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Anaplan

The Anaplan component uses the Anaplan API to retrieve bulk data (exports) to load into a table—this stages the data, so the table is reloaded each time. You can then use transformations to enrich and manage the data in permanent tables.

Warning

This component is potentially destructive. Depending on the setting of the property Create Table Mode, the target table might be recreated or truncated. Setting the property to Fail if Exists will prevent both recreation and truncation. Do not modify the target table structure manually.


Properties

Name = string

A human-readable name for the component.


Authentication Method = drop-down

Currently, only Anaplan Username & Password credentials are supported.


Username = string

Your Anaplan login username.


Password = string

Your Anaplan password. This is stored as a secret definition. Read Secret definitions to learn how to store a password as a secret definition.

Note

Login passwords have a 90-day lifespan in Anaplan, and must be reset manually.


Workspace = drop-down

An Anaplan workspace. You can find your Workspace ID in the URL of a given model. See the portion of the URL that reads /workspaces/[workspace-id]/ where [workspace-id] is an alphanumeric Workspace ID.


Model = drop-down

An Anaplan model. You can find your Model ID in the URL of a given model. See the portion of the URL that reads /models/[models-id]/ where [model-id] is an alphanumeric Model ID.


Data Source Type = drop-down

Currently only supports Anaplan export.


Export = drop-down

Select an available export. Anaplan offers documentation on creating export actions here.

Note

It's recommended that you format your exports as "Tabular Single or Multi Column CSV" to allow exports to be stored in a cloud data warehouse as a standard table.


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 exists: 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.

  • 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.

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.

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 Blob 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 (Database) = drop-down

The Databricks schema. The special value, [Environment Default], will use the schema defined in the environment. Read Create and manage schemas to learn more.


Table Name = string

The name of the table to be created.


Load Strategy = drop-down

  • Replace if exists: 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.

  • 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.

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 Blob 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

  • Amazon 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 exists: 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.

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 Blob 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.


Snowflake Databricks Amazon Redshift (preview)