HEALTHCARE & PHARMACEUTICALS
Ora Clinical Case Study: From Raw Data to Insights Through a Robust Data Pipeline
Dec 10
•10 min read
### **Objective**
The project aims to build a streamlined and scalable data pipeline solution focused on delivering clean, structured, and analysis-ready data to clients. By leveraging modern data engineering tools and best practices, the objective is to ensure efficient data ingestion, transformation, and validation processes. This initiative is designed to simplify data access, improve data quality, and support downstream analytics and decision-making with reliable, consistent datasets.
### **Technology**
**1. Pipelines** – Automated flows that bring data from different systems into one place.
**2. Processing (Notebooks)** – Tools that clean and prepare the data for use.
**3. Lakehouse** – A central storage layer for both raw and refined data.
**4. Warehouse** – A structured layer built for fast reporting and analysis.
**5. Model** – A simplified view of the data that makes reporting easier.
**6. Dashboards** – Visual reports that show insights and updates in real-time.
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**Goals**
**1. Centralize All Study Data** – Bring data from diverse data sources into one unified platform instead of multiple scattered sources.
**2. Improve Data Quality** – Clean, standardize, and validate all incoming data so it becomes accurate, consistent, and ready for reporting.
**3. Enable Faster, Reliable Reporting** – Make Power BI dashboards run faster by using Lakehouse storage, incremental loads, and optimized data models.
**4. Reduce Manual Work** – Automate data ingestion, transformation, and push-back processes to remove manual file handling and repeated work.
**5. Support Study-Level Billing** – Ensure correct and updated information is pushed back into Veeva to help the billing team generate accurate invoices.
**6. Build a Scalable Architecture** – Create a system that can easily handle more studies, more APIs, and new requirements in the future without major rework.
**Solution**
**1. Connected All Sources** – Integrated iMednet, Medidata, and Veeva into a single Lakehouse using secure API and SFTP connections.
**2. Built Automated Pipelines** – Set up Microsoft Fabric Pipelines to fetch, load, and refresh data automatically at scheduled intervals.
**3. Structured the Data for Use** – Organized the data into a clear, consistent format so teams can use it directly for reports and analysis.
**4. Enabled Veeva Push-Back** – Implemented a workflow that sends required study and billing updates back into Veeva after processing.
**5. Fabric-Only Architecture** – Designed using Microsoft Fabric tools, keeping the solution lightweight, simple, and cost-effective.
**Pre-Fabric Architecture Overview**
[ ]()
**Before Fabric Implementation:**
1\. Power BI was directly taking data from APIs, making everything slow and dependent on API speed.
2\. Reports took **5–6 hours to refresh** because all data was pulled every time.
3\. No centralized storage — no organized or reusable dataset.
4\. No incremental load — system downloaded all data instead of only new/updated records.
**Post-Fabric Architecture Overview**
[ ]()
**After Fabric Implementation:**
1\. All data lands first in the Lakehouse through automated pipelines for clean and organized storage.
2\. Data processed through **Medallion Architecture (Bronze → Silver → Gold)** for quality and reliability.
3\. Power BI connects via **DirectLake**, dashboards load instantly without API dependency.
4\. Significant performance improvement — reduced API hits and stable incremental loads.
5\. Report refresh now **3–4 minutes** instead of several hours.
**After Fabric Implementation Architecture**
**After Fabric Implementation **
1\. Data first lands in Lakehouse via pipelines
2\. Transformed in Bronze → Silver → Gold architecture
3\. Power BI connects using Direct Lake
4\. Faster performance, reduced API load
5\. stable refreshes upto 3 to 4 minutes
**Previous Manual Process**
1\. Files sent to Veeva via manual uploads (Excel, CSV, etc.)
