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Workday

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.

To extract data via the Workday Reporting as a Service API, use the 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 component.

If the component requires access to a cloud provider, it will use the cloud credentials associated with your environment to access resources.


Properties

Reference material is provided below for the Connect, Configure, and Destination properties.

Connect

Host = string

Your Workday host name. Read Workday authentication guide to learn how to acquire this credential.


Tenant = string

Your Workday Tenant ID. Read Workday authentication guide to learn how to acquire this credential.


Authentication Type = drop-down

Only currently supports username and password.


Username = string

Your Workday username.


Password = drop-down

Use the drop-down menu to select the corresponding secret definition that denotes your Workday password. Read Secret definitions to learn how to create a new secret definition.


Configure

Version = string

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.


Web Service = drop-down

Select the Workday Web Service you want to query. The drop-down list will include all services available to the selected Version.


Operation = drop-down

Select the operation you want to perform on the selected Workday Web Service. The drop-down list will include all operations available to the selected Web Service.


Object Filter = column editor

Set filter settings for extracting data from Workday.

  • Object Name - Id Type: Select an object from the drop-down menu.
  • ID: Specify the value of the object.
  • Descriptor: Provide a description of the object.

Data Selection = dual listbox

Select columns to be extracted to load.


Exclude from Organizations = dual listbox

Exclude fine-grained organization object detail from the extract and load operation.


Destination

Select your cloud data warehouse.

Destination = drop-down

  • Snowflake: Load your data into Snowflake. You'll need to set a cloud storage location for temporary staging of the data.
  • Cloud Storage: Load your data directly into your preferred cloud storage location.

Click either the Snowflake or Cloud Storage tab on this page for documentation applicable to that destination type.

Warehouse = drop-down

The Snowflake warehouse used to run the queries. The special value, [Environment Default], will use the warehouse defined in the environment. Read Overview of Warehouses to learn more.


Database = drop-down

The Snowflake database. The special value, [Environment Default], will use the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.


Schema = drop-down

The Snowflake schema. The special value, [Environment Default], will use the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.


Table Name = string

The name of the table to be created.


Load Strategy = drop-down

  • 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.

Clean Staged files = boolean

  • 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.

Stage Platform = drop-down

Choose a data staging platform using the drop-down menu.

  • 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.

Click one of the tabs below for documentation applicable to that staging platform.

Amazon S3 Bucket = drop-down

An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.

Internal Stage Type = drop-down

A Snowflake internal stage type. Currently, only type User is supported.

Read Choosing an Internal Stage for Local Files to learn more about internal stage types and the usage of each.

Storage Account = drop-down

Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.


Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

Storage Integration= drop-down

Select the storage integration. Storage integrations are required to permit Snowflake to read data from and write to a cloud storage location. Integrations must be set up in advance of selecting them. Storage integrations can be configured to support Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage, regardless of the cloud provider that hosts your Snowflake account.


GCS Bucket= drop-down

The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.


Overwrite = boolean

Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.

Load Strategy = drop-down

  • Append Files in Folder: Appends files to storage folder. This is the default setting.
  • Overwrite Files in Folder: Overwrite existing files with matching structure.

See the configuration table for how this parameter works with the Folder Path and File Prefix parameters:

Configuration Description
Append files in folder with defined folder path and file prefix. Files will be stored under the structure uniqueID/timestamp-partX where X is the part number, starting from 1. For example, 1da27ea6-f0fa-4d15-abdb-d4e990681839/20240229100736969-part1.
Append files in folder without defined folder path and file prefix. Files will be stored under the structure folder/prefix-timestamp-partX where X is the part number, starting from 1. For example, folder/prefix-20240229100736969-part1.
Overwrite files in folder with defined folder path and file prefix. Files will be stored under the structure folder/prefix-partX where X is the part number, starting from 1. All files with matching structures will be overwritten.
Overwrite files in folder without defined folder path and file prefix. Validation will fail. Folder path and file prefix must be supplied for this load strategy.

Folder Path = string

The folder path of the written files.


File Prefix = string

A string of characters to include at the beginning of the written files. Often used for organizing database objects.


Storage = drop-down

A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.

Click the tab that corresponds to your chosen cloud storage service.

Amazon S3 Bucket = drop-down

An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.

Storage Account = drop-down

Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.


Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

GCS Bucket= drop-down

The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.


Overwrite = boolean

Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.

Destination = drop-down

  • 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 your preferred cloud storage location.

Click either the Databricks or Cloud Storage tab on this page for documentation applicable to that destination type.

Catalog = drop-down

Select a Databricks Unity Catalog. The special value, [Environment Default], will use the catalog specified in the Data Productivity Cloud environment setup. Selecting a catalog will determine which schema are available in the next parameter.


