This page describes how to configure a Gmail data source. With Data Loader, you can replicate and load your source data into your target destination.
Schema Drift Support: Yes. Read Schema Drift to learn more.
Return to any page of this wizard by clicking Previous.
Click X in the upper-right of the UI and then click Yes, discard to close the pipeline creation wizard.
- Read the Allow-listed IP Addresses topic before you begin. You may not be able to connect to certain data sources without first allow-listing the Batch IP addresses. In these circumstances, connection tests will always fail and you will not be able to complete the pipeline.
- You must have a valid Gmail account.
- In Data Loader, click Add pipeline.
- Choose Gmail from the grid of data sources.
- Choose Batch Loading.
Connect to Gmail
Configure the Gmail database connection settings, specifying the following:
|Email address||A valid Gmail address.|
|Password||A managed entry representing your Gmail login password. Choose an existing password from the dropdown menu or click Manage and then click Add new password to configure a new managed password entry. Give the password a label, which is what you can see in the password dropdown menu, and then input the value of the password. Read Manage Passwords to learn more.|
|Advanced settings||Additional JDBC parameters or connection settings. Click Advanced settings and then choose a parameter from the dropdown menu and enter a value for the parameter. Click Add parameter for each extra parameter you want to add.|
Click Test and Continue to test your settings and move forward. You can't continue if the test fails for any reason.
Choose any tables you wish to include in the pipeline. Use the arrow buttons to move tables to the Tables to extract and load listbox and then reorder any tables with click-and-drag. Additionally, select multiple tables using the
Click Continue with X tables to move forward.
Choose the columns from each table to include in the pipeline. By default, Data Loader selects all columns from a table.
Click Configure on a table to open Configure table. This dialog lists columns in a table and the data type of each column. Additionally, you can set a primary key and assign an incremental column state to a column.
- Primary Key columns should represent a true
PRIMARY KEYthat uniquely identifies each record in a table. Composite keys work, but you must specify all columns that compose the key. Based on the primary key, this won't permit duplicate records. Jobs may fail or replicate data incorrectly if these rules aren't applied.
- Make sure an Incremental column is a true change data capture (CDC) column that can identify whether there has been a change for reach record in the table. This column should be a TIMESTAMP/DATE/DATETIME type or an INTEGER type representing a date key or UNIX timestamp. :::
Click Add and remove columns to modify a table before a load. Use the arrow buttons to move columns out of the Columns to extract and load listbox. Order columns with click-and-drag. Select multiple columns using
Click Done adding and removing to continue and then click Done.
Click Continue once you have configured each table.
- Choose an existing destination or click Add a new destination.
- Select a destination from Snowflake, Amazon Redshift, or Google BigQuery.
|Pipeline name||A descriptive label for your pipeline. This is how the pipeline appears on the pipeline dashboard and how Data Loader refers to the pipeline.|
|Sync every||The frequency at which the pipeline should sync. Day values include 1—7. Hour values include 1—23. Minute values include 5—59. The input is also the length of delay before the first sync.|
Currently, you can't specify a start time.
Once you are happy with your pipeline configuration, click Create pipeline to complete the process and add the pipeline to your dashboard.