2\. High chances of human error
3\. No automated validation/checks
4\. No audit trail or version control
5\. Slow and inconsistent billing updates
6\. Processing time ranged from minutes to hours/days for large data volumes
[ ]()
**Automated Push Pipeline**
1\. Fabric prepares and validates study data
2\. iMednet & Medidata data pushed directly back into Veeva via pipeline
3\. Consistent, error-free updates
4\. Complete logging + audit trail
5\. Faster and accurate billing cycles
6\. Bulk processing enabled — **\~30,000 records in under 2 minutes**
7\. Maintainable configuration setup
8\. Complete logging framework tracks every push-pipeline step (success, validation, warnings, errors) ensuring transparency & traceability
[ ]()
**Fabric Implementation Architecture**
**Goals**
**1. Centralize All Study Data** – Bring data from diverse data sources into one unified platform instead of multiple scattered sources.
**2. Improve Data Quality** – Clean, standardize, and validate all incoming data so it becomes accurate, consistent, and ready for reporting.
**3. Enable Faster, Reliable Reporting** – Make Power BI dashboards run faster by using Lakehouse storage, incremental loads, and optimized data models.
**4. Reduce Manual Work** – Automate data ingestion, transformation, and push-back processes to remove manual file handling and repeated work.
**5. Support Study-Level Billing** – Ensure correct and updated information is pushed back into Veeva to help the billing team generate accurate invoices.
**6. Build a Scalable Architecture** – Create a system that can easily handle more studies, more APIs, and new requirements in the future without major rework.
**Solution**
**1. Connected All Sources** – Integrated iMednet, Medidata, and Veeva into a single Lakehouse using secure API and SFTP connections.
**2. Built Automated Pipelines** – Set up Microsoft Fabric Pipelines to fetch, load, and refresh data automatically at scheduled intervals.
**3. Structured the Data for Use** – Organized the data into a clear, consistent format so teams can use it directly for reports and analysis.
**4. Enabled Veeva Push-Back** – Implemented a workflow that sends required study and billing updates back into Veeva after processing.
**5. Fabric-Only Architecture** – Designed using Microsoft Fabric tools, keeping the solution lightweight, simple, and cost-effective.
**Pre-Fabric Architecture Overview**
[ ]()
**Before Fabric Implementation:**
1\. Power BI was directly taking data from APIs, making everything slow and dependent on API speed.
2\. Reports took **5–6 hours to refresh** because all data was pulled every time.
3\. No centralized storage — no organized or reusable dataset.
4\. No incremental load — system downloaded all data instead of only new/updated records.
**Post-Fabric Architecture Overview**
[ ]()
**After Fabric Implementation:**
1\. All data lands first in the Lakehouse through automated pipelines for clean and organized storage.
2\. Data processed through **Medallion Architecture (Bronze → Silver → Gold)** for quality and reliability.
3\. Power BI connects via **DirectLake**, dashboards load instantly without API dependency.
4\. Significant performance improvement — reduced API hits and stable incremental loads.
5\. Report refresh now **3–4 minutes** instead of several hours.
**After Fabric Implementation Architecture**
**After Fabric Implementation **
1\. Data first lands in Lakehouse via pipelines
2\. Transformed in Bronze → Silver → Gold architecture
3\. Power BI connects using Direct Lake
4\. Faster performance, reduced API load
5\. stable refreshes upto 3 to 4 minutes
**Previous Manual Process**
1\. Files sent to Veeva via manual uploads (Excel, CSV, etc.)
2\. High chances of human error
3\. No automated validation/checks
4\. No audit trail or version control
5\. Slow and inconsistent billing updates
6\. Processing time ranged from minutes to hours/days for large data volumes
[ ]()
**Automated Push Pipeline**
1\. Fabric prepares and validates study data
2\. iMednet & Medidata data pushed directly back into Veeva via pipeline
3\. Consistent, error-free updates
4\. Complete logging + audit trail
5\. Faster and accurate billing cycles
6\. Bulk processing enabled — **\~30,000 records in under 2 minutes**
7\. Maintainable configuration setup
8\. Complete logging framework tracks every push-pipeline step (success, validation, warnings, errors) ensuring transparency & traceability
[ ]()
**Fabric Implementation Architecture**
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