Schema = drop-down

Select the Databricks schema. The special value, [Environment Default], will use the schema specified in the Data Productivity Cloud environment setup.


Table Name = string

The name of the table to be created.


Load Strategy = drop-down

  • 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.

Clean Staged Files = boolean

  • 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.

Stage Platform = drop-down

Choose a data staging platform using the drop-down menu.

  • Amazon S3: Stage your data on an AWS S3 bucket.
  • Azure Storage: Stage your data in an Azure Blob Storage container.

Click one of the tabs below for documentation applicable to that staging platform.

Amazon S3 Bucket = drop-down

An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.

Storage Account = drop-down

Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.


Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

Storage Integration= drop-down

Select the storage integration. Storage integrations are required to permit Snowflake to read data from and write to a cloud storage location. Integrations must be set up in advance of selecting them. Storage integrations can be configured to support Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage, regardless of the cloud provider that hosts your Snowflake account.


GCS Bucket= drop-down

The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.


Overwrite = boolean

Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.

Load Strategy = drop-down

  • Append Files in Folder: Appends files to storage folder. This is the default setting.
  • Overwrite Files in Folder: Overwrite existing files with matching structure.

See the configuration table for how this parameter works with the Folder Path and File Prefix parameters:

Configuration Description
Append files in folder with defined folder path and file prefix. Files will be stored under the structure uniqueID/timestamp-partX where X is the part number, starting from 1. For example, 1da27ea6-f0fa-4d15-abdb-d4e990681839/20240229100736969-part1.
Append files in folder without defined folder path and file prefix. Files will be stored under the structure folder/prefix-timestamp-partX where X is the part number, starting from 1. For example, folder/prefix-20240229100736969-part1.
Overwrite files in folder with defined folder path and file prefix. Files will be stored under the structure folder/prefix-partX where X is the part number, starting from 1. All files with matching structures will be overwritten.
Overwrite files in folder without defined folder path and file prefix. Validation will fail. Folder path and file prefix must be supplied for this load strategy.

Folder Path = string

The folder path of the written files.


File Prefix = string

A string of characters to include at the beginning of the written files. Often used for organizing database objects.


Storage = drop-down

A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.

Click the tab that corresponds to your chosen cloud storage service.

Amazon S3 Bucket = drop-down

An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.

Storage Account = drop-down

Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.


Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

GCS Bucket= drop-down

The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.


Overwrite = boolean

Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.

Destination = drop-down

  • 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 your preferred cloud storage location.

Click either the Amazon Redshift or Cloud Storage tab on this page for documentation applicable to that destination type.

Schema = drop-down

Select the Redshift schema. The special value, [Environment Default], will use the schema defined in the environment. For information about using multiple schemas, read Schemas.


Table Name = string

The name of the table to be created.


Load Strategy = drop-down

  • 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.

Clean Staged Files = boolean

  • 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.

Amazon S3 Bucket = drop-down

An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.

Load Strategy = drop-down

  • Append Files in Folder: Appends files to storage folder. This is the default setting.
  • Overwrite Files in Folder: Overwrite existing files with matching structure.

See the configuration table for how this parameter works with the Folder Path and File Prefix parameters:

Configuration Description
Append files in folder with defined folder path and file prefix. Files will be stored under the structure uniqueID/timestamp-partX where X is the part number, starting from 1. For example, 1da27ea6-f0fa-4d15-abdb-d4e990681839/20240229100736969-part1.
Append files in folder without defined folder path and file prefix. Files will be stored under the structure folder/prefix-timestamp-partX where X is the part number, starting from 1. For example, folder/prefix-20240229100736969-part1.
Overwrite files in folder with defined folder path and file prefix. Files will be stored under the structure folder/prefix-partX where X is the part number, starting from 1. All files with matching structures will be overwritten.
Overwrite files in folder without defined folder path and file prefix. Validation will fail. Folder path and file prefix must be supplied for this load strategy.

Folder Path = string

The folder path of the written files.


File Prefix = string

A string of characters to include at the beginning of the written files. Often used for organizing database objects.


Storage = drop-down

A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.

Click the tab that corresponds to your chosen cloud storage service.

Amazon S3 Bucket = drop-down

An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.

Storage Account = drop-down

Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.


Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

GCS Bucket= drop-down

The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.


Overwrite = boolean

Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.


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. Log in to the Azure portal.
  2. In the top-left, click ☰ → Storage Accounts.
  3. Select the intended storage account.
  4. In the menu, under Data management, click Data protection.
  5. Untick Enable soft delete for blobs. For more information, read Soft delete for blobs.

Snowflake Databricks Amazon Redshift