Migrating data from SAS Data Sets to Snowflake using CData SSIS Components.

Snowflake is a leading cloud data warehouse and a popular backbone for enterprise BI, analytics, data management, and governance initiatives. Snowflake offers features such as data sharing, real-time data processing, and secure data storage which makes it a common choice for cloud data consolidation.

The CData SSIS Components enhance SQL Server Integration Services by enabling users to easily import and export data from various sources and destinations.

In this article, we explore the data type mapping considerations when exporting to Snowflake and walk through how to migrate SAS Data Sets data to Snowflake using the CData SSIS Components for SAS Data Sets and Snowflake.

Data Type Mapping

Special Considerations

• Casing: Snowflake enforces an exact case match by default for identifiers, so it is common to run into issues that can be attributed to mismatched casing. Set the IgnoreCase property to True in your CData SSIS Components for Snowflake connection to resolve these issues. This property directly maps to the QUOTED_IDENTIFIERS_IGNORE_CASE property in Snowflake and specifies whether Snowflake will treat identifiers as case-sensitive.

• Timestamps: Snowflake supports three timestamp types:

  • TIMESTAMP_NTZ: This timestamp stores UTC time with a specified precision. However, all operations are performed in the current session's time zone, controlled by the TIMEZONE session parameter.
  • TIMESTAMP_LTZ: This timestamp stores "wallclock" time with a specified precision. All operations are performed without taking any time zone into account.
  • TIMESTAMP_TZ: This timestamp stores UTC time together with an associated time zone offset. When a time zone isn't provided, the session time zone offset is used.

  By default the CData SSIS Components write timestamps to Snowflake as TIMESTAMP_NTZ unless manually configured.

Prerequisites

• Visual Studio 2022
• SQL Server Integration Services Projects extension for Visual Studio 2022
• CData SSIS Components for Snowflake
• CData SSIS Components for SAS Data Sets

Create the project and add components

1. Open Visual Studio and create a new Integration Services Project. 👁 Create the SSIS project
   
2. Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
3. Add a CData SAS Data Sets Source control and a CData Snowflake Destination control to the data flow task. 👁 Add the source and destination controls (Salesforce is shown)
   

Configure the SAS Data Sets source

Follow the steps below to specify properties required to connect to SAS Data Sets.

1. Double-click the CData SAS Data Sets Source to open the source component editor and add a new connection. 👁 Open the source component editor (Salesforce is shown)
   
2. In the CData SAS Data Sets Connection Manager, configure the connection properties, then test and save the connection.

   Set the following connection properties to connect to your SAS DataSet files:

   Connecting to Local Files

   • Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
   • Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

   Connecting to Cloud-Hosted SAS DataSet Files

   While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

   Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

   👁 Configure the source connection (Salesforce is shown)
   
3. After saving the connection, select "Table or view" and select the table or view to export into Snowflake, then close the CData SAS Data Sets Source Editor. 👁 Select the table to export (Salesforce is shown)
   

Configure the Snowflake destination

With the SAS Data Sets Source configured, we can configure the Snowflake connection and map the columns.

1. Double-click the CData Snowflake Destination to open the destination component editor and add a new connection. 👁 Open the destination component editor
   
2. In the CData Snowflake Connection Manager, configure the connection properties, then test and save the connection.
   • The component supports Snowflake user authentication, federated authentication, and SSL client authentication. To authenticate, set User and Password, and select the authentication method in the AuthScheme property. Starting with accounts created using Snowflake’s bundle 2024_08 (October 2024), password-based authentication is no longer supported due to security concerns. Instead, use alternative authentication methods such as OAuth or Private Key authentication.

   Other helpful connection properties

   • QueryPassthrough: When this is set to True, queries are passed through directly to Snowflake.
   • ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
   • IgnoreCase: A session parameter that specifies whether Snowflake will treat identifiers as case sensitive. Default: false(case is sensitive).
   • BindingType: There are two kinds of binding types: DEFAULT and TEXT. DEFAULT uses the binding type DATE for the Date type, TIME for the Time type, and TIMESTAMP_* for the Timestamp_* type. TEST uses the binding type TEXT for Date, Time, and Timestamp_* types.
   👁 Configure the destination connection
   
3. After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert. 👁 Choose the destination table
   
4. On the Column Mappings tab, configure the mappings from the input columns to the destination columns. 👁 Map the columns (Salesforce is shown)
   

Run the project

You can now run the project. After the SSIS Task has finished executing, data from your SQL table will be exported to the chosen table.