The Challenge
The cost of legacy systems.
Holding onto outdated architecture isn't just about technical debt—it's actively preventing your business from operating with agility and costing you significantly in maintenance.
The Nightly Batch Run That Now Takes 14 Hours
Your SQL Server used to process everything overnight. As data volume grew, "overnight" stretched into the morning. Now, the 6:00 AM refresh finishes at 10:30 AM. Business users wait half the day for yesterday's data, and your engineers receive frantic emails every morning.ning.ning.ning.
The Cost of Maintaining Server Hardware
You're paying for peak capacity 24/7. When the finance team runs complex quarterly reporting, the server grinds to a halt. When nobody is querying the system on Sunday, you're still paying the same compute costs. Scaling up means painful downtime and budget approvals.vals.
Talent Drain
Your best data engineers are spending 80% of their time maintaining brittle ETL pipelines and tuning indexes on over-burdened servers. They want to build AI and advanced analytics, but they're stuck doing plumbing. Modern talent demands modern tools.
Deliverables
A clean transition to modern data.
Migration Strategy & Architecture
A detailed plan mapping current source systems to Microsoft Fabric Lakehouse architecture, including security, governance, and capacity sizing. We don't just lift and shift; we re-architect for the cloud.
Fabric Workspace & Security
Landing zones, Medallion Architecture (Bronze, Silver, Gold), row-level security, and Purview data catalog configuration established on day one.
Automated Data Ingestion
Data Factory pipelines moving data from legacy SQL, Oracle, or APIs into the Fabric OneLake, designed for incremental loading and schema evolution.
Refactored Models & Validated Reports
Legacy stored procedures converted to Fabric Notebooks (PySpark/SQL) optimizing for distributed compute. We re-point existing Power BI dashboards to the new semantic models with automated testing to prove the numbers match the legacy system exactly.
Methodology
Our migration methodology
Assessment, architecture, wave-based migration, and validation — typically delivered in 30-day waves for a full enterprise data warehouse migration.
Assessment & Sizing
We catalog existing objects (tables, views, stored procedures), identify technical debt that shouldn't be migrated, and size the Microsoft Fabric capacity required for your actual workloads.
Foundation & Governance Build
Before moving data, we stand up the Fabric environment, configure Purview data catalog, and establish Azure Active Directory (Entra ID) security patterns.
Wave-Based Migration
We migrate by functional subject area (e.g., Sales, then Finance), running the old and new systems in parallel. This allows business continuity while validation occurs.
Validation & Cutover
Automated data reconciliation scripts verify that calculations in Fabric match the legacy system. Once signed off by business owners, we cut over reporting and deprecate the legacy servers.
Migrating a global manufacturer from legacy SQL to Fabric.
The Situation
A medical device manufacturer relied on a 15-year-old on-premise SQL Server data warehouse. Daily processing of IoT device telemetry and global SAP ERP data took 12 hours. Any failure meant supply chain analysts ran daily planning without current inventory data. Hardware upgrades were quoted at $250K just to maintain current performance.
The Solution
- Replaced 800+ legacy SSIS packages with metadata-driven Fabric Data Factory pipelines.
- Refactored complex, nested Stored Procedures into PySpark Notebooks, parallelizing execution.
- Implemented Purview to map lineage from source SAP tables to final FDA compliance reports.