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The Google Sheets connector uses the Connect and Configure parameters to create a table of Google Sheets data, which is then stored in your preferred storage location (Snowflake, Databricks, Amazon Redshift, or cloud storage). You do not need to use the Create Table component when using this connector, as the Google Sheets component will create a new table or replace an existing table for you using the Destination parameters you define. If the component requires access to a cloud provider (AWS, Azure, or GCP), it will use the cloud credentials associated with your environment to access resources. To stage data to Azure Blob Storage, the Azure credentials associated with your environment must be assigned the Storage Blob Data Contributor role. For more information, read User assigned with the Storage Blob Data Contributor role.
This component supersedes the Google Sheets Query component, which is no longer available for new pipelines.Existing pipelines with the Google Sheets Query component will continue to work as expected.

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Properties

Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.
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Name
string
required
A human-readable name for the component.

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Connect

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Authentication Type
drop-down
required
Select OAuth 2.0 Authorization Code from the drop-down menu.
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Authentication
drop-down
required
Choose your OAuth connection from the drop-down menu.Click Manage to navigate to the OAuth connections list to review OAuth connections and to add new connections. Read OAuth to learn how to create an OAuth connection.To set up a new OAuth:
  1. Click Manage under the drop-down menu.
  2. Click Add OAuth connection in the OAuth tab to open Add new OAuth.
  3. Provide an appropriate OAuth name.
  4. Choose Google in the Provider field.
  5. Select OAuth 2.0 Authorization Code Grant from the Authentication type drop-down.
  6. Click Sign in with Google.
  7. Choose an account.
  8. Click Allow to authorize access to your chosen Google account. You will return to the OAuth tab, where your new OAuth connection will have been created.
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Connection Options
column editor
  • Parameter: A JDBC parameter supported by the database driver. The available parameters are explained in data model. Manual setup is not usually required, since sensible defaults are assumed.
  • Value: A value for the given parameter.
Click the Text Mode toggle at the bottom of the Connection Options dialog to open a multi-line editor that lets you add items in a single block. For more information, read Text mode.To use grid variables, select the Use Grid Variable checkbox at the bottom of the Connection Options dialog.

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Configure

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Spreadsheet Name
string
required
Enter the name of the spreadsheet. This must be accessible to the account used to authenticate (but that account doesn’t need to own the spreadsheet).
The spreadsheet ID is the long string of characters in the middle of the spreadsheet URL. For example, if the spreadsheet URL is docs.google.com/spreadsheets/d/1aBCD2efg3HiJk4lMNopQ5rs6Tu7-vwxyZ8/edit then the spreadsheet ID is 1aBCD2efg3HiJk4lMNopQ5rs6Tu7-vwxyZ8.
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Contains Header Row
boolean
required
  • Yes: The first row of data (row 1) is used to derive column names. Spaces and special characters are removed. This is the default setting.
  • No: Data is returned using columns A, B, C, and so on. All rows are treated as data.
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Mode
drop-down
required
  • Basic: Configure data selection using the Data Source, Data Selection, Data Source Filter, Combine Filters, and Row Limit properties.
  • Advanced: Write your own SQL query using the SQL Query property.
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Cell Range
string
Optionally specify a cell range. For example, A1:C500. If not specified, the entire sheet is read. If specified, then you must set Contains Header Row to No. The specified cell range should only include data and not column headings.The wildcard character * is supported, for example A5:E* would consider columns A-E and rows 5 onwards.
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SQL Query
code editor
Write an SQL query to retrieve your data when Mode is set to Advanced.
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Data Source
drop-down
required
Select the data source to query from. Available when Mode is set to Basic.
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Data Selection
dual listbox
required
Select the columns to include from the data source. Available when Mode is set to Basic.
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Data Source Filter
grid
Filter the rows returned from the data source. Available when Mode is set to Basic.
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Combine Filters
drop-down
required
Choose how to combine multiple filters. Select AND to return rows matching all filters, or OR to return rows matching any filter. Available when Mode is set to Basic.
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Row Limit
integer
The maximum number of rows to return from the data source. Leave blank to return all rows. Available when Mode is set to Basic.

