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QuickHire

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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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

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.

60%
of data engineering effort spent on pipeline maintenance rather than insight delivery
4x
higher storage cost from redundant data copies across siloed platforms
$2.4M
average annual cost of unplanned data downtime for mid-market enterprises
18mo
average time to value for traditional warehouse modernisation without a unified platform

Why QuickHire

Why Enterprises Choose QuickHire

01

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.

02

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.

03

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.

04

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.

05

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.

06

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

01

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.

02

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.

03

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.

04

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.

05

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.

01
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.
02
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.
03
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.
04
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

Fabric Accelerator

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.

Timeline
6 weeks
Team Size
2-3 engineers
Full Platform 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.

Timeline
16-24 weeks
Team Size
4-8 engineers
Managed Fabric Operations

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.

Timeline
Ongoing
Team Size
2-4 engineers

Capabilities

Technical Capability Matrix

Data Engineering
Lakehouse medallion architectureData Factory pipeline developmentDataflows Gen2Spark notebook engineeringDelta Lake optimisation
Data Warehousing
Fabric Data Warehouse designT-SQL warehouse developmentDimensional modellingCross-warehouse federationQuery performance tuning
Real-Time Intelligence
Eventstream topology designKQL database engineeringReal-Time Hub configurationActivator alert rulesIoT and event stream ingestion
Governance and Security
Microsoft Purview integrationSensitivity label deploymentRow and column securityPrivate endpoint networkingCapacity governance policy

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Discovery and Assessment

Weeks 1-3

We 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.

2

Architecture Design and Review

Weeks 3-5

Our 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.

3

Foundation Build

Weeks 4-7

We 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.

4

Data Migration and Pipeline Engineering

Weeks 6-18

Source 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.

5

Cutover, Hypercare, and Enablement

Weeks 18-24 and ongoing

Production 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

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

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.

✓Fabric Solutions Architect
✓Senior Data Engineer (Spark/Python)
✓SQL Warehouse Engineer
✓Real-Time Intelligence Engineer
✓Power BI Semantic Model Developer
✓Microsoft Purview Governance Specialist
✓DevOps and Platform Engineer
✓Delivery Lead / Programme Manager

Project Lifecycle

From Kickoff to Production

01
2-3 weeks

Discovery

Estate inventory, migration complexity scorecard, capacity sizing model, risk register, and target-state architecture recommendation.

02
2 weeks

Architecture Design

Fabric design document, OneLake topology diagram, workspace and domain structure, security model, governance policy framework, and CI/CD pipeline design.

03
3-4 weeks

Foundation Build

Configured Fabric tenant, provisioned capacities, workspace hierarchy, network security controls, Purview governance baseline, Git integration, and deployment pipeline templates.

04
8-14 weeks

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.

05
Ongoing

Cutover and Hypercare

Production cutover execution, hypercare incident support, capacity optimisation recommendations, operational runbooks, enablement sessions, and knowledge transfer documentation.

Case Studies

Enterprise Outcomes

Financial Services

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.

47%reduction in total data platform cost in year one
Retail

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.

23%reduction in stockout incidents within 90 days of go-live
Healthcare

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.

4xfaster report refresh compared to the previous on-premises SQL Server environment

