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PRACTICE 04

Data Engineering & Modern Data Platforms.

End-to-end implementation of Microsoft Fabric as the organisation's unified data and analytics platform.

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41SERVICES IN THIS PRACTICE

3SPECIALIST SUB-PRACTICES

Milestone based, or time and materials for evolving scopeDELIVERY MODEL

The problems this practice exists to solve.

Data fragmented across systems with no single trusted source

Reporting that disagrees depending on where the number came from

Legacy platforms nearing end of support

Platform cost rising without a corresponding increase in value

Every service in this practice.

41 SERVICES · 1 NEW FOR 2026-27

SUB-PRACTICE 4.1Microsoft Fabric13 services

Microsoft Fabric ImplementationEnd-to-end implementation of Microsoft Fabric as the organisation's unified data and analytics platform.Fabric Lakehouse & Data EngineeringDesign and build of lakehouse architecture, medallion layers and transformation pipelines within Fabric.Fabric Data WarehouseImplementation of the Fabric warehouse for structured analytical workloads and downstream reporting.Fabric Real-Time IntelligenceStreaming ingestion, event processing and real-time analytics for operational decision making.Fabric Power BI & Semantic ModelsDesign of semantic models, measures and reporting layers that produce consistent numbers across the organisation.Fabric Data Activator & GovernanceEvent-driven alerting and action on data conditions, with the governance controls around it.OneLake ImplementationDesign and implementation of OneLake as the single logical data lake, including domains, shortcuts and access model.Microsoft Fabric MigrationMigration from Synapse, on-premises warehouses or legacy platforms onto Fabric with parallel run and cutover.Fabric SecurityWorkspace, item and row-level security design, identity integration and data protection within Fabric.Fabric Capacity PlanningSizing, allocation and monitoring of Fabric capacity against workload profile and budget.Fabric Cost OptimisationReduction of Fabric running cost through capacity tuning, workload scheduling and query optimisation.Fabric DevOpsSource control, deployment pipelines, environment separation and release management for Fabric artefacts.Microsoft Purview IntegrationConnection of Fabric and the wider estate to Microsoft Purview for cataloguing, lineage and classification.

SUB-PRACTICE 4.2Data Engineering & Integration18 services

Azure Data FactoryDesign and build of orchestration and data movement pipelines using Azure Data Factory.Data Pipeline ArchitectureArchitecture for ingestion, transformation, orchestration and observability across batch and streaming workloads.ETL & ELT ServicesDevelopment of extract, transform and load processes with testing, error handling and reprocessing capability.Data Integration SolutionsIntegration of data across applications, databases, files and external providers into a governed platform.API & System IntegrationDevelopment and integration of application programming interfaces between enterprise systems.Real-Time Data ProcessingLow-latency processing of event streams for operational analytics and immediate action.Batch Data ProcessingScheduled, high-volume processing with dependency management, restartability and reconciliation.Streaming Data PlatformsImplementation of event streaming infrastructure and the patterns that make it maintainable.ERP Data IntegrationExtraction and integration of data from enterprise resource planning systems into the analytics and AI estate.CRM Data IntegrationExtraction and integration of customer relationship management data with consistent customer identity.Finance-System IntegrationIntegration of general ledger, sub-ledger, billing and treasury systems into a reconciled financial data layer.Data MigrationPlanned migration of data between platforms with profiling, mapping, validation, reconciliation and cutover.Data ObservabilityMonitoring of freshness, volume, schema, distribution and lineage so data issues are detected before users find them.Pipeline MonitoringOperational monitoring and alerting for pipeline health, latency and failure.DataOpsEngineering practice covering version control, automated testing, continuous delivery and environment management for data.Integration TestingSystematic testing of end-to-end data flows including reconciliation to source and regression protection.Data Platform BuildDesign and construction of a complete enterprise data platform from landing zone to consumption layer.DevOps for DataAutomated build, test and release pipelines for data platform code, configuration and infrastructure.

SUB-PRACTICE 4.3Data Modelling & Warehousing10 services

Data Architecture DesignTarget-state design for how data is stored, structured, moved and consumed across the organisation.Dimensional ModellingDesign of facts, dimensions and hierarchies that make analytical data intuitive and performant.Data Warehouse SolutionsDesign and build of an enterprise data warehouse supporting reporting, analytics and regulatory needs.Conceptual Data ModellingBusiness-level model of key entities and relationships, agreed with stakeholders before physical design.Logical Data ModellingPlatform-independent model defining attributes, keys, relationships and business rules.Physical Data ModellingImplementation-level model covering storage, partitioning, indexing and performance design.Semantic Data ModellingThe business-facing layer that defines measures, hierarchies and calculations so every report agrees.Star-Schema DevelopmentConstruction of star and snowflake schemas optimised for analytical query patterns.Warehouse ModernisationMigration and re-engineering of a legacy warehouse onto a modern cloud platform.AI-Ready Data PreparationNEWPreparation of enterprise data for AI use, covering cleansing, enrichment, unstructured content processing, chunking and vectorisation.

What we deliver.

Target-state architecture and workspace design

Implemented lakehouse, warehouse and semantic layers

Ingestion and transformation pipelines with testing

Security, capacity and cost configuration

Deployment pipelines and environment separation

Documentation, enablement and handover

The outcomes that follow.

A single governed platform serving analytics and AI

Consistent numbers across the organisation

Reduced platform and licensing cost

A foundation on which AI can be deployed safely

Delivery practice the client's team can sustain

How every engagement runs.

01Discoverconfirm objectives, stakeholders, constraints and success measures

02Assessreview current systems, data, controls and delivery readiness

03Designprepare the target design, backlog, governance and implementation plan

04Validatetest the priority requirements through a prototype, pilot or controlled design review

05Implementconfigure, integrate, test and deploy the approved solution

06Enabletrain users, transfer knowledge and establish operating procedures

07Optimisemonitor adoption, performance, cost, quality and improvement opportunities

BUILT FORBanks, insurers, superannuation funds and professional-services firmsConstruction, engineering and infrastructure contractorsGovernment agencies and regulated public-sector bodiesUtilities, energy and resources operators

What our customers say.

All customer stories

"Reconciliation that took our team nine days now closes in three — with a full audit trail on every match. It changed how the board sees AI.

Finance ManagerGCC Construction Group, Dubai

"Site teams stopped digging through folders. They ask the assistant, they get the clause with a citation, and they move on.

Project DirectorTier-One Contractor, Sydney

"The anomaly models flag outliers the week they appear, not at quarter-end. We stopped two overruns before they hit the P&L.

Commercial DirectorProperty Developer, Dubai

"Seven subsidiaries of spreadsheets became one governed reporting platform. The executive finally trusts the numbers on the screen.

Group CFOInfrastructure Holding, Riyadh

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