Model Amazon S3 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 Cloud, AAS facilitates immediate cloud-to-cloud access to Amazon S3 data for applications. This article outlines the process of connecting to Amazon S3 via Connect Cloud and importing Amazon S3 data into Visual Studio using an AAS extension.

CData Connect Cloud offers a seamless cloud-to-cloud interface tailored for Amazon S3, enabling you to create live models of Amazon S3 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 Cloud is equipped with optimized data processing capabilities right from the start, directing all supported SQL operations, including filters and JOINs, directly to Amazon S3. This leverages server-side processing for swift retrieval of the requested Amazon S3 data.

Configure Amazon S3 Connectivity for AAS

Connectivity to Amazon S3 from Azure Analysis Services is made possible through CData Connect Cloud. To work with Amazon S3 data from Azure Analysis Services, we start by creating and configuring a Amazon S3 connection.

1. Log into Connect Cloud, click Connections and click Add Connection
2. Select "Amazon S3" from the Add Connection panel
3. Enter the necessary authentication properties to connect to Amazon S3.

   To authorize Amazon S3 requests, provide the credentials for an administrator account or for an IAM user with custom permissions. Set AccessKey to the access key Id. Set SecretKey to the secret access key.

   Note: You can connect as the AWS account administrator, but it is recommended to use IAM user credentials to access AWS services.

   For information on obtaining the credentials and other authentication methods, refer to the Getting Started section of the Help documentation.

4. Click Create & Test
5. Navigate to the Permissions tab in the Add Amazon S3 Connection page and update the User-based permissions.

Add a Personal Access Token

If you are connecting from a service, application, platform, or framework that lacks support for OAuth authentication, you have the option to generate a Personal Access Token (PAT) for authentication purposes. It's advisable to follow best practices by creating a distinct PAT for each service to uphold access granularity.

1. Click on your username at the top right of the Connect Cloud app and click User Profile.
2. On the User Profile page, scroll down to the Personal Access Tokens section and click Create PAT.
3. Give your 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, you are ready to connect to Amazon S3 data from Visual Studio using Azure Analysis Services.

Connect to Amazon S3 in Visual Studio Using AAS

The steps below outline connecting to CData Connect Cloud from Azure Analysis Services to create a new Amazon S3 data source. You will need the Microsoft Analysis Services Project Extension installed to continue.

1. In Visual Studio, create a new project. Select Analysis Services Tabular Project.
2. In the Configure your new project dialog, enter a name for your project in the Project name field. Fill in the rest of the fields.
3. Click 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). Click Test Connection and sign in to your server.
4. Click OK to create the project. Your Visual Studio window should resemble the following screenshot:
5. In Visual Studio, in Tabular model Explorer, right-click Data Sources and select Import From Data Source.
6. In the Table Import Wizard, select SQL Server database and click Connect. In the Server field, enter the Virtual SQL Server endpoint and port separated by a comma: e.g., tds.cdata.com,14333
7. Click SQL Server Authentication and enter the following information:
   • User name: Enter your CData Connect Cloud username. This is displayed in the top-right corner of the CData Connect Cloud interface. For example, test@cdata.com.
   • Password: Enter the PAT you generated on the Settings page.
   Click Test Connection. If successful, choose from the Database name list and click Next.
8. On the next screen, select Current User and click Next.
9. Now, select the first option and click Next.
10. On the next screen, select a table from the list and click Preview & Filter.
11. You should now see the table from Amazon S3 populated with data.

Now that you have imported the Amazon S3 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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