Model Sage US Data Using Azure Analysis Services

Microsoft Azure Analysis Services (AAS) is a fully-managed platform-as-a-service (PaaS) offering that delivers enterprise-grade data models in the cloud. When combined with CData Connect AI, AAS facilitates immediate cloud-to-cloud access to Sage US data for applications. This article outlines the process of connecting to Sage US via Connect AI and importing Sage US data into Visual Studio using an AAS extension.

CData Connect AI offers a seamless cloud-to-cloud interface tailored for Sage US, enabling you to create live models of Sage US data in Azure Analysis Services without the need to replicate data to a natively supported database. While constructing high-quality semantic data models for business reports and client applications, Azure Analysis Services formulates SQL queries to retrieve data. CData Connect AI is equipped with optimized data processing capabilities right from the start, directing all supported SQL operations, including filters and JOINs, directly to Sage US. This leverages server-side processing for swift retrieval of the requested Sage US data.

Prerequisites

Before you connect, you must first do the following:

• Connect a data source to your CData Connect AI account. Detailed steps are provided in the next section.
• Generate a Personal Access Token (PAT). Copy this down, as it acts as your password during authentication.
• Create a server in Azure Analysis Services to which you will deploy your data from CData Connect AI.
• Install and configure an On-Premise Gateway in your system. This will pull data from the source via CData Connect AI into the Azure Analysis Services project and deploy models to the server. Refer to the given link to find the detailed process.

Configure Sage US Connectivity for AAS

Connectivity to Sage US from Azure Analysis Services is made possible through CData Connect AI. To work with Sage US data from Azure Analysis Services, we start by creating and configuring a Sage US connection.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "Sage US" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Sage US.

   The Application Id and Company Name connection string options are required to connect to Sage as a data source. You can obtain an Application Id by contacting Sage directly to request access to the Sage 50 SDK.

   Sage must be installed on the machine. The Sage.Peachtree.API.dll and Sage.Peachtree.API.Resolver.dll assemblies are required. These assemblies are installed with Sage in C:/Program Files/Sage/Peachtree/API/. Additionally, the Sage SDK requires .NET Framework 4.0 and is only compatible with 32-bit applications. To use the Sage SDK in Visual Studio, set the Platform Target property to "x86" in Project -> Properties -> Build.

   You must authorize the application to access company data: To authorize your application to access Sage, restart the Sage application, open the company you want to access, and connect with your application. You will then be prompted to set access permissions for the application in the resulting dialog.

   While the compiled executable will require authorization only once, during development you may need to follow this process to reauthorize a new build. To avoid restarting the Sage application when developing with Visual Studio, click Build -> Configuration Manager and uncheck "Build" for your project.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Sage US Connection page and update the User-based permissions.

Add a Personal Access Token

When connecting to Connect AI through the REST API, the OData API, or the Virtual SQL Server, a Personal Access Token (PAT) is used to authenticate the connection to Connect AI. It is best practice to create a separate PAT for each service to maintain granularity of access.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, you are ready to connect to Sage US data from Visual Studio using Azure Analysis Services.

Connect to Sage US in Visual Studio Using AAS

The steps below outline connecting to CData Connect AI from Azure Analysis Services to create a new Sage US data source. You will need the Microsoft Analysis Services Project extension installed in Microsoft Visual Studio to continue.

1. In Visual Studio, create a new project. Select Analysis Services Tabular Project. Click on Next.

2. In the Configure your new project dialog box, enter a name for your project in the Project name field. Fill in the rest of the fields.

3. Click on Create. The Tabular model designer dialog box opens. Select Workspace server and enter the address of your Azure Analysis Services server (for example, asazure://eastus.azure.windows.net/myAzureServer). Also, make sure to select the option SQL Server 2022 / Azure Analysis Services (1600) from the Compatibility level dropdown. Click on Test Connection to check if the connection details are correct. Click OK and sign in to your server.

4. Now, click on OK to create the project. Your Visual Studio window should resemble the following screenshot:

5. In the Tabular Model Explorer window of Visual Studio, right-click Data Sources and select Import From Data Source.

6. In the Get Data window, select SQL Server database and click Connect. In the Server field, enter the Virtual SQL Server endpoint and the port separated by a comma: e.g., “tds.cdata.com, 14333”, and click on OK.

7. Click on Database and enter the following information:
   • User name: Enter your CData Connect AI username. This is displayed in the top-right corner of the CData Connect AI interface. For example, [email protected].
   • Password: Enter the PAT you generated on the Settings page.

   Click on Connect. If successful, the Navigator window will pop up.

   
8. In the Navigator window, search and select the tables of your choice
9. You should now see the Salesforce table populated with data in the preview section on the right panel.
10. Click on Load to import the data.

Now that you have imported the Sage US data into your data model, you are ready to deploy the project to Azure Analysis Services for use in business reports, client applications, and more.

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