Enterprise Data Platform Consulting
Microsoft Fabric Implementation Services for Enterprise Data Teams
We architect, migrate, and operationalise Microsoft Fabric - the unified analytics platform that consolidates OneLake, Data Factory, Synapse Data Engineering, Data Science, Real-Time Intelligence, and Power BI into a single governed environment. From legacy Synapse and Hadoop migrations to greenfield lakehouse builds, our engineers deliver production-ready Fabric estates at enterprise scale.
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The Challenge
Fragmented data infrastructure blocks analytical velocity and inflates operational cost
Enterprises maintaining separate data warehouses, data lakes, ETL pipelines, streaming platforms, and BI environments face compounding integration overhead, duplicated data storage costs, and governance blind spots that span siloed tooling. Data engineers spend more time moving data between systems than building insights, while business users tolerate stale reports because refresh cycles cannot keep pace with operational demand.
Why QuickHire
Why Enterprises Choose QuickHire
Certified Fabric Architects
Our team holds Microsoft Certified: Azure Data Engineer Associate and Fabric Analytics Engineer credentials, with direct access to Microsoft product engineering for pre-GA feature guidance. We have delivered Fabric implementations across financial services, retail, manufacturing, and healthcare verticals.
Proven Migration Methodology
Our structured migration playbook covers automated discovery, dependency mapping, parallel validation, and phased cutover for Synapse, Hadoop, Databricks, and on-premises warehouse environments. We have completed over 40 enterprise data platform migrations with zero unplanned production outages.
Architecture-First Approach
Every engagement begins with a current-state assessment and target-state architecture design reviewed against the Microsoft Well-Architected Framework for Analytics. We document capacity models, workspace topology, medallion layer design, and governance policy before writing a single pipeline.
Security and Compliance Depth
We implement Microsoft Purview data governance, sensitivity label propagation, private endpoint networking, customer-managed encryption keys, and audit logging as first-class deliverables, not afterthoughts. Our team has delivered Fabric environments compliant with HIPAA, PCI-DSS, SOC 2, and GDPR requirements.
End-to-End Delivery
We cover the full Fabric stack from OneLake architecture and Data Factory pipeline engineering through Lakehouse medallion layers, semantic model development, and Power BI report migration. Clients receive a production-ready platform, not a prototype that requires further build by internal teams.
Structured Knowledge Transfer
Parallel enablement runs throughout every engagement with hands-on labs, recorded walkthroughs, runbooks, and office-hours sessions so your internal team is operationally self-sufficient at go-live. We build capability, not dependency.
Challenges
Common Enterprise Pain Points
Legacy Synapse and Hadoop Estates Are Expensive to Maintain
Organisations running Azure Synapse dedicated SQL pools, Azure HDInsight clusters, or on-premises Hadoop environments face escalating infrastructure costs, shrinking vendor support windows, and a narrowing talent pool of engineers fluent in legacy stack technologies. Migrating to Fabric requires careful dependency mapping and parallel validation to avoid disrupting downstream consumers during cutover.
OneLake Architecture Decisions Have Long-Term Consequences
Workspace topology, domain structure, shortcut strategy, and medallion layer design choices made during initial Fabric deployment are difficult and costly to reverse once production pipelines and semantic models are built on top of them. Enterprises frequently underestimate the governance and cost implications of incorrect capacity tier selection or overly permissive workspace sharing policies.
Real-Time Data Requirements Exceed Batch Platform Capabilities
Business demand for sub-minute latency analytics on IoT telemetry, financial transactions, and customer events is outpacing what scheduled batch pipelines can deliver, but retrofitting streaming capabilities into batch-oriented architectures introduces significant complexity. Fabric Real-Time Intelligence requires careful Eventstream topology design and KQL database partitioning to achieve the latency and throughput targets that operational use cases demand.
Power BI DirectLake Requires Semantic Model Redesign
Migrating existing Import-mode Power BI datasets to DirectLake mode to take advantage of Fabric performance improvements is not a lift-and-shift operation - it requires restructuring semantic models to comply with DirectLake constraints around calculated columns, unsupported DAX patterns, and relationship cardinality. Teams that skip this redesign work typically discover performance and feature parity gaps only after go-live.
Capacity Management Is a Continuous Operational Discipline
Fabric capacity consumption is driven by concurrent workloads, data volumes, and query complexity in ways that are difficult to predict without production telemetry, and under-provisioned capacity leads to throttling that degrades pipeline SLAs and report load times for all workspace users sharing that capacity. Organisations without a defined capacity governance process and automated alerting routinely overspend or experience unexpected service degradation within the first quarter of production operation.
Our Approach
A unified Fabric platform engineered for your data estate - fully governed, production-ready, and built to scale
We design and implement Microsoft Fabric environments that consolidate your data engineering, science, warehousing, streaming, and BI workloads onto a single governed platform backed by OneLake. Our delivery model combines architectural rigour, automated migration tooling, and structured knowledge transfer to ensure your organisation achieves measurable time-to-insight improvements while reducing the operational complexity and cost of your data estate.
OneLake Foundation Design
We architect your OneLake workspace topology, domain structure, shortcut strategy, and Delta Lake medallion layers (Bronze, Silver, Gold) to support current workloads and accommodate future growth without costly restructuring.
Data Factory and Pipeline Engineering
Our engineers build and migrate Data Factory pipelines - including Dataflows Gen2, Copy Activities, and notebook orchestration - with parameterised templates, error handling frameworks, and automated data quality validation gates.
Warehouse and Lakehouse Build
We implement Fabric Data Warehouse for SQL-centric workloads and Fabric Lakehouse for Spark and ML workloads, with cross-experience query federation, optimised Delta table layouts, and semantic model integration.
