Use Azure AI Foundry to Talk to Your Amazon Athena Data via CData Connect AI

Anusha M B
Anusha M B
Technical Marketing Engineer
Leverage the CData Connect AI Remote MCP Server to enable Azure AI Foundry agents to securely answer questions and take actions on your Amazon Athena data for you.

Azure AI Foundry is Microsoft's comprehensive platform for building, deploying, and managing AI applications and agents. It provides a unified environment for creating intelligent agents that can automate tasks, answer questions, and assist with various business processes. When combined with CData Connect AI Remote MCP, you can leverage Azure AI Foundry to interact with your Amazon Athena data in real-time. This article outlines the process of connecting to Amazon Athena using Connect AI Remote MCP and creating an agent in Azure AI Foundry to interact with your Amazon Athena data.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Amazon Athena data. The CData Connect AI Remote MCP Server enables secure communication between Azure AI Foundry and Amazon Athena. This allows you to ask questions and take actions on your Amazon Athena data using Azure AI Foundry agents, all without the need for data replication to a natively supported database. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Amazon Athena. This leverages server-side processing to swiftly deliver the requested Amazon Athena data.

In this article, we show how to build an agent in Azure AI Foundry to conversationally explore (or Vibe Query) your data. The connectivity principles apply to any Azure AI Foundry agent. With Connect AI you can build AI agents with access to live Amazon Athena data, plus hundreds of other sources.

About Amazon Athena Data Integration

CData provides the easiest way to access and integrate live data from Amazon Athena. Customers use CData connectivity to:

  • Authenticate securely using a variety of methods, including IAM credentials, access keys, and Instance Profiles, catering to diverse security needs and simplifying the authentication process.
  • Streamline their setup and quickly resolve issue with detailed error messaging.
  • Enhance performance and minimize strain on client resources with server-side query execution.

Users frequently integrate Athena with analytics tools like Tableau, Power BI, and Excel for in-depth analytics from their preferred tools.

To learn more about unique Amazon Athena use cases with CData, check out our blog post: https://www.cdata.com/blog/amazon-athena-use-cases.


Getting Started


Step 1: Create an Azure AI Foundry Resource

Before connecting to Amazon Athena data, you'll need to create an Azure AI Foundry resource in your Azure portal.

  1. Log into the Azure Portal.
  2. Click Create a resource and search for Microsoft Foundry.
  3. Click Create to begin the resource creation wizard.
  4. In the Basics tab:
    • Select or create a Resource group
    • Enter a Name for your Foundry resource
    • Enter a Project name
    • Click Next
  5. Configure the Storage, Network, Identity, Encryption, and Tags tabs according to your organization's requirements, clicking Next after each section.
  6. On the Review + submit tab, review your settings and click Create.
  7. Once the resource is created, click Go to resource.
  8. Click Go to Foundry portal to access the Azure AI Foundry portal.

Step 2: Configure Amazon Athena Connectivity for Azure AI Foundry

Connectivity to Amazon Athena from Azure AI Foundry is made possible through CData Connect AI Remote MCP. To interact with Amazon Athena data from Azure AI Foundry, we start by creating and configuring a Amazon Athena connection in CData Connect AI.

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

    Authenticating to Amazon Athena

    To authorize Amazon Athena 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: Though you can connect as the AWS account administrator, it is recommended to use IAM user credentials to access AWS services.

    Obtaining the Access Key

    To obtain the credentials for an IAM user, follow the steps below:

    1. Sign into the IAM console.
    2. In the navigation pane, select Users.
    3. To create or manage the access keys for a user, select the user and then select the Security Credentials tab.

    To obtain the credentials for your AWS root account, follow the steps below:

    1. Sign into the AWS Management console with the credentials for your root account.
    2. Select your account name or number and select My Security Credentials in the menu that is displayed.
    3. Click Continue to Security Credentials and expand the Access Keys section to manage or create root account access keys.

    Authenticating from an EC2 Instance

    If you are using the CData Data Provider for Amazon Athena 2018 from an EC2 Instance and have an IAM Role assigned to the instance, you can use the IAM Role to authenticate. To do so, set UseEC2Roles to true and leave AccessKey and SecretKey empty. The CData Data Provider for Amazon Athena 2018 will automatically obtain your IAM Role credentials and authenticate with them.

