Data workflow management can be challenging if you are working with customer information, sales histories, or operations data. You require a platform that streamlines those processes for you while still providing you with strong returns. Microsoft Fabric is up to the task as one complete analytics solution, natively combining data ingestion, transformation, warehousing, and reporting. 

To simplify your life, Microsoft Fabric Copilot presents an AI-driven assistant that allows you to define and operate data pipelines in plain English. This integration simplifies your workflows, saving time and decreasing complexity.

This guide takes you through the process of deploying and utilizing Copilot for Data Factory within Microsoft Fabric. You'll go through the requirements, turn on Copilot, review its features, follow best practices, overcome limitations, ensure ethical use, and debug problems. We begin with what you need to install.

TL;DR / Key Takeaways

• Copilot in Data Factory allows users to create and manage Dataflows Gen2 and Data pipelines using natural language, making complex data engineering tasks easier and faster to execute.

• To enable Copilot, you need a supported Fabric capacity (F64+ or Power BI P1+), Python 3.7+, the Microsoft ODBC Driver for SQL Server (v17+), and admin configuration via the Fabric portal.

• Copilot automates common tasks like building pipelines, transforming data, and resolving errors, significantly reducing manual effort and accelerating workflow development.

• Follow best practices such as writing clear, specific prompts, logically organizing workflows, and reviewing Copilot-generated steps to ensure accuracy and maintain control.

Prerequisites

Before you can access Copilot's features in Data Factory, you must prepare your environment. Microsoft documentation spells out certain prerequisites to get everything up and running, and compliance is essential if you are to be successful. Here are the key prerequisites:

• Supported Fabric SKU: You need a paid Fabric capacity (F64 or higher) or Power BI Premium capacity (P1 or higher). Trial SKUs won’t work, so check your capacity in the Fabric admin portal.

• Software Components: Install Python 3.7 or higher for any Python-related tasks, though Copilot relies primarily on Fabric’s cloud infrastructure. The Microsoft ODBC Driver for SQL Server (version 17 or higher) is required for SQL-based data sources.

• Admin Configuration: Administrators must enable Copilot in the Fabric tenant settings, which involves accessing the admin portal and activating the feature for your organization.

• Region Availability: Copilot uses Azure OpenAI Service, available in US datacenters (East US, East US2, South Central US, West US) and France Central. If your capacity is outside these regions, enable cross-geo data processing in tenant settings.

With these in place, you’re set to enable Copilot and start building smarter workflows.

Enabling Copilot for Data Factory

To use Copilot in Data Factory, you must configure it within Microsoft Fabric’s settings. This involves enabling the feature and setting up permissions, as detailed in Microsoft’s documentation.

Here’s how to enable Copilot:

• Access the Admin Portal: Log into the Fabric admin portal through your workspace. Navigate to tenant settings to manage organization-wide features.

• Enable Copilot: You will see a setting named "Users can use Copilot and other features powered by Azure OpenAI". Use this setting to turn it on. Everyone in your organization capable of using Data Factory features will have access to the Copilot for Data Factory and potentially for other eligible workloads.

• Set Up Permissions: Assign Copilot access to specific users or groups. Ensure users have contributor-level or higher access in the workspace where Data Factory tasks will run, enabling them to use Copilot for Dataflows Gen2 and Data pipelines.

• Handle Cross-Geo Settings: If your capacity is outside the US or France, enable the tenant setting for cross-geo data processing to ensure Copilot functions correctly.

These steps make Copilot accessible, allowing you to use it in Data Factory for streamlined data integration.

Visit Microsoft’s official documentation for more information.

Using Copilot in Data Factory

Once enabled, Copilot becomes your go-to assistant within the Data Factory interface, helping you create and manage data workflows with ease. Microsoft’s documentation describes Copilot as a subject-matter expert who interprets natural language to simplify complex tasks.

To start, access Copilot in the Data Factory editor. When working on Dataflows Gen2 or Data pipelines, find the Copilot button on the Home tab. Clicking it opens a chat pane where you can type natural language prompts. For example, you might enter, “Build a pipeline to move customer data from a SQL database to a lakehouse,” and Copilot will suggest the necessary activities.

For Dataflows Gen2, you can ask Copilot to perform transformations, like “Filter rows where sales are below 500.” It generates the steps, which you can review and apply. If you’re using dbt, you can request Copilot to trigger dbt run or dbt test commands, provided your dbt project is configured, streamlining transformations.

Monitor Copilot-initiated workflows using Fabric’s monitoring tools. Check the status of Data pipelines or Dataflows in the workspace to ensure successful execution. If a task fails, click the error message icon next to the activity. Copilot provides a summary and suggestions to fix the issue, making troubleshooting straightforward.

As you get started with Copilot, you will notice several built-in features that will allow you to manage data workflows quickly and easily. Let's take a look at the main features of Copilot that make it unique.  

Features of Copilot in Data Factory

Copilot enhances Data Factory with features that simplify data integration and transformation. Microsoft’s documentation highlights how these capabilities save you time and effort.

Key features include:

• Dataflow Generation and Transformation: Copilot creates new queries or transformation steps in Dataflows Gen2 based on your prompts. For example, asking “Add a column for total revenue” generates a multiplication step for unit price and quantity.

• Automated Pipeline Creation: Copilot builds Data pipeline activities, like Copy activity, from natural language inputs. Request “Move CSV data to a warehouse,” and it configures the pipeline, which you can tweak for specific connections.

