Using Copilot for Fabric Development
Microsoft Fabric
Microsoft Fabric9 min read

Using Copilot for Fabric Development

Accelerate Microsoft Fabric development with Copilot AI. Generate PySpark code, SQL queries, DAX formulas, and data insights using natural language.

By Administrator

Copilot in Microsoft Fabric uses generative AI to accelerate development across every Fabric workload—SQL warehouses, Spark notebooks, Power BI reports, and Data Factory pipelines. Rather than replacing developers, Copilot acts as an intelligent pair programmer that generates first drafts, explains unfamiliar code, suggests optimizations, and handles boilerplate. For organizations investing in Fabric, Copilot can reduce development time by 30-50% for routine tasks while maintaining code quality through mandatory human review.

Copilot in Data Warehouse

Fabric's SQL-based warehouse integrates Copilot for natural language to SQL conversion:

Query Generation: Describe what you want in English, and Copilot generates the SQL query. "Show me total revenue by product category for Q4 2025, only categories with revenue above $1 million, sorted by revenue descending" produces a working SELECT statement with GROUP BY, HAVING, and ORDER BY clauses. The generated query references actual table and column names from your warehouse schema.

Query Explanation: Paste a complex query written by someone else, and Copilot explains it in plain language—what the CTEs do, why the window functions are used, and what the expected output represents. Invaluable for onboarding new team members to an existing data warehouse.

Query Optimization: Submit a slow query and ask Copilot for optimization suggestions. It identifies missing indexes, suggests materialized views, recommends query restructuring, and flags common anti-patterns like correlated subqueries that could be rewritten as joins.

DDL Generation: Describe a table structure in English, and Copilot generates CREATE TABLE statements with appropriate data types, distribution strategies, and constraints. Useful for rapid prototyping of new warehouse schemas.

Copilot in Notebooks

Spark notebooks benefit from Copilot's code generation capabilities:

PySpark Code Generation: Describe data transformations in English. "Read the sales table from the lakehouse, filter to 2025 data, join with the customer dimension on CustomerID, calculate total revenue and order count per customer, and write the result as a Delta table called customer_summary." Copilot generates the complete PySpark code with proper DataFrame operations.

Code Debugging: Paste error tracebacks, and Copilot identifies the issue and suggests fixes. It recognizes common Spark errors—serialization failures, out-of-memory exceptions, schema mismatch errors—and provides targeted solutions.

Code Explanation: Highlight a code block and ask "explain this code." Copilot describes what each section does, identifies potential issues, and suggests improvements. Particularly helpful for reviewing code written by other team members.

Documentation Generation: Ask Copilot to generate docstrings, comments, and markdown documentation for your notebook. It produces context-aware documentation that describes the business logic, not just the technical operations.

Copilot in Power BI

Power BI Copilot capabilities span report creation and data analysis:

Report Page Generation: Describe the report you want: "Create a sales dashboard with revenue by region, monthly trend, top 10 products, and a YoY comparison." Copilot generates a complete report page with appropriate visuals, layout, and formatting. The result is a starting point that you refine, not a finished product.

DAX Measure Generation: Describe the calculation in business terms: "Create a measure that shows the rolling 3-month average of revenue, only for active customers, compared to the same period last year." Copilot generates the DAX with CALCULATE, DATESINPERIOD, and SAMEPERIODLASTYEAR patterns. Always verify the generated DAX logic against sample data.

Data Q&A: Users ask questions about their data in natural language, and Copilot provides answers with visualizations. More sophisticated than the older Q&A feature—Copilot understands complex multi-step questions and generates appropriate DAX queries behind the scenes.

Narrative Generation: Copilot generates text summaries of data: "Revenue increased 12% YoY driven primarily by the West region (+23%) while the East region declined 5% due to the Q3 supply chain disruption." These narratives update dynamically as data refreshes.

Copilot in Data Factory

Pipeline development benefits from AI assistance:

Pipeline Design: Describe your data flow, and Copilot suggests pipeline structure—which activities to use, how to handle errors, and where to add validation checkpoints.

Expression Building: Data Factory expressions for dynamic content (parameterized file paths, conditional logic, variable manipulation) have an unintuitive syntax. Copilot generates these expressions from plain language descriptions.

Troubleshooting: Describe a pipeline failure, and Copilot suggests likely causes and fixes based on the error pattern.

Effective Prompting Strategies

Be Specific and Contextual Poor prompt: "Write a query for sales data" Good prompt: "Write a SQL query against the FactSales table that calculates total SalesAmount and OrderCount by ProductCategory from DimProduct, for the year 2025 only, grouped by month, and filter out categories with less than $100,000 in total sales"

Iterate in Stages 1. Generate the initial code with a basic prompt 2. Ask for modifications: "Add error handling for null values" 3. Request optimization: "This query runs slowly on 500M rows—suggest optimizations" 4. Add documentation: "Generate comments explaining the business logic"

Provide Schema Context When Copilot does not automatically detect your schema, include table and column names in your prompt. "Using the FactSales table (columns: SalesKey, DateKey, ProductKey, CustomerKey, SalesAmount, Quantity) and DimDate table (columns: DateKey, FullDate, Year, Quarter, Month)..."

Limitations and Governance

Always Review Output: Copilot generates plausible code that may be logically incorrect for your specific business rules. A measure that looks correct syntactically might calculate the wrong number due to incorrect filter context. Test every Copilot-generated DAX measure against known values.

Data Residency: Copilot sends schema metadata and potentially sample data to Azure OpenAI services for processing. Understand where this processing occurs relative to your data residency requirements. In regulated environments, consult your compliance team before enabling Copilot.

Licensing Requirements: Copilot features require Fabric capacity (F64 or higher for full Copilot capabilities) and tenant admin enablement. Some Copilot features roll out progressively—availability varies by region and workload.

Not a Replacement: Copilot accelerates experienced developers—it does not replace the need for understanding DAX, PySpark, SQL, or data modeling fundamentals. Developers who cannot evaluate Copilot's output are at risk of deploying incorrect logic.

Related Resources

Frequently Asked Questions

Is Copilot available in all Fabric workloads?

Copilot is rolling out across Fabric workloads progressively. Availability varies by workload and region. Check Microsoft documentation for current availability in your tenant.

Does Copilot understand my specific data?

Copilot has context about your schema and metadata. It uses this context to generate relevant queries and code, but always verify the output matches your business logic.

Microsoft FabricCopilotAIDevelopment

Industry Solutions

See how we apply these solutions across industries:

Need Help With Power BI?

Our experts can help you implement the solutions discussed in this article.

Ready to Transform Your Data Strategy?

Get a free consultation to discuss how Power BI and Microsoft Fabric can drive insights and growth for your organization.