Create a connection in Anaplan Data Orchestrator to import data from Snowflake. Then use the connection to extract data and create a source dataset.

Prerequisites

See the sections below for the requirements needed before you create a Snowflake connection.

Create a connection with Snowflake

Use the Snowflake connector in Data Orchestrator to create a connection.

You need your Snowflake credentials to connect the Snowflake data with Data Orchestrator. View the Snowflake documentation for more information about your credentials.

To create a connection:

  1. Select Data Orchestrator from the top-left navigation menu.
  2. Choose a dataspace from the list.
  3. Select Connections from the left-side panel.
  4. Select Create connection.
  5. Select the Snowflake connector and then select Next.
    If you can't find the connector, enter a search term in the Find... field.
  6. Enter these details on the Connection details screen, and then select Next:
    • Name: Create a name for your connection. The name can contain alphanumeric characters and underscores.
    • Description: Enter a description about your connection.
  7. Enter your Snowflake credentials on the Connection credentials screen, and then select Next.
    For information about the fields on the Connection credentials screen, see Authentication options for Snowflake connections.
  8. After the connection test is complete, select Done.

Extract data from a Snowflake connection

You can extract data from the Snowflake connection to add source data to Data Orchestrator. The data extract creates a source dataset.

To extract data:

  1. Select Data Orchestrator from the top-left navigation menu.
  2. Choose a dataspace from the list.
  3. Select Source data from the left-side panel.
  4. Select Add data > From connection.
  5. On the Dataset details screen, enter these details and select Next:
    • Connection: Select the Snowflake connection you want to base the dataset on. 
    • Dataset name: Create a name for the dataset.
    • Description: Enter a description about the dataset.
    • Namespace: Select a namespace. This is the Snowflake schema for the specific database defined for the connection. 
    • Path name: Select a path name. This is the Snowflake table name. 
  6. Select the Load type and columns to import on the Choose an upload type screen, and then select Next.
    See the Load types table below for more information.
  7. Select Create in the confirmation dialog.

Load types

This table provides more information about the load types you can select for the data extract.

Load typeDescription
Full replaceThe dataset is overwritten with the content of the source.
Append

The content of the source is added to the dataset. The current content of the dataset remains unchanged.


A selected column can optionally be configured as a Cursor Field. Only rows that have a value greater than the maximum value of the cursor field from the previous sync are included. Typically a cursor field would be a last-updated timestamp or an auto-incrementing integer column.



Note: If the cursor field isn't unique, such as a date rather than a timestamp:

  • The connector checks if there are any rows with the same value as the previous maximum to ensure no additions are missed.
  • We suggest you use the Incremental load type with a primary key, rather than the Append load type.
Incremental

The content of the source updates the dataset based on the selected Primary Key and Cursor Field. 


If the source has rows that match the rows in the dataset, the dataset rows are updated with matching the data in the source. Data in the source without an existing match are added to the dataset. Existing rows in the dataset that have no match in the source are unchanged.


Only rows in the source that have a value greater than the maximum value of the cursor field from the previous sync are included. Typically a cursor field would be a last-updated timestamp or an auto-incrementing integer column.


Note that if the source contains multiple rows with the same primary key values, the duplicates are ignored. This situation should be avoided to ensure the most recent values are used to update the source dataset.

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