• Error Handling Assistance: When a pipeline fails, Copilot explains error messages and suggests fixes. For instance, a failed Copy activity might prompt a recommendation to verify your linked service settings.

Also Read: Managing Microsoft Fabric Capacity and Licensing Plans

Now that you're familiar with what Copilot can do, it's time to look at how to use it most effectively. Following a few best practices can help you maximize accuracy, clarity, and performance in your workflows.

Best Practices for Using Copilot

To achieve optimal performance from Copilot within Data Factory, adopt practices that guarantee effective and trusted workflows, as advised in Microsoft's documentation.

Here are key best practices:

• Organize Workflows Logically: Organize Dataflows and Data pipelines logically by separating related steps, such as ingestion or transformation, into independent pipelines. For instance, design one pipeline for raw data uploading and another for report generation.

• Use Clear Prompts: Tell precise prompts, such as "Remove null values from the sales column," rather than imprecise ones such as "Clean my data." This helps Copilot produce correct transformation steps.

• Keep It Simple and Iterative: Start with basic tasks, like a single Copy activity, and add complexity gradually. This lets you verify each step before scaling up.

• Review Outputs: Always review Copilot's produced pipelines or transformations. For example, double-check that a filtered dataset satisfies your requirements before putting it into production.

Although it's an enormously powerful tool, Copilot has some significant limitations. Knowing them will ensure you can effectively plan ahead and steer clear of unexpected hurdles..

Limitations and Considerations

Copilot has some limitations you should understand to use it effectively, as noted in Microsoft’s documentation.

Key limitations include:

• Single-Query Transformations: Copilot can’t apply transformations across multiple queries in one prompt. For example, you must specify transformations query by query, like “Capitalize headers in query A.”

• No Undo Functionality: Once you commit changes via Copilot, you can’t undo them automatically. Use the Data Factory UI to remove unwanted steps manually.

• Performance Variability: Copilot’s performance depends on your Fabric capacity. Ensure you’re using F64 or higher to avoid slowdowns with large datasets.

For long-term adoption, plan for scalability by monitoring capacity needs. Regularly review generated outputs to ensure they align with your goals, and consider upgrading your SKU for complex pipelines.

Beyond functionality, it’s essential to consider how to use Copilot responsibly. Let’s discuss privacy, security, and ethical AI use to ensure your data processes stay compliant and trustworthy.

Privacy, Security, and Responsible AI Use

Using Copilot responsibly involves safeguarding data and ensuring ethical AI use, as outlined by Microsoft.

Key considerations include:

• Role-Based Access: Assign Copilot access only to users with contributor-level or higher permissions in the workspace. This protects sensitive data in Dataflows and Data pipelines.

• Data Privacy Settings: Enable cross-geo data processing for non-US/EU regions in the admin portal to comply with data residency requirements while using Copilot.

• Responsible AI: Monitor Copilot’s outputs for accuracy, as it may produce errors. Review generated pipelines to ensure they meet your business needs, preventing unreliable results.

Even with careful setup and responsible use, you may occasionally run into issues. Here’s how to troubleshoot the most common challenges with Copilot in Data Factory.

Troubleshooting Common Issues

Issues with Copilot in Data Factory can arise, but Microsoft’s documentation offers solutions to keep you on track.

Common issues and fixes include:

• Connectivity Problems: If Copilot can’t access data sources, verify linked service settings in Data pipelines. Ensure the Microsoft ODBC Driver is installed and credentials are valid.

• User Access Issues: Confirm users have contributor access in the workspace and that Copilot is enabled in tenant settings. Check group assignments in the admin portal.

• Feature Limitations: If Copilot is unresponsive, verify your Fabric capacity (F64+) and region settings. Restarting the session or reallocating resources can resolve performance issues.

These tips help you address problems quickly, ensuring smooth Copilot operation.

Conclusion

Copilot for Data Factory in Microsoft Fabric revolutionizes your data workflows, letting you build Dataflows Gen2 and Data pipelines with simple natural language prompts. By setting up Copilot, using its features, and following best practices, you streamline complex tasks while ensuring reliability. 

Addressing limitations and prioritizing security keeps your pipelines robust. Want to explore Copilot’s full potential? WaferWire’s Microsoft Fabric experts can help you implement and optimize it for your needs. Contact WaferWire today to transform your data strategy.

FAQs

Here are five common questions about using Copilot for Data Factory, with answers to guide your journey.

1. Why use Copilot in Data Factory for analytics?
Copilot simplifies creating Dataflows Gen2 and Data pipelines with natural language, reducing manual work. For example, you can generate a pipeline to load sales data into a lakehouse, speeding up insights with minimal effort.

2. What’s required to enable Copilot in Fabric?
You need a Fabric capacity (F64+), Python 3.7+, the Microsoft ODBC Driver and Copilot enabled in the admin portal. For non-US/EU regions, enable cross-geo data processing to ensure Copilot works.

3. How does Copilot streamline Data pipeline creation?
Copilot generates Data pipeline activities, like Copy activity, from prompts like “Move data from a CSV to a warehouse.” You can refine these in the Data Factory editor, making pipeline creation intuitive.

4. How do you fix Copilot errors in Data Factory?
Verify linked service configurations and credentials for connectivity issues. Ensure contributor access for users and sufficient capacity (F64+). Use Copilot’s error assistant to diagnose and resolve failed pipeline activities.

5. What limits Copilot’s functionality in Data Factory?
Copilot can’t transform multiple queries in one prompt or undo changes after commits. Review outputs for accuracy, as errors may occur. Ensure adequate capacity to avoid performance issues with complex workflows.