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Hiring Models

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Frequently Asked Questions

Microsoft Fabric is a unified, end-to-end analytics platform that consolidates data engineering, data science, real-time analytics, data warehousing, and business intelligence into a single SaaS environment backed by OneLake. Azure Synapse Analytics was an earlier generation platform that required separately provisioning and integrating multiple services such as Spark pools, dedicated SQL pools, and linked Power BI workspaces. Fabric eliminates that fragmentation by providing a common compute and storage layer so data teams can collaborate in one place without managing infrastructure. Existing Synapse workspaces can be migrated to Fabric with tooling that preserves pipelines, notebooks, and warehouse schemas.
OneLake is the single, multi-cloud data lake that underpins every Microsoft Fabric workload, similar in concept to how OneDrive provides a unified storage layer for Microsoft 365. All Fabric items - lakehouses, warehouses, KQL databases, and semantic models - write to and read from OneLake using the Delta Parquet format, eliminating redundant data copies across silos. Enterprises benefit because a single authoritative copy of data can be queried by SQL, Spark, Python, and Power BI without ETL pipelines to move data between systems. OneLake also supports shortcuts, which allow Fabric to reference data residing in Azure Data Lake Storage Gen2, Amazon S3, or Google Cloud Storage without physically moving it.
Our engineers routinely migrate workloads from Azure Synapse Analytics, Azure Data Factory, on-premises SQL Server Integration Services, Apache Hadoop clusters (Cloudera, HDP, EMR), legacy Azure HDInsight, Databricks, and traditional on-premises data warehouses such as Teradata, Netezza, and Oracle Exadata. Each migration begins with an automated discovery phase that catalogs existing jobs, schemas, lineage dependencies, and compute usage patterns. We then map each component to the appropriate Fabric experience - Data Factory pipelines for orchestration, Lakehouse notebooks for Spark workloads, Fabric Data Warehouse for SQL-heavy workloads, and Real-Time Intelligence for streaming. Migration timelines vary from eight weeks for mid-size Synapse environments to six months for large-scale Hadoop estates.
Fabric Data Warehouse is a fully serverless, T-SQL-compatible warehouse built directly on OneLake Delta Parquet files, whereas Synapse dedicated SQL pools required pre-provisioning fixed DWU capacity that often sat idle or constrained burst workloads. Fabric Warehouse automatically scales compute without cold-start delays and stores data in open Delta format that other Fabric experiences can read directly, removing the duplication cost of dedicated SQL pool proprietary storage. Cross-database queries between Fabric warehouses are also significantly simpler, using familiar SQL federation without the complexity of linked services. Organisations typically see 30 to 50 percent reductions in total cost of ownership after migrating dedicated SQL pools to Fabric Warehouse.
Real-Time Intelligence in Microsoft Fabric integrates KQL (Kusto Query Language) databases, Eventstream for no-code streaming ingestion, and Real-Time Hub as a centralised catalogue of streaming data sources across the enterprise. It enables sub-second latency analytics on event streams from IoT devices, application logs, financial tick data, and customer clickstreams without requiring separate Azure Event Hubs or Azure Stream Analytics deployments. Fabric Activator (formerly Reflex) allows business users to define triggers that fire alerts, Teams messages, or Power Automate flows the moment a metric breaches a threshold, closing the loop between analytics and operational action. This converged streaming and query experience is particularly valuable for industries such as manufacturing, retail, and financial services that need real-time situational awareness.
Microsoft Fabric inherits Microsoft Purview as its native governance layer, providing data cataloguing, data lineage tracking, sensitivity label enforcement, and compliance reporting across all Fabric workloads without a separate deployment. Row-level security, column-level security, and dynamic data masking policies defined in the semantic model propagate automatically to all downstream Power BI reports, ensuring consistent access control regardless of the report author. Fabric workspaces integrate with Azure Active Directory (Entra ID) for role-based access control, and domain-level governance allows central data offices to enforce policies while delegating day-to-day workspace administration to individual business units. All data at rest in OneLake is encrypted with Microsoft-managed keys by default, with customer-managed key (CMK) support available for organisations with stricter sovereignty requirements.
A Fabric Lakehouse combines the open file format flexibility of a data lake with the ACID-transactional reliability of Delta Lake, making it the right choice when data arrives in semi-structured or unstructured formats, when data science and machine learning workloads need direct file access, or when schema evolution is frequent. The Fabric Data Warehouse, by contrast, is optimised for structured relational workloads where SQL users need familiar DDL, multi-table transactions, and concurrency guarantees similar to traditional RDBMS systems. In practice most enterprise Fabric architectures use both - the Lakehouse for raw and curated zone storage with Spark-based transformation, and the Warehouse as the serving layer for SQL-centric BI and reporting. Our architects design the medallion layer boundaries (Bronze, Silver, Gold) to match your data volume, team skills, and query patterns.
Power BI is natively embedded in Microsoft Fabric as the business intelligence experience, meaning semantic models (formerly Power BI datasets) are first-class Fabric items that live in OneLake alongside lakehouses and warehouses. Existing Power BI Premium workspaces can be upgraded to Fabric-enabled workspaces, which unlocks direct lake mode queries, large semantic model storage, and the ability for Spark notebooks to write directly to semantic model tables using the Public API. BI teams gain the ability to query Fabric Warehouse and Lakehouse tables via DirectLake, a new connection mode that reads Delta files from OneLake at memory speed without the latency of Import mode refreshes or the concurrency limits of DirectQuery. Existing PBIX files, data flows, and paginated reports continue to function unchanged during and after migration.