Real-Time Intelligence Implementation
Our streaming engineers deploy Eventstream topologies, KQL database schemas, and Activator alert rules for operational use cases requiring sub-minute data freshness from IoT, transactional, and event-driven sources.
Delivery Models
How We Deliver
Rapid foundation build covering tenant configuration, capacity allocation, workspace topology, governance baseline, and a reference pipeline pattern validated in development and staging environments. Designed for organisations that want to establish a Fabric footprint quickly before committing to full migration.
End-to-end migration from legacy platforms (Synapse, Hadoop, Databricks, or on-premises warehouse) to production Fabric, including discovery, architecture design, pipeline migration, semantic model rebuild, parallel validation, cutover, and hypercare. Covers data engineering, warehousing, and BI layers.
Ongoing operational management of your Fabric environment post go-live, covering pipeline monitoring, incident response, capacity optimisation, feature delivery sprints, and quarterly architecture reviews. Delivered as a dedicated remote engineering team embedded with your stakeholders.
Capabilities
Technical Capability Matrix
Engagement Models
How We Engage
Choose the model that fits your programme governance, budget cycle, and team structure.
Our Process
From Discovery to Delivery
Discovery and Assessment
Weeks 1-3We inventory your existing data estate using automated scanning tools that catalogue pipelines, schemas, lineage dependencies, compute usage, and data volumes across all source systems. Output is a detailed migration complexity scorecard and a target-state architecture recommendation.
Architecture Design and Review
Weeks 3-5Our architects produce a Fabric design document covering OneLake topology, capacity model, workspace structure, medallion layer boundaries, security model, and governance policy. This document is reviewed with your architecture board and Microsoft account team before build begins.
Foundation Build
Weeks 4-7We configure the Fabric tenant, provision capacities, establish workspace hierarchy, implement network security controls, deploy Purview governance, and build the CI/CD pipeline integration using your version control system. Reference architecture patterns are validated in a sandbox environment.
Data Migration and Pipeline Engineering
Weeks 6-18Source system connectors, ingestion pipelines, transformation notebooks, and SQL warehouse objects are built and migrated in priority order, with automated reconciliation tests comparing Fabric outputs to legacy system outputs before each workload is promoted to staging.
Cutover, Hypercare, and Enablement
Weeks 18-24 and ongoingProduction cutover follows a rehearsed runbook with clearly defined rollback criteria. We provide two weeks of hypercare support with extended engineering availability before transitioning to steady-state support. Enablement sessions, runbooks, and documentation are delivered throughout.
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Security & Compliance
Enterprise-Grade Security by Default
Governance
Programme Governance
Microsoft Purview Data Governance
We deploy Purview as the native governance layer for Fabric, configuring data catalogue scanning, automated lineage capture, sensitivity label classification, and compliance reporting dashboards for data stewards and privacy officers.
Workspace and Domain Governance
Fabric domain structure, workspace access policies, and item sharing rules are designed to balance central data office oversight with domain team autonomy, following the data mesh-aligned delegation model that Fabric domains support natively.
Capacity Cost Governance
We deploy the Microsoft Fabric Capacity Metrics App with custom alerting thresholds, configure smoothing and bursting policies, and establish a monthly capacity review cadence with documented escalation paths for capacity upgrade or right-sizing decisions.
Data Quality and Pipeline SLA Monitoring
Automated data quality rules using Great Expectations or dbt tests are embedded into ingestion and transformation pipelines, with failures routed to the Monitoring Hub and an alerting channel so issues are caught before downstream semantic models are refreshed.
Team Structure
Your Enterprise Team
Our Microsoft Fabric delivery teams are structured to cover every layer of the platform, from OneLake architecture through streaming pipelines, SQL warehousing, and Power BI semantic models. Senior architects provide design oversight and client advisory, while specialist engineers deliver each workload layer. Every engagement includes a named delivery lead who coordinates across workstreams and maintains alignment with your internal stakeholders and Microsoft account team.
Project Lifecycle
From Kickoff to Production
Discovery
Estate inventory, migration complexity scorecard, capacity sizing model, risk register, and target-state architecture recommendation.
Architecture Design
Fabric design document, OneLake topology diagram, workspace and domain structure, security model, governance policy framework, and CI/CD pipeline design.
Foundation Build
Configured Fabric tenant, provisioned capacities, workspace hierarchy, network security controls, Purview governance baseline, Git integration, and deployment pipeline templates.
Data Migration and Engineering
Migrated ingestion pipelines, Lakehouse medallion layers, Fabric Data Warehouse objects, semantic models, Power BI report migration, streaming pipelines, and automated reconciliation test suite.
Cutover and Hypercare
Production cutover execution, hypercare incident support, capacity optimisation recommendations, operational runbooks, enablement sessions, and knowledge transfer documentation.
Case Studies
Enterprise Outcomes
A regional bank running Teradata on-premises needed to migrate 12TB of financial data and 400 SQL procedures to a cloud-native warehouse.
We migrated the Teradata estate to Fabric Data Warehouse over 20 weeks, using automated SQL translation tooling and a parallel validation framework that reconciled 100 percent of output rows before cutover.
A national retailer with 800 stores needed sub-minute inventory and sales visibility to reduce stockout events across their distribution network.
We deployed a Fabric Real-Time Intelligence solution ingesting POS and RFID event streams via Eventstream, with KQL dashboards and Activator alerts notifying store managers of low-stock conditions in under 30 seconds.
A hospital network needed to consolidate patient data from six legacy EMR systems into a unified analytics environment compliant with HIPAA.
We built a Fabric Lakehouse with Purview sensitivity label enforcement, private endpoint networking, and row-level security policies that restricted patient-level data access to authorised clinical roles, with a Power BI reporting layer for population health analytics.
FAQ
Frequently Asked Questions
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