hello@itrexgroup.com +1 559 550 4575

←←All cases

Data platform migration for a water utility company

Client

A regional US water utility company responsible for delivering safe, reliable water services and maintaining critical infrastructure across multiple service areas

Industry

Utilities

Services

Data platform consulting, cloud consulting, data governance, data analytics, AI readiness assessment

Tech stack

Microsoft Azure, Databricks (Delta Lake, Delta Live Tables, Unity Catalog), PySpark, SQL, Azure Data Lake Storage (ADLS Gen2), Azure Data Factory, Power BI, GitHub Actions, Azure DevOps

Challenge

Before this project started, ITRex conducted a comprehensive data platform assessment, giving the client a clear modernization roadmap built on their chosen platform, Microsoft Fabric. The proposed architecture promised to eliminate data silos, improve reporting accuracy, and enable future AI-driven insights. When the implementation phase began, the client encountered insurmountable commercial challenges related to licensing and vendor management within the Microsoft Fabric environment. This slowed and eventually stopped the Fabric rollout, putting the company's digital transformation on hold. To get their data strategy back on track, the client needed an immediate deployment of a reliable, cost-effective, and vendor-neutral platform. That's why ITRex, an experienced data migration services provider, proposed switching from the current data ecosystem to Azure Databricks, a mature, cost-effective, and AI-ready data platform.

Solution

ITRex gave the client's data transformation project a second wind by deploying a cloud-based data platform powered by Databricks on Azure Databricks. The new environment centralized critical operational, financial, and maintenance data, allowing for consistent, governed, and scalable analytics throughout the organization. The project deliverables included:

●

Automated data ingestion pipelines, which connected and synchronized information from core systems, including finance, maintenance, GIS, and billing software

●

A unified data lake and transformation layer powered by Databricks Delta Lake to ensure consistent data models and produce clean, analytics-ready datasets

●

Robust data governance that involved implementing role-based access controls and data lineage with Unity Catalog to meet strict regulatory and security standards

●

Power BI dashboards for effective real-time reporting and KPI tracking across the organization

To ensure a seamless transition from the stalled Fabric architecture to the new Azure Databricks environment, ITRex executed a phased migration strategy designed to minimize business disruption and amplify value:

Rapid discovery & assessment. We quickly reviewed the existing architecture and data sources to define the optimal migration path within the client’s Azure ecosystem.

Proof of concept (PoC). The ITRex team deployed a small-scale Databricks environment to validate data ingestion, transformation, and reporting workflows with real data, demonstrating clear performance and cost benefits to stakeholders.

Incremental migration. We used Databricks to systematically reengineer production data pipelines and replace manual processes with automated, dependable workflows powered by Delta Live Tables.

Governance & optimization. New data models, lineage tracking, and access controls were established in Unity Catalog to align with compliance standards and ensure data trust.

Business enablement. Power BI dashboards were reconnected to the new Databricks data lake, giving business users near-real-time insights into asset performance and KPI tracking.

Data-platform-migration-for-a-water-utility-company

Tech

The core technical achievement of this project was the successful re-architecture of data ingestion and transformation pipelines from a Microsoft Fabric design to a more traditional Databricks-native implementation—all without disrupting business operations.

●

Seamless translation of dataflows. The ITRex engineers expertly recreated Fabric's Data Factory integrations and the OneLake structure in Databricks. Using Delta Live Tables and PySpark, we achieved logical parity while significantly improving pipeline reliability and performance.

●

Automated schema harmonization. To resolve inconsistencies across the client’s finance, GIS, and billing systems, the team implemented automated schema detection and mapping scripts in Databricks. This eliminated manual reconciliation errors and guaranteed data consistency for critical KPIs.

●

Robust governance integration. Although the planned Microsoft Purview integration was no longer viable, ITRex implemented comparable data governance capabilities using Unity Catalog, establishing full data lineage, traceability, and secure role-based access controls.

Architecture

●

Cloud platform: Microsoft Azure

●

Core data engine: Databricks (Delta Lake, Delta Live Tables, Unity Catalog)

●

Data processing & transformation: PySpark, SQL

●

Data storage: Azure Data Lake Storage (ADLS Gen2)

●

Orchestration & automation: Azure Data Factory

●

Data visualization & reporting: Power BI

●

CI/CD: GitHub Actions and Azure DevOps

Impact

By migrating their data ecosystem from Microsoft Fabric to Databricks, the client went from a state of operational paralysis to a unified, automated data ecosystem, achieving immediate and measurable business outcomes:

●

Accelerated reporting & insights. Automated pipelines and unified data models allowed the water utility to report more quickly in maintenance, finance, and GIS.

●

Improved operational reliability. With automated, fault-tolerant pipelines, the Databricks platform lowers data latency and gets rid of disruptions caused by dashboard refreshes.

●

Increased cost efficiency. By switching from Microsoft Fabric's fixed licensing fees to Databricks' pay-as-you-go pricing, the client cut down on unnecessary costs for unused infrastructure and matched their cloud spending more closely with their actual data processing needs.

●

Foundation for future innovation. The new Databricks platform unified previously siloed data into a single, analytics-ready environment with standardized models and scalable compute. Its native integration with machine learning frameworks and Azure services positions the client to seamlessly develop and deploy AI solutions for predictive maintenance, demand forecasting, and water-usage optimization.