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Destination

Select your cloud data warehouse.
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Destination
drop-down
required
Select the destination for your data. This is either in Snowflake as a table or as files in cloud storage.
  • Snowflake: Load your data into a table in Snowflake. The data must first be staged via Snowflake or a cloud storage solution.
  • Cloud Storage: Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension so we suggest inspecting the output to determine the format of the data.
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Warehouse
drop-down
required
The Snowflake warehouse used to run the queries. The special value [Environment Default] uses the warehouse defined in the environment. Read Overview of Warehouses to learn more.
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Database
drop-down
required
The Snowflake database to access. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
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Schema
drop-down
required
The Snowflake schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
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Table Name
string
required
The name of the table to be created in your Snowflake database. You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.
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Load Strategy
drop-down
required
Define what happens if the table name already exists in the specified Snowflake database and schema.
  • Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
  • Truncate and Insert: If the specified table name already exists, all rows within the table will be removed and new rows will be inserted per the next run of this pipeline.
  • Fail if Exists: If the specified table name already exists, this pipeline will fail to run.
  • Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It’s appended onto the end of the existing data in the table. If the specified table name doesn’t exist, then the table will be created, and your data will be inserted into the table.
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Primary Keys
dual listbox
Select one or more columns to be designated as the table’s primary key.
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Clean Staged files
boolean
required
  • Yes: Staged files will be destroyed after data is loaded. This is the default setting.
  • No: Staged files are retained in the staging area after data is loaded.
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Stage Access Strategy
drop-down
Select the stage access strategy. The strategies available depend on the cloud platform you select in Stage Platform.
  • Credentials: Connects to the external stage (AWS, Azure) using your configured cloud provider credentials. Not available for Google Cloud Storage.
  • Storage Integration: Use a Snowflake storage integration to grant access to Snowflake to read data from and write to a cloud storage location. This will reveal the Storage Integration property, through which you can select any of your existing Snowflake storage integrations.
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Stage Platform
drop-down
required
Use the drop-down menu to choose where the data is staged before being loaded into your Snowflake table.
  • Amazon S3: Stage your data on an AWS S3 bucket.
  • Snowflake: Stage your data on a Snowflake internal stage.
  • Azure Storage: Stage your data in an Azure Blob Storage container.
  • Google Cloud Storage: Stage your data in a Google Cloud Storage bucket.

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Advanced Settings

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Auto Debug
drop-down
required
Choose whether to automatically log debug information about your load. These logs can be found in the task history and should be included in support requests concerning the component. Turning this on will override any debugging connection options.
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Debug Level
drop-down
required
The level of verbosity with which your debug information is logged. Levels above 1 can log huge amounts of data and result in slower execution. These logs can be found in the Message field of the task details after pipeline execution and should be included in support requests concerning the component.
  1. Will log the query, the number of rows returned by it, the start of execution and the time taken, and any errors.
  2. Will log everything included in Level 1, plus cache queries and additional information about the request, if applicable.
  3. Will additionally log the body of the request and the response.
  4. Will additionally log transport-level communication with the data source. This includes SSL negotiation.
  5. Will additionally log communication with the data source, as well as additional details that may be helpful in troubleshooting problems. This includes interface commands.
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Parse 'Null' & Empty Strings as NULL
boolean
required
Converts common strings that represent null into a null value. This is case-sensitive and works with the following strings: "", “NULL”, “NUL”, “Null”, “null”. The default is No.
Currently, this property is only applicable when using Snowflake as your destination.
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Trim String Columns
boolean
required
When Yes, remove leading and trailing characters from a string column. The default is No.

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Data model

The JDBC driver for this component models Google Sheets APIs as relational tables, views, and stored procedures, which are documented in the data model. You’ll also find API limitations and requirements. This connector also allows you to query system tables in Advanced mode. To see the available system tables in the data model, read the System Tables section of the data model. For more information about using system tables, read our System tables guide.

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Deactivate soft delete for Azure blobs (Databricks)

If you intend to set your destination as Databricks and your stage platform as Azure Storage, you must turn off the “Enable soft delete for blobs” setting in your Azure account for your pipeline to run successfully. To do this:
  1. In the Azure portal, navigate to your storage account.
  2. In the menu, under Data management, click Data protection.
  3. Clear the Enable soft delete for blobs checkbox. For more information, read Soft delete for blobs.