The Migration Problem We Don’t Talk About: Where Data Fails Before It Even Moves

Case Study

About the client

Barry-Wehmiller is a global leader in manufacturing and engineering solutions, operating across multiple business units and geographies. With diverse systems and legacy data sources, the organisation requires high data consistency, traceability, and reliability across environments.

Use case Overview

Reliable data movement across multiple environments
Controlled and traceable batch processing
Reduced dependency on manual validation
Improved trust in migrated data

Challenges

Data Consistency Across Environments
Lack of Control in Bulk Processing
High Manual Validation Effort
Risk of Data Issues
Limitations of Traditional Migration Approaches

List of Deliverables

Designed and implemented batch-driven data pipelines using Azure Data Factory
Built centralised transformation logic in Azure SQL Database to standardise business/Data rules
Developed data standardisation and processing workflows using Databricks
Implemented lakehouse-based staging architecture using Microsoft Fabric
Created real-time data validation dashboards in Power BI
Built batch-level failure tracking and recovery framework
Enabled end-to-end data traceability across environments
Optimised pipelines for scalability, performance, and controlled execution

Tools and Technologies Used

To test this approach, a modern data stack was implemented. The system was tested on core data such as operations, Production etc. Data across multiple environments, But the technology itself wasn’t the most important part. The design was.

Azure Data Factory

Data orchestration, ETL pipelines, batch processing

Microsoft Fabric

Lakehouse architecture, data staging, unified analytics

Azure SQL Database

Centralised transformation logic, structured data processing

Databricks

Big data processing, data standardisation, scalable transformations

Power BI

Real-time dashboards, data validation, reporting

Data Architecture

The architecture follows a modern cloud data engineering pattern: Source Systems → Staging (Lakehouse) via Microsoft Fabric Batch-Orchestrated Pipelines using Azure Data Factory Transformation Layer in Azure SQL + Databricks Validation Layer with embedded rules Consumption Layer via Power BI dashboards Key Design Principle: 👉 Batch-driven, controlled, and observable data pipelines

Target Audience

The target audience for these points would include:

Migration Program Managers : Leaders overseeing data migration programs, timelines, risk management, and delivery outcomes.
Business Stakeholders / SMEs : Domain experts who rely on trusted data for operational and strategic decisions.
IT & Digital Transformation Leaders: Stakeholders driving cloud adoption, modern data platforms, and enterprise-wide transformation initiatives.

Solution

The POC introduced a controlled migration framework:

Batch-Driven Orchestration

- Data processed in smaller units to ensure:
- Failure isolation
- Safe reprocessing
- Improved pipeline stability

CentralisedTransformation Logic

- Standardised business rules
- Reduced inconsistency across systems

Embedded Validation

- Validation integrated within pipelines
- Real-time visibility instead of end-stage checks

Observability & Monitoring

- Power BI dashboards for continuous validation
- Traceability across batches and environments

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Impact

100% prevention of duplicate data loads (tested scenarios)
Zero overwrite issues during reprocessing
~50% reduction in manual validation effort
Improved pipeline stability with batch-level failure isolation
Faster issue detection via real-time dashboards

👉 Most importantly: Improved trust in data