Microsoft organisations consolidating analytics tooling
Connect Azure and enterprise data with Fabric engineering, warehousing, real-time, semantic models, Power BI, identity, and governance.
OneLake, lakehouse, warehouse, Data Factory, real-time intelligence, Power BI, semantic models, security, CI/CD, governance, and operations
Microsoft Fabric implementation services
Rokad designs, implements, migrates, governs, and operates Microsoft Fabric data platforms across engineering, warehousing, real-time analytics, and Power BI.
Platform fit / 01
Designed for teams with a specific platform requirement.
Microsoft Fabric provides a SaaS analytics environment spanning OneLake, lakehouse, warehouse, Data Factory, real-time workloads, data science, semantic models, and Power BI. Rokad designs tenant, capacity, workspace, domain, data, identity, pipeline, model, deployment, security, monitoring, cost, and support boundaries around the organisation's operating model.
Power BI teams expanding into a governed data platform
Introduce OneLake, lakehouse or warehouse, Data Factory, Direct Lake, domains, deployment, quality, lineage, and platform operations.
Enterprises migrating data workloads into Fabric
Move pipelines, lakes, warehouses, reports, semantic models, security, history, and operational workflows with continuity.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
Fabric capacity is shared without workload governance
Engineering, warehouse, refresh, report, real-time, data science, and interactive use compete for capacity and service expectations.
Workspaces mirror teams but not data ownership
Domains, environments, lakehouses, warehouses, semantic models, reports, pipelines, permissions, and support boundaries diverge.
Power BI assets and upstream data delivery evolve separately
Model changes, Direct Lake, pipelines, data quality, lineage, deployment, refresh, and report compatibility are not coordinated.
Platform capabilities / 03
What Rokad can implement and operate.
01
Fabric tenant, capacity, domain, workspace, OneLake, item, identity, licence, usage, cost, and governance assessment
02
Lakehouse, Warehouse, SQL endpoints, shortcuts, medallion and domain models, OneLake organisation, and data lifecycle
03
Data Factory pipelines, Dataflow Gen2, connectors, notebooks, Spark, ingestion, orchestration, transformations, retries, and backfills
04
Real-Time Intelligence, event ingestion, streaming, KQL databases, event processing, alerting, and operational analytics
05
Power BI semantic models, Direct Lake, import and DirectQuery decisions, measures, security, reports, apps, and embedded scenarios
06
Microsoft Entra, workspace roles, item permissions, data access, sensitivity, lineage, audit, gateways, private connectivity, and governance
07
Git integration, deployment pipelines, CI/CD, environments, monitoring, capacity, performance, cost, support, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
Fabric tenant and capacity foundation
Tenant settings, capacities, domains, workspaces, identities, roles, gateways, networks, policies, licences, budgets, and support.
OneLake data architecture
Lakehouses, warehouses, shortcuts, files, tables, schemas, SQL endpoints, domains, ownership, retention, quality, and lineage.
Engineering and analytical delivery
Pipelines, dataflows, notebooks, Spark, real-time, transformations, semantic models, Direct Lake, reports, and applications.
Fabric lifecycle operations
Git, deployments, environments, refresh, capacity, quality, access, audit, performance, cost, incidents, and support.
Use cases / 05
Where this platform creates practical leverage.
Enterprise Microsoft Fabric foundation
Establish tenant, capacity, domain, workspace, OneLake, identity, governance, deployment, monitoring, and support controls.
Power BI and data-platform consolidation
Connect upstream ingestion and modelling with semantic layers, Direct Lake, reports, apps, security, lineage, and release workflows.
Fabric lakehouse or warehouse implementation
Build ingestion, storage, transformation, SQL, quality, lineage, semantic, BI, data-science, and operational processes.
Microsoft data-estate migration
Move Azure Data Factory, Synapse, Power BI, lakes, warehouses, pipelines, reports, and models through validated waves where suitable.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Capacity is a shared service with workload ownership
Define placement, scale, priorities, refresh, concurrency, monitoring, chargeback, incident, and exception procedures for each workload class.
Workspace and domain boundaries are designed separately
Use domains for business ownership and discovery, while workspaces carry environment, team, item, deployment, access, and lifecycle boundaries.
Semantic models are production data products
Version measures, relationships, security, metadata, deployment, refresh, Direct Lake behaviour, tests, documentation, and ownership.
Quality and governance / 07
Production controls are part of the implementation.
Governed data boundaries
Catalogues, schemas, workspaces, projects, domains, identity, classification, policy, lineage, audit, and ownership are explicit.
Tested and observable data
Contracts, freshness, completeness, validity, reconciliation, lineage, failures, backfills, and consumer impact are measurable.
Workload and cost isolation
Compute, storage, concurrency, priority, scaling, quotas, budgets, retention, and workload ownership protect performance and economics.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
Fabric tenant, capacity, workspace, data, pipeline, semantic, licence, usage, cost, and risk assessment
Tenant, domain, workspace, OneLake, engineering, warehouse, BI, governance, and operating architecture
Production capacities, workspaces, lakehouses, warehouses, shortcuts, pipelines, dataflows, and notebooks
Semantic models, Direct Lake, reports, apps, real-time, gateways, and enterprise integrations
Git, deployment, testing, lineage, security, monitoring, performance, capacity, and cost controls
Data, BI, developer, administrator, governance, operator, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Snowflake
Cloud data platform for managed storage, compute, pipelines, sharing, governance, and analytics.
02
Databricks
Lakehouse, Unity Catalog, Delta, Lakeflow, SQL, ML, AI, and governed data operations.
01
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
02
Cloud and DevOps
Cloud architecture, delivery automation, observability, security, reliability, and platform operation.
03
AI development
AI applications, agents, retrieval, evaluation, model integration, and intelligent workflows.
FAQ
Microsoft Fabric implementation services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Should Microsoft Fabric use a lakehouse, warehouse, or both?
Should Microsoft Fabric use a lakehouse, warehouse, or both?
The decision depends on data types, SQL needs, engineering, BI, interoperability, transaction and governance expectations, skