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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 significant 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. As an experienced data migration services provider, ITRex proposed switching from the existing data ecosystem to Azure Databricks—a mature, cost-effective, and AI-ready platform with a track record in enterprise data engineering.

Solution

ITRex revived the client's data transformation project 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:

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

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

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Data governance built on role-based access controls and data lineage tracking via Unity Catalog, meeting the client's regulatory and security requirements

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

To seamlessly transition from the stalled Fabric architecture to Azure Databricks while keeping the business running, ITRex implemented a phased migration strategy:

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 establish data trust across teams.

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 here was re-architecting data ingestion and transformation pipelines from a Microsoft Fabric design to a Databricks-native implementation—without disrupting business operations.

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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.

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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.

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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

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Cloud platform: Microsoft Azure

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

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Data processing & transformation: PySpark, SQL

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Data storage: Azure Data Lake Storage (ADLS Gen2)

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Orchestration & automation: Azure Data Factory

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Data visualization & reporting: Power BI

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CI/CD: GitHub Actions and Azure DevOps

Impact

The move from a fragmented, stalled environment to a unified Azure Databricks platform produced immediate and measurable gains across operations, costs, and long-term infrastructure management:

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A 60% reduction in data processing and reporting time collapsed the gap between data availability and decision-making across maintenance, finance, and GIS—turning what were once day-long waits for KPI updates into near-real-time visibility.

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A 20% reduction in administrative overhead and labor costs tied to data preparation, translating to an estimated $1.5 million in annual savings. Eliminating data silos, automating ingestion pipelines, and harmonizing schemas across finance, GIS, and billing systems removed the manual reconciliation work that had been absorbing staff time.

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20–30% in annual data infrastructure cost savings driven by the shift from Microsoft Fabric's fixed licensing model to Databricks' consumption-based pricing. The client eliminated overprovisioning, matched cloud spending to actual usage, and removed the vendor lock-in risk that had stalled the original project.

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A 15–25% reduction in unplanned asset downtime and a 10–20% extension of infrastructure lifecycle, made possible by predictive maintenance models deployed after siloed data was consolidated into a governed, ML-ready lakehouse. For a capital-intensive utility operating aging water infrastructure, both figures carry significant financial weight.