A standard enterprise Fabric implementation progresses through five phases: discovery and architecture design (weeks 1 to 3), foundation build including tenant configuration, capacity allocation, workspace topology, and network security (weeks 4 to 6), data ingestion and pipeline migration (weeks 7 to 14), semantic model and reporting layer build (weeks 15 to 20), and production cutover with hypercare support (weeks 21 to 24). Timelines compress or extend depending on the complexity of existing estate, number of source systems, data volume, and organisational readiness. We run a parallel validation phase where Fabric outputs are reconciled against legacy system outputs before decommissioning any existing workloads. Post go-live, our managed services team can assume operational responsibility for pipeline monitoring, capacity management, and incremental feature delivery.
Microsoft Fabric is licensed through Fabric Capacity SKUs (F2 through F2048) purchased either as reserved Azure capacity or pay-as-you-go, or through Power BI Premium Per Capacity (P SKUs) that have been Fabric-enabled. The right capacity size depends on concurrent workloads, data volumes, refresh frequency, and the mix of workload types - Spark workloads are more capacity-intensive than SQL queries, and Real-Time Intelligence KQL ingestion has its own capacity consumption profile. Our pre-sales architects run a capacity sizing workshop using your actual workload patterns to recommend a starting SKU with a scaling strategy, avoiding the common mistake of over-provisioning on day one. Fabric also supports capacity bursting and smoothing, which spreads short peaks over a 24-hour window to avoid throttling without requiring a permanent capacity upgrade.
Yes. Fabric Data Factory provides over 150 native connectors covering on-premises SQL Server (via the on-premises data gateway), Oracle, SAP HANA, SAP BW, Salesforce, Snowflake, Google BigQuery, Amazon Redshift, and dozens of SaaS applications. OneLake shortcuts allow Fabric to reference data sitting in Amazon S3 or Google Cloud Storage buckets as if they were native OneLake folders, enabling multi-cloud data federation without physical data movement. For organisations with strict network perimeter requirements, Fabric Private Links and Managed Virtual Networks isolate all data traffic to private endpoints, preventing any data from traversing the public internet. Our integration architects have delivered Fabric implementations for hybrid enterprises running SAP ECC on-premises alongside cloud-native workloads.
Fabric Data Science provides Jupyter-compatible notebooks backed by Spark compute that read and write directly from Lakehouse storage, with built-in MLflow tracking for experiment management, model versioning, and model registry. The Fabric environment management system allows data scientists to define reusable Python and R library environments that attach to Spark sessions, replacing ad-hoc conda or pip installs that create reproducibility problems. Models trained in Fabric can be operationalised using Fabric ML endpoints or published to Azure Machine Learning for more complex inference infrastructure. Fabric also integrates with Microsoft Copilot, which provides AI-assisted code generation within notebooks, helping data scientists who are newer to PySpark or KQL accelerate their productivity.
Fabric provides the Monitoring Hub as a centralised dashboard within each workspace where administrators and pipeline authors can view the run history, duration, and error details of all Data Factory activities, Spark jobs, KQL ingestion tasks, and semantic model refreshes in one place. Capacity-level metrics are surfaced through the Microsoft Fabric Capacity Metrics App, a pre-built Power BI report that tracks CU consumption, throttling events, and workload heat maps over time to support capacity right-sizing decisions. Integration with Azure Monitor allows Fabric diagnostic logs to be routed to a Log Analytics workspace for custom alerting, long-term retention, and cross-service correlation with other Azure workloads. Our engineering teams also deploy automated quality gates in pipelines using dbt or Great Expectations to validate data completeness and accuracy before downstream semantic models are refreshed.
Microsoft Fabric supports Git integration with Azure DevOps Repos and GitHub, enabling workspace items - notebooks, pipelines, lakehouse definitions, and warehouse stored procedures - to be version-controlled and deployed through pull request workflows. Fabric Deployment Pipelines provide a built-in promotion mechanism for moving content through development, test, and production workspaces with configurable rules for connection string substitution and parameter overrides at each stage. For organisations that prefer infrastructure-as-code, the Fabric REST API and Terraform provider allow workspace provisioning, capacity assignment, and item creation to be scripted and embedded in existing IaC workflows. We establish branching strategies, pipeline templates, and automated testing frameworks tailored to your team size and release cadence during the foundation phase of every engagement.
A well-functioning Fabric operations team typically comprises three capability groups: data engineers who manage pipelines, lakehouses, and notebooks using Python and Spark; analytics engineers or data modellers who own semantic models, SQL warehouses, and dimensional design; and platform administrators responsible for capacity management, workspace governance, security policy, and cost optimisation. Organisations coming from Power BI and Azure Synapse backgrounds will find the skill adjacency significant - existing Power BI developers can upskill to semantic model development, and Synapse engineers transition naturally to Fabric Data Engineering and Warehouse. We provide a tailored enablement programme - including hands-on labs, reference architectures, runbooks, and office-hours support - to accelerate team capability in parallel with the implementation so your staff can assume full ownership by go-live.
The most frequently encountered risks are capacity sizing errors that lead to throttling in production, incomplete lineage mapping that causes missed dependencies during cutover, performance regressions in SQL workloads caused by query pattern differences between dedicated SQL pools and Fabric Warehouse, and governance gaps when sensitivity labels from legacy systems are not migrated to Purview. We mitigate capacity risk by running shadow workloads in Fabric under realistic load before cutover, and by establishing autoscale burst policies with budget guardrails. Lineage risk is addressed through automated dependency scanning during discovery that surfaces hidden cross-system dependencies before migration begins. SQL performance regressions are caught in a structured query equivalence testing phase where the top 200 queries by execution frequency are benchmarked against Fabric Warehouse before production traffic is switched.
Industries
Financial ServicesRetail and E-CommerceHealthcareManufacturingTelecommunications