using the account properties below.
This field will only appear if you selected the "via Direct Access gateway" data connection. Select the data gateway through which you need to connect to your data source.
Information noteUsers that need to leverage gateway-enabled data connections must have the Can Consume Data permission for the space with which the gateway is associated.
Select User Authentication or Service Authentication mechanisms.
For Service Authentication, the Google service account must be BigQuery Job User for connections to multiple catalogs, or BigQuery Data Viewer for connections to a single catalog. Read more about how to configure permissions for the Google service account in the Google documentation.
|Sign In||Redirects to a Google sign-in page where you can obtain a confirmation code.||Yes, when User Authentication is selected.|
|Confirmation Code||The code obtained from the Google sign-in page.||Yes, when User Authentication is selected.|
|The service account email.||Yes, when Service Authentication is selected.|
|Key File||The uploaded key file.||Yes, when Service Authentication is selected.|
|File||Click to upload a key file.||Yes, when Service Authentication is selected.|
|Validate||Validates the confirmation code or the key.||Yes|
This option allows you to use a P12 private key file with a non-standard password. The specified value is the password your key file is encrypted with.
Information noteP12 private key file support is about to be deprecated. You can use JSON private key files instead.
|Catalog (Project)||The name of your BigQuery project. This project is the default project the Google BigQuery Connector queries against. The Google BigQuery Connector supports multiple catalogs, the equivalent of Google BigQuery projects.||Yes|
|Minimum TLS||The minimum version of TLS allowed for encrypting connections. For example, if TLS 1.1 is specified, TLS 1.0 cannot be used. Default value is TLS 1.2.||Yes|
Credentials are used to prove that a user is allowed to access the data in a connection.
There are two types of credentials that can be used when making a connection in Qlik Sense SaaS. If you leave the User defined credentials check box deselected, then only one set of credentials will be used for the connection. These credentials belong to the connection and will be used by anyone who can access it. For example, if the connection is in a shared space, every user in the space will be able to use these credentials. This one-to-one mapping is the default setting.
If you select User defined credentials, then every user who wants to access this connection will need to input their own credentials before selecting tables or loading data. These credentials belong to a user, not a connection. User defined credentials can be saved and used in multiple connections of the same connector type.
Creating new credentials
A page with an authentication code will open.
Click Copy to clipboard to copy the authentication code.
Return to the connection dialog and paste the authentication code.
Click Verify to check that the authentication is successful.
Set a name for the credentials in Credentials name.
Click Get Catalogs to populate Catalogs with the catalogs that you have access to.
In the Data load editor, you can click the underneath the connection to edit your credentials. In the hub or Data manager, you can edit credentials by right-clicking on the connection and selecting Edit Credentials.
Use this dropdown to select Standard SQL or Legacy SQL.
Standard SQL: is compliant with the SQL 2011 standard. It supports querying nested and repeated data. For more information, see SQL Reference.
Legacy SQL: Non-standard SQL dialect previously named BigQuery SQL. Legacy SQL was used prior to Google BigQuery 2.0. For more information, see Query Reference.
|Rows Per Block||Sets the maximum number of rows to fetch for each data request.|
|Default String Column Length||Sets the maximum number of characters that can be contained in string columns.|
Large Results Options
|Allow Large Result Sets||This option specifies the connector's response to query results larger than 128MB when using Legacy SQL.||No|
|Dataset Name For Large Result Sets||The ID of the existing BigQuery dataset that you want to use to store temporary tables for large result sets. Only specify a value for this option if you want to enable support for large result sets.||No|
|Query timeout||Amount of time before a data load query times out. Can be set from 30 seconds to 65535 seconds. Default is 30 seconds.|
|Retry timeout||The length of time, in seconds, for which the connector retries a failed API call before timing out. The specified value must be an integer. A value of 0 indicates no timeout. Default value is 300.|
Load optimization settings
|Max String Length|
Maximum length of string fields. This can be set from 256 to 16384 characters. The default value is 4096. Setting this value close to the maximum length may improve load times, as it limits the need to allocate unnecessary resources. If a string is longer than the set value, it will be truncated, and the exceeding characters will not be loaded.
Name of the custom property. You can add additional properties by clicking the .
Value of the property.