Use Microsoft Copilot Studio to talk to your Azure Data Lake Storage Data via CData Connect AI

Microsoft Copilot Studio is a no-code/low-code platform for creating AI Agents that can automate tasks, answer questions, and assist with various business processes. When combined with CData Connect AI Remote MCP, you can leverage Copilot Studio to interact with your Azure Data Lake Storage data in real-time. This article outlines the process of connecting to Azure Data Lake Storage using Connect AI Remote MCP and creating a connection in Copilot Studio to interact with your Azure Data Lake Storage data.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Azure Data Lake Storage data. The CData Connect AI Remote MCP Server enables secure communication between Microsoft Copilot Studio and Azure Data Lake Storage. This allows you to ask questions and take actions on your Azure Data Lake Storage data using Microsoft Copilot Studio, 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 Azure Data Lake Storage. This leverages server-side processing to swiftly deliver the requested Azure Data Lake Storage data.

In this article, we show how to build a agent in Microsoft Copilot Studio to conversational explore (or Vibe Query) your data. The connectivity principals apply to any Copilot agent. With Connect AI you can build workflows and agents with access to live Azure Data Lake Storage data, plus hundreds of other sources.

Step 1: Configure Azure Data Lake Storage Connectivity for Microsoft Copilot Studio

Connectivity to Azure Data Lake Storage from Microsoft Copilot Studio is made possible through CData Connect AI Remote MCP. To interact with Azure Data Lake Storage data from Microsoft Copilot Studio, we start by creating and configuring a Azure Data Lake Storage connection in CData Connect AI.

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

   Authenticating to a Gen 1 DataLakeStore Account

   Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

   For this, an Active Directory web application is required. You can create one as follows:

   1. Sign in to your Azure Account through the .
   2. Select "Entra ID" (formerly Azure AD).
   3. Select "App registrations".
   4. Select "New application registration".
   5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
   6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
   7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.

   To authenticate against a Gen 1 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen1.
   • Account: Set this to the name of the account.
   • OAuthClientId: Set this to the application Id of the app you created.
   • OAuthClientSecret: Set this to the key generated for the app you created.
   • TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.

   Authenticating to a Gen 2 DataLakeStore Account

   To authenticate against a Gen 2 DataLakeStore account, the following properties are required:

   • Schema: Set this to ADLSGen2.
   • Account: Set this to the name of the account.
   • FileSystem: Set this to the file system which will be used for this account.
   • AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
   • Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
   Click Save & Test
4. Navigate to the Permissions tab in the Add Azure Data Lake Storage Connection page and update the User-based permissions.

With the connection configured, we are ready to connect to Azure Data Lake Storage data from Microsoft Copilot Studio.

Step 2: Connect Microsoft Copilot Studio to CData Connect AI

Follow these steps to add a CData Connect AI MCP connection in Microsoft Copilot Studio:

1. Under Tools, click Add tool, then click + New Tool.
2. In the Add Tool window, search for and click CData Connect AI.
3. In the Connect to CData Connect AI window, click Create to authenticate your connection CData Connect AI using OAuth authentication.
4. Click Add and configure to add the CData Connect AI Tool to your agent.

Optional: Give the AI Agent context

This step establishes the AI Agent's role and provides context for the conversation through the Instructions property in the Agent. By providing instructions that explicitly informs the agent about its role as an MCP Server expert and lists the available tools, you can enhance the agent's understanding and response accuracy. For example, you can set the System Message to:

You are an expert at using the MCP Client tool connected which is 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 3: Explore Live Azure Data Lake Storage Data with Microsoft Copilot Studio

With the Agent created in Microsoft Copilot Studio and the MCP tool connected, you can now interact with your Azure Data Lake Storage data using Microsoft Copilot Studio. The MCP tool allows you to send queries and receive responses from the Azure Data Lake Storage data source in real-time.

Open the chat window in your Microsoft Copilot Studio Agent to begin interacting with your Azure Data Lake Storage data. You can ask questions, retrieve data, and perform actions on your Azure Data Lake Storage data using the MCP tool:

Get CData Connect AI

To get live data access to 300+ SaaS, Big Data, and NoSQL sources directly from your cloud applications, try CData Connect AI today!