> ## Documentation Index
> Fetch the complete documentation index at: https://docs.skypoint.ai/llms.txt
> Use this file to discover all available pages before exploring further.
#
# Connecting to CMS SNF
This guide provides a step-by-step approach to effortlessly connecting CMS SNF with skyData.
The CMS SNF Connector integrates CMS Skilled Nursing Facility data into skyData’s Lakehouse, ensuring compliance, efficiency, and real-time insights by ingesting data via API calls, storing it in azure blob storage, and processing it through Databricks into Delta Lake via Unity Catalog. With Databricks as a fully managed warehouse, SNFs gain real-time insights, personalized reports, and proactive alerts to optimize patient care.
## To import data using the CMS SNF connector
Follow these steps to create and configure a new dataflow for the CMS SNF import connector:
1. Navigate to **Dataflow > Imports**.
2. Click **New dataflow** as indicated by an arrow.
The **Set dataflow name** page appears.
3. Enter the desired name for the dataflow in the **Name** text field.
4. Click **Next** to proceed.
The **Choose connector** page appears.
## Add CMS SNF connector
1. On the **Choose Connector** page, use the **Search** feature to locate and select the **CMS SNF** Connector.
2. Enter the **Display Name** for your dataflow in the provided text field.
3. Optionally, add a **Description** in the designated text area.
4. Click **Next** to proceed.
The **Configuration** page appears.
## Connect to the CMS SNF account
1. The credentials gets auto populated on the Configuration page.
2. Click **Connect**.
Once the connection is established, the connector can be used to import data from CMS SNF tables.
3. Scroll down to the **Table Details** section, select the checkboxes for the tables you wish to import, and use the dropdown menu to label them as either **Data** or **Metadata.**
By default, all tables in the **Table Details** section are selected. You can choose to import only specific tables that are relevant to your data processing needs. For example, to import customer data, select tables containing details like name, email, address, and contact information.
| Item | Description |
| :---------- | :-------------------------------------------------------------------------- |
| Purpose | Option to assign a purpose (Data or Metadata) for each table. |
| Data | Loads customer data |
| Metadata | Loads Metadata |
| File name | Displays the name of the file that you imported. |
| Entity name | Displays the imported table name by default. You can rename it if required. |
4. Click **Save** to apply the changes.
Congratulations ! for saving the CMS SNF connector dataflow, which appears on the **Dataflow > Imports** page.
## Run, Edit, and Delete the imported data
Once the table is imported, you can execute, modify, and remove the imported table from the Dataflow. Follow the below steps:
1. Go to the **Dataflow** > **Imports** page.
| Item | Description |
| :---------------- | :------------------------------------------------------------------------------ |
| Name | Displays the name of the imported Dataflow. |
| Type | Displays connector type symbol. |
| Connector Name | Displays connector name. |
| Status | Indicates whether the data is imported successfully. |
| Tables Count | Displays the number of tables imported. |
| Created Date | Displays date of creation. |
| Last Refresh Type | Displays the refresh value: Full or Incremental. |
| Updated Date | Displays last modified date. |
| Last Refresh | Displays the latest refresh date, which updates each time you refresh the data. |
| Actions | Provides multiple options for managing dataflows. |
2. Select the horizontal ellipsis under the **Actions** column and do the following:
| If you want to | Then |
| :----------------------------------- | :------------------------------------------------------------------------------------------------- |
| Modify the Dataflow | Select **Edit** and modify the Dataflow. Click **Save** to apply your changes. |
| Execute the Dataflow | Select **Run**. |
| Bring the data to its previous state | Select **Rollback**. |
| Delete the Dataflow | Select **Remove** and then click the **Delete** button. All tables in the data source get deleted. |
| See the run history of the dataflow | Select **Run history**. |
3. Click **Run** to execute the dataflow. Once the execution is successful, the data pipeline status will update to **Completed**, as illustrated in the figure below.
In the Dataflow's **Run History Description,** you can view error messages related to data import failures from a data source. Additionally, you can check the status, start time, and end time of the data pipeline execution.
## Next step
After completing the data import, start the [Master Data Management (MDM) - Resolve](https://docs.skypointcloud.com/docs/stitch.html) process to create a single, unified view of the data. With Skypoint MDM, you can ensure that your data is accurate, consistent, and reliable, making it easier to use for business processes, analytics, and reporting.