Modernizing a Legacy Data Warehouse with Azure Synapse

Client Overview

A global retail brand with over 400 stores in North America was relying on a legacy on-premises SQL Server data warehouse.

Their reporting process supported dozens of business units, including sales, finance, marketing, and supply chain.

As the volume of data and reporting needs grew, the legacy setup became costly, hard to maintain, and performance began to degrade.

Business Challenge

The client faced several critical issues:

• The SQL Server warehouse couldn’t scale efficiently to handle growing data volumes.
• Nightly ETL jobs were taking too long, often missing SLAs for morning dashboards.
• Infrastructure costs were rising due to frequent hardware upgrades and support needs.
• There was no separation of workloads — queries from analysts often slowed down ETL.
• The business couldn’t enable real-time analytics or integrate cloud-native ML tools.

The organization needed a modern, scalable solution to support self-service BI and advanced analytics while reducing infrastructure overhead.

Objective

To re-architect the data warehouse using Azure Synapse Analytics and modern cloud practices to achieve:

• High-performance, elastic querying at scale
• Faster ETL/ELT pipelines with parallel processing
• Integration with Azure Data Lake, Power BI, and Azure ML
• Reduced infrastructure maintenance and license costs
• Secure access and data governance for enterprise users

Solution Delivered

Ghanshyam Data Tech designed and executed a phased migration strategy to Azure Synapse Analytics.

Assessment & Planning: Conducted a full inventory of legacy SQL Server objects — tables, stored procedures, SSIS packages, and user workloads. Identified transformation logic and business-critical data marts.

Data Modeling & Landing Zone Setup: Built a modernized data model in Synapse SQL pools using star schemas, partitioning, and columnstore indexes. Connected the model to Azure Data Lake Gen2 for cost-effective storage and staging layers.

Migration & Pipeline Development: Legacy ETL jobs were re-engineered using Azure Data Factory and Synapse Pipelines for scalable, code-free workflows. Adopted an ELT strategy, pushing complex logic to Synapse for better performance.

Integration & Reporting: Integrated the new warehouse with Power BI datasets, enabling semantic modeling and real-time dashboarding. Deployed row-level security and Azure AD integration for secure access.

Automation & Monitoring: Implemented CI/CD pipelines for deployment using Azure DevOps and added Synapse monitoring via Log Analytics for job failure alerts and performance tracking.

Achieved Results & Strategic Impact

After the migration to Azure Synapse Analytics:

• ETL process runtime was reduced by 62% — from 6.5 hours to just 2.5 hours
• Dashboard delivery SLA improved from 9 AM to before 6 AM, boosting productivity
• Reduced data processing data and increased data refresh frequency
• Enabled Power BI reports with 10x faster response times using materialized views
• Improved governance with RBAC and secure sharing across business units

The new cloud data platform provided agility, transparency, and scalability — positioning the client to integrate predictive analytics and AI in future phases.

Key Takeaways

• Modern cloud data warehouses like Azure Synapse offer elasticity, high concurrency, and lower maintenance overhead.
• Shifting from ETL to ELT improves performance when paired with Synapse’s inbuilt compute capabilities.
• Integrating Power BI, ADF, and Synapse within Azure creates a unified analytics ecosystem.
• A well-planned migration approach ensures minimal disruption to business reporting.

Tools & Technologies Used

• Azure Synapse Analytics
• Azure Data Factory (ETL/ELT Pipelines)
• Azure Data Lake Gen2
• Power BI (Dataset & Reporting Layer)
• Azure DevOps (CI/CD)
• Log Analytics & Azure Monitor
• Azure Active Directory (Security & Access Control)