Azure SynapseData LakeADFPower BIData GovernanceETL/ELTAnalyticsPurview

TL;DR

A global enterprise relied on an outdated on-prem data warehouse with slow pipelines, unreliable reporting, and growing storage needs.

I led the migration to a modern Azure-based data platform using Synapse Analytics, Data Lake, and Azure Data Factory.

The result: faster analytics, automated pipelines, improved governance, and a scalable platform ready for AI workloads.

Data Analytics Dashboard and Business Intelligence

Data Platform Modernization: Migrating Legacy Analytics to Azure Synapse & ADF

A real-world transformation delivering scalability, governance, and business insights

45%
Faster ETL/ELT
40%
Storage Cost Reduction
Real-Time
Reporting
Zero
Migration Downtime

Impact Summary

  • ETL/ELT pipelines accelerated by ~45% through parallel data flows in Synapse & ADF
  • Operational reporting improved with near-real-time data availability
  • Storage costs reduced by ~40% via tiered lake architecture (hot/warm/cold)
  • Data governance dramatically improved with classification, lineage & RBAC
  • Migration downtime reduced to near zero through staged cutover patterns

Client Context

The client managed multiple business units, each with its own reporting pipelines and siloed databases. Their on-prem data warehouse suffered from:

  • • Slow SQL jobs running overnight
  • • No centralized governance
  • • Expensive storage and compute scaling
  • • Manual ETL scripts and unmanaged dependencies
  • • No ability to support advanced analytics or AI
  • • Finance and operations teams working with stale data

They needed to modernize their data stack, consolidate data flows, improve accuracy, and enable future analytics/AI features.

The Challenge

Technical Challenges

  • Legacy SQL Server workloads not optimized for cloud
  • Dozens of scheduled ETL jobs failing unpredictably
  • No orchestration layer
  • Lack of data lineage and documentation
  • Poor performance due to single-node architecture
  • No separation of compute vs storage
  • Disconnected dashboards relying on Excel exports

Organizational Challenges

  • Analysts, BI teams, and IT working in silos
  • Fragmented definitions ("one KPI" meant 3 different things to 3 teams)
  • No governance board or data ownership model
  • Business frustrated with reporting delays

The client needed a unified, governed, cloud-native data platform with scalable pipelines and reliable analytics.

Cloud Data Platform Architecture

My Role & Approach

As the Data & Cloud Architect, I designed and led the full modernization program across technology, governance, and business alignment.

1. Architecture Assessment & Migration Strategy

Technologies Used:

Azure Synapse AnalyticsAzure Data FactoryAzure Data Lake Gen2SQL PoolsDatabricksPower BIAzure PurviewAzure Monitor

Key Steps:

  • Mapped current data sources, ETL dependencies, and reporting flows
  • Identified critical pipelines, performance bottlenecks, and key KPIs
  • Designed a migration roadmap using the medallion lake architecture (Bronze → Silver → Gold)
  • Defined ingestion strategies for structured & semi-structured data
  • Planned for minimal service disruption using parallel-run and phased cutover

Output: A clear blueprint for the future-state data platform.

2. Data Lakehouse Design & Synapse Modernization

Designed a modern analytics architecture:

Data Lake (ADLS Gen2)

  • • Raw zone (Bronze) to store source data as-is
  • • Clean zone (Silver) for normalized and validated datasets
  • • Curated zone (Gold) for BI-ready data models
  • • Tiered storage reducing cost significantly

Synapse SQL & Spark Pools

  • • Migration of data warehouse tables into Synapse dedicated SQL pools
  • • Use of serverless SQL for lightweight ad-hoc analysis
  • • Spark-based transformations for high-volume workloads

Azure Data Factory Pipelines

  • • Orchestration of ingestion workflows
  • • Data flow transformations for complex ETL
  • • Parameterized pipelines for multi-environment deployments
  • • Error handling, retries, alerts, and data quality checks

This provided scalability, performance, and a future-proof foundation for AI workloads.

3. Data Governance, Security & Lineage

Introduced enterprise-grade governance:

  • RBAC and ACLs on ADLS layers
  • Data classification & sensitivity labels
  • Lineage tracking via Azure Purview
  • Data quality checks integrated into ADF
  • Audit monitoring for access and transformations
  • Centralized KPI definitions and semantic models in Power BI

This established a trusted data foundation across the organization.

4. Reporting Modernization & BI Enablement

  • Migrated legacy Excel/Pivot dashboards to Power BI
  • Built semantic models and shared datasets
  • Implemented RLS (Row-Level Security) per business unit
  • Enabled near-real-time reporting through incremental refresh
  • Provided curated data marts aligned to Finance, Ops, HR, and Sales

Business users finally had consistent metrics and fast dashboards.

5. Organizational Alignment & Knowledge Transfer

  • Conducted workshops with BI analysts, business units, and IT
  • Created documentation, playbooks, and data catalogs
  • Established a Data Governance Board with clear ownership
  • Introduced a unified vocabulary for KPIs & metrics
  • Supported skill uplift for internal teams on ADF, Synapse, and Power BI

This ensured the platform could be maintained and evolved long-term.

The Outcome

The transformation resulted in:

A modern, scalable data platform running fully on Azure
Consolidated and governed data pipelines
Faster analytics and more accurate reporting
Significant cost savings across compute and storage
Improved decision-making with consistent business metrics
A reliable, secure foundation ready for machine learning & GenAI initiatives
A data ecosystem future-proofed for new requirements

The organization transitioned from a fragmented, slow, legacy warehouse to a cloud-native analytics platform that delivered real business value.

Why This Matters for Future Clients

I help organizations:

  • Modernize legacy data warehouses
  • Build scalable analytics platforms on Azure
  • Improve data governance, lineage, and security
  • Design high-performance ETL/ELT pipelines
  • Enable BI teams with cleaner, more reliable data
  • Support future AI, ML, and automation initiatives

If your company is ready to unlock better analytics, more governance, and faster insights — I can lead your data platform modernization end-to-end.

This project demonstrates how a well-executed cloud data platform migration can reduce cost, modernize analytics, and give organizations fast, trusted insight into their business.

Client Feedback

"Iulian transformed our entire approach to data. We went from overnight SQL jobs and Excel exports to a modern lakehouse with real-time dashboards. The medallion architecture he designed gave us flexibility we never had before. More importantly, he brought our fragmented teams together around a single source of truth. The platform he built isn't just faster and cheaper—it's our foundation for AI and analytics for the next decade."
Chief Data Officer
Global Enterprise Client

Ready to Modernize Your Data Platform?

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