How to Import Financial Data into Microsoft Fabric: Methods and Best Practices

Microsoft Fabric offers several methods for importing your financial data. Whether you work with Excel files, ERP databases, or APIs, this guide helps you choose the best approach.

Ingestion Methods in Microsoft Fabric

1. Dataflows Gen2

Dataflows Gen2 are perfect for non-technical users who want to import and transform data via a visual interface.

Ideal use case:

• Importing Excel or CSV files from SharePoint
• Simple transformation of accounting data
• Creating reference tables (chart of accounts, cost centers)

2. Data Pipelines

Pipelines offer more control and allow orchestration of complex workflows.

Ideal use case:

• Import from ERPs (SAP, Oracle, Dynamics)
• Scheduled loads with dependencies
• Processing large data volumes

3. Notebooks (Python/Spark)

For technical teams, notebooks offer maximum flexibility.

Ideal use case:

• Complex transformations with Python/Pandas
• Financial API integration
• Advanced statistical calculations

4. Shortcuts

Shortcuts allow access to external data without copying it.

Ideal use case:

• Accessing Azure Data Lake data
• Connecting to data shared between teams
• Reducing storage costs

Import from Common Financial Sources

Excel and Flat Files

Source → OneLake → Dataflow Gen2 → Lakehouse

1. Drop your Excel files into OneLake
2. Create a Dataflow Gen2 to transform the data
3. Load into a Lakehouse table

SAP and ERP

Source → Pipeline (Copy Activity) → Lakehouse → Transformation

1. Configure the SAP connector in the pipeline
2. Schedule daily extraction
3. Transform with a notebook or Dataflow

Banking and Financial APIs

API → Notebook (requests) → DataFrame → Lakehouse

1. Use Python to call the API
2. Parse the JSON response
3. Load into a Delta table

Best Practices for Financial Data

1. Bronze/Silver/Gold Separation

Organize your data in layers:

• Bronze: Raw data, exactly as received
• Silver: Cleaned and validated data
• Gold: Aggregated data ready for analysis

2. Accounting Period Management

Make sure to properly manage:

• Closing dates
• Inter-period adjustments
• Restatements

3. Reconciliation Controls

Implement automatic controls:

• Control totals between source and destination
• Accounting balance validation
• Alerts in case of discrepancy

4. Security and Compliance

For sensitive financial data:

• Use Row-Level Security (RLS)
• Enable access auditing
• Encrypt sensitive data

Comparison Table of Methods

| Method | Complexity | Volume | Real-time | Usage | |--------|------------|--------|-----------|-------| | Dataflows Gen2 | Low | Medium | No | Simple transformations | | Pipelines | Medium | High | No | Complex orchestration | | Notebooks | High | High | Possible | Advanced calculations | | Shortcuts | Low | High | Yes | Direct access |

Example: Monthly Accounting Data Import

Here's a typical workflow for closing data import:

1. Day-1 of closing: Automatic extraction from ERP
2. Transformation: Data cleaning and validation
3. Controls: Reconciliation with trial balances
4. Publication: Made available in Power BI
5. Notification: Teams alert to users

Conclusion

Microsoft Fabric offers the flexibility needed to handle all your financial data import use cases. Choose the method adapted to your context and implement appropriate controls to ensure the reliability of your analyses.

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