In this blog, I’ll walk you through how to build a scalable data pipeline using Microsoft Fabric—covering every stage from data ingestion to automation.
To make things more practical, I’ll use an Indian Box Office data pipeline project as an example to demonstrate the process.
Data Pipeline Orchestration Architecture Diagram:
Why Microsoft Fabric for Data Pipelines?
Microsoft Fabric is an end-to-end analytics platform that brings together data engineering, data integration, data science, real-time analytics, and business intelligence in one place.
For this project, I leveraged:
• Lakehouse for storing raw files.
• Warehouse for structured, query-ready data.
• Data Pipelines for automation and orchestration.
Step-by-Step Implementation
1. Workspace Creation:
Create a dedicated Microsoft Fabric workspace to keep all related assets—datasets, pipelines, and reports—organized in one environment.
2. Lakehouse Setup & Data Upload:
In the example project, I created a indianbo_LH Lakehouse to store raw CSV files.
• For the Indian Box Office example, the dataset contained 52 CSV files across multiple industries—Bollywood, Kollywood, Mollywood, Sandalwood, and Tollywood.
• These files were uploaded into the Lakehouse’s Folders section.
3. Warehouse Creation:
Set up a Warehouse to hold transformed and structured datasets.
• In the example, I created indianbo_WH as the destination for all processed movie industry data.
4. Data Pipeline Creation:
A pipeline named indian-bo-pipeline was built to:
• Filter datasets based on specific conditions.
• Move them into the Warehouse.
• Send teams notifications upon successful execution.
5. Adding Pipeline Activities:
• Filter Activity – Isolated datasets for a specific category (e.g., Bollywood).
• Get Metadata Activity – Retrieved file details for processing.
• For Each Activity – Looped through filtered datasets to apply operations.
• Copy Activity – copying the files from lakehouse
• Teams Activity – to send teams notifications
This same process was applied to Kollywood, Mollywood, Sandalwood, and Tollywood datasets in the example.
6. Copy Data into Warehouse:
Inside each loop:
• Source – CSV files from the Lakehouse.
• Destination – Corresponding tables in the Warehouse.
7. Adding Notifications:
Using Teams Activities, automated the notifications were sent after each industry’s dataset was loaded successfully.
8. Orchestration & Execution:
• The pipeline was saved and executed.
• Monitoring tools showed live progress.
• All target tables were populated successfully with industry-specific data.
Results & Benefits:
• Centralized Storage – Structured data stored in the Warehouse.
• Scalable Setup – Easily adaptable to new datasets or industries.
• Automation – No manual intervention for filtering, copying, or notifications.
• Analytics Ready – Data instantly available for Power BI reports.
Key Takeaways:
• Microsoft Fabric’s integration of Lakehouse, Warehouse, and Data Pipelines makes building automated data workflows simple.
• Filtering, orchestration, and notifications can significantly improve efficiency.
• While this example focused on the Indian Box Office, the same approach applies to retail, healthcare, finance, and more.
Take a look at the tutorial for more details : https://youtu.be/viJyBoQclNo?si=0KyL7RHOJ2gVdhHF
— Inturi Suparna Babu