    Authenticating as an AWS Role

    In many situations it may be preferable to use an IAM role for authentication instead of the direct security credentials of an AWS root user. An AWS role may be used instead by specifying the RoleARN. This will cause the CData Data Provider for Amazon Athena 2018 to attempt to retrieve credentials for the specified role. If you are connecting to AWS (instead of already being connected such as on an EC2 instance), you must additionally specify the AccessKey and SecretKey of an IAM user to assume the role for. Roles may not be used when specifying the AccessKey and SecretKey of an AWS root user.

    Authenticating with MFA

    For users and roles that require Multi-factor Authentication, specify the MFASerialNumber and MFAToken connection properties. This will cause the CData Data Provider for Amazon Athena 2018 to submit the MFA credentials in a request to retrieve temporary authentication credentials. Note that the duration of the temporary credentials may be controlled via the TemporaryTokenDuration (default 3600 seconds).

    Connecting to Amazon Athena

    In addition to the AccessKey and SecretKey properties, specify Database, S3StagingDirectory and Region. Set Region to the region where your Amazon Athena data is hosted. Set S3StagingDirectory to a folder in S3 where you would like to store the results of queries.

    If Database is not set in the connection, the data provider connects to the default database set in Amazon Athena.

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

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from Azure AI Foundry. 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, we are ready to connect to Amazon Athena data from Azure AI Foundry.

Step 3: Create an AI Agent in Azure AI Foundry

Follow these steps to create an AI agent and connect it to CData Connect AI:

  1. In the Azure AI Foundry portal, click New Foundry to create a new project.

  2. Click Start building and then select Create agent.

  3. Enter a Name for your agent.

  4. In the Setup section:

    • Choose your preferred AI model
    • Configure Instructions for how the agent should behave

Step 4: Add the CData Connect AI MCP Tool

Now you'll add the CData Connect AI MCP Server as a custom tool for your agent:

  1. In the agent setup, navigate to the Tools section and click Add.

  2. Select Custom from the tool options.

  3. Choose Model Context Protocol and click Create.

  4. Enter a Name for the MCP tool (such as "CData Connect AI MCP Server").

  5. In the Remote MCP Server endpoint field, enter: https://mcp.cloud.cdata.com/mcp/

  6. For Authentication, select Key-based.

  7. Configure the credential using:

    • Header name: Authorization
    • Value: Basic EMAIL:PAT, replacing EMAIL with your Connect AI email address and PAT with the personal access token you created earlier
    For example: Basic [email protected]:Uu90pt5vEO...

  8. Click Connect to establish the connection to CData Connect AI.

Optional: Provide Agent Context

You can enhance your agent's understanding by providing specific instructions about using the MCP Server tools. In the agent's Instructions section, you can add guidance such as: 

You are an expert at using the MCP Client tool connected to the CData Connect AI MCP Server. Always search thoroughly and use the most relevant MCP Client tool for each query. Below are the available tools and a description of each:

queryData: Execute SQL queries against connected data sources and retrieve results. When you use the queryData tool, ensure you use the following format for the table name: catalog.schema.tableName
getCatalogs: Retrieve a list of available connections from CData Connect AI. The connection names should be used as catalog names in other tools and in any queries to CData Connect AI. Use the `getSchemas` tool to get a list of available schemas for a specific catalog.
getSchemas: Retrieve a list of available database schemas from CData Connect AI for a specific catalog. Use the `getTables` tool to get a list of available tables for a specific catalog and schema.
getTables: Retrieve a list of available database tables from CData Connect AI for a specific catalog and schema. Use the `getColumns` tool to get a list of available columns for a specific table.
getColumns: Retrieve a list of available database columns from CData Connect AI for a specific catalog, schema, and table.
getProcedures: Retrieve a list of stored procedures from CData Connect AI for a specific catalog and schema
getProcedureParameters: Retrieve a list of stored procedure parameters from CData Connect AI for a specific catalog, schema, and procedure.
executeProcedure: Execute stored procedures with parameters against connected data sources

Step 5: Chat with Your Amazon Athena Data

With your agent configured and connected to CData Connect AI, you can now interact with your Amazon Athena data using natural language:

  1. In the Azure AI Foundry portal, navigate to the Chat with data section of your agent.

  2. Start asking questions about your Amazon Athena data. For example:

    • "Show me all customers from the last 30 days"
    • "What are my top performing products?"
    • "Analyze sales trends for Q4"
    • "List all active projects with their current status"

  3. The agent will use the CData Connect AI MCP Server to query your Amazon Athena data in real-time and provide responses based on live data.

Step 6: Publish Your Agent

Once you're satisfied with your agent's configuration and testing, click Publish to make your agent available for use in your organization.

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