> ## Documentation Index > Fetch the complete documentation index at: https://docs.maia.ai/llms.txt > Use this file to discover all available pages before exploring further. # Workday export const designer = "Designer"; export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => { const allWarehouses = [...warehouses.map(w => ({ name: w, supported: true })), ...unsupportedWarehouses.map(w => ({ name: w, supported: false }))]; return

Project Availability	{allWarehouses.map((w, i) => {w.name} {w.supported ? '✅' : '❌'} )}
Component Type	{componentType}
Connection Inputs	{connectionInputs}
Connection Outputs	{connectionOutputs}

; }; For Snowflake and Google BigQuery projects, this component is superseded by the [Workday Load](/docs/components/workday-load) component, which offers both full and incremental loading. Existing pipelines in Snowflake projects using the Workday component will continue to work as expected, but new pipelines in Snowflake and Google BigQuery projects must use the Workday Load component instead. When opening a pipeline containing the Workday component, it may appear grayed out. This indicates that you should replace the existing component with the new Workday Load component. Databricks and Amazon Redshift projects should continue to use the Workday component. The Workday component uses the Workday API to retrieve and store data—such as employee, financial, and business-related data—from Workday, to be either referenced by an external table or loaded into a table, depending on your cloud data warehouse. You can then use transformation components to enrich and manage the data in permanent tables. You do not need to set up a Create Table component before using this component. To extract data via the Workday Reporting as a Service API, use the [Workday Custom Reports](/docs/components/workday-custom-reports) component instead. Using this component may return structured data that requires flattening. For help with flattening such data, we recommend using the [Extract Nested Data](/docs/components/extract-nested-data) component. If the component requires access to a cloud provider (AWS, Azure, or Google Cloud), it will use the [cloud credentials](/docs/guides/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](https://learn.microsoft.com/en-us/answers/questions/1155139/user-assigned-with-storage-blob-data-contributor-r). *** ## Properties Reference material is provided below for the Connect, Configure, and Destination properties. A human-readable name for the component. ### Connect Your Workday host name. Read [Workday authentication guide](/docs/guides/workday-authentication-guide) to learn how to acquire this credential. {/* */} Your Workday Tenant ID. Read [Workday authentication guide](/docs/guides/workday-authentication-guide) to learn how to acquire this credential. {/* */} The authentication method to authorize access to your Workday data. Choose **OAuth 2.0 Authorization Code** to use an OAuth connection, or **Username & password** to use a username and password. {/* */} (OAuth 2.0 Authorization Code only) 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](/docs/guides/oauth) to learn how to create an OAuth connection. Additionally, read [Workday authentication guide](/docs/guides/workday-authentication-guide), which explains how to create an OAuth connection for Workday. {/* */} (Username & password only) Your Workday username. {/* */} (Username & password only) Choose the secret definition that represents your credentials for this connector. If you have not already saved your credentials for this connector as a secret definition, click **Add secret** to create a secret definition representing these credentials. Read [Secrets and secret definitions](/docs/guides/secrets-and-secret-definitions) for details about creating a secret definition. ### Configure The version of Workday Web Services directory you want to use. The default is `v41.0`, but you can replace this with any valid version. {designer} supports any Workday Web Services version listed [here](https://community.workday.com/sites/default/files/file-hosting/productionapi/versions/index.html). {/* */} Select the Workday Web Service you want to query. The drop-down will include all services available to the selected **Version**. {/* */} Select the operation you want to perform on the selected Workday Web Service. The drop-down will include all operations available to the selected **Web Service**. {/* */} Set filter settings for extracting data from Workday. * **Object Name - Id Type:** Select an object from the drop-down. * **ID:** Specify the value of the object. * **Descriptor:** Provide a description of the object. {/* */} Select columns to be extracted to load. Exclude fine-grained organization object detail from the extract and load operation. ### Destination Select your cloud data warehouse. 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. {/* */} The Snowflake warehouse used to run the queries. The special value `[Environment Default]` uses the warehouse defined in the environment. Read [Overview of Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-overview.html) to learn more. {/* */} {/* */} The Snowflake database to access. The special value `[Environment Default]` uses the database defined in the environment. Read [Databases, Tables and Views - Overview](https://docs.snowflake.com/en/guides-overview-db) to learn more. {/* */} {/* */} The Snowflake schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Database, Schema, and Share DDL](https://docs.snowflake.com/en/sql-reference/ddl-database.html) to learn more. {/* */} {/* */} The name of the table to be created in your Snowflake database. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded. {/* */} {/* */} 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:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. {/* */} {/* */} Select one or more columns to be designated as the table's primary key. {/* */} {/* */} * **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. {/* */} {/* */} 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](/docs/guides/cloud-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. {/* */} {/* */} 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. {/* */} {/* */} Select the Snowflake internal stage type. Use the Snowflake links provided to learn more about each type of stage. * **User:** Each Snowflake user has a [user stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#user-stages) allocated to them by default for file storage. You may find the user stage convenient if your files will only be accessed by a single user, but need to be copied into multiple tables. * **Named:** A [named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#named-stages) provides high flexibility for data loading. Users with the appropriate privileges on the stage can load data into any table. Furthermore, because the stage is a database object, any security or access rules that apply to all objects will apply to the named stage. Named stages can be altered and dropped. User stages cannot. {/* */} {/* */} Select your named stage. Read [Creating a named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#creating-a-named-stage) to learn how to create a new named stage. There is a known issue where named stages that include special characters or spaces are not supported. {/* */} {/* */} * **Append Files in Folder:** Appends files to storage folder. This is the default setting. * **Overwrite Files in Folder:** Overwrite existing files with matching structure. {/* */} {/* */} The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name. {/* */} {/* */} A string of characters that precedes the name of the written files. This can be useful for organizing database objects. {/* */} {/* */} A cloud storage location to load your data into files for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage. {/* */} Select the destination for your data. This is either in Databricks as a table or as files in cloud storage. * **Databricks:** Load your data into Databricks. You'll need to set a cloud storage location for temporary staging of the data. * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location. {/* */} Select a [Databricks Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html). The special value `[Environment Default]` uses the catalog defined in the environment. Selecting a catalog will determine which databases are available in the next parameter. {/* */} {/* */} The Databricks schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Create and manage schemas](https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html) to learn more. {/* */} {/* */} The name of the table to be created in your Databricks schema. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded. {/* */} {/* */} Define what happens if the table name already exists in the specified Databricks schema. * **Fail if Exists:** If the specified table name already exists, this pipeline will fail to run. * **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:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. {/* */} {/* */} * **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. {/* */} {/* */} Use the drop-down menu to choose where the data is staged before being loaded into your Databricks table. * **Amazon S3:** Stage your data on an AWS S3 bucket. * **Azure Storage:** Stage your data in an Azure Blob Storage container. {/* */} {/* */} * **Append Files in Folder:** Appends files to storage folder. This is the default setting. * **Overwrite Files in Folder:** Overwrite existing files with matching structure. {/* */} {/* */} The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name. {/* */} {/* */} A string of characters that precedes the name of the written files. This can be useful for organizing database objects. {/* */} {/* */} A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage. {/* */} Select the destination for your data. This is either in Amazon Redshift as a table or as files in cloud storage. * **Redshift:** Load your data into Amazon Redshift. You'll need to set a cloud storage location for temporary staging of the data. * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location. {/* */} Select the Amazon Redshift schema that will contain your table. The special value `[Environment Default]` uses the schema defined in the environment. For information about using multiple schemas, read [Schemas](https://docs.aws.amazon.com/redshift/latest/dg/r_Schemas_and_tables.html). {/* */} {/* */} The name of the table to be created in your Amazon Redshift database. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded. {/* */} {/* */} Define what happens if the table name already exists in the specified Amazon Redshift 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. * **Fail if Exists:** If the specified table name already exists, this pipeline will fail to run. * **Truncate and Insert:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. {/* */} {/* */} * **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. {/* */} {/* */} An AWS S3 bucket to stage data into before it is loaded into your Amazon Redshift table. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} * **Append Files in Folder:** Appends files to storage folder. This is the default setting. * **Overwrite Files in Folder:** Overwrite existing files with matching structure. {/* */} {/* */} The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name. {/* */} {/* */} A string of characters to include at the beginning of the written files. Often used for organizing database objects. {/* */} {/* */} A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage. {/* */} ## 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](https://portal.azure.com/), 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](https://learn.microsoft.com/en-gb/azure/storage/blobs/soft-delete-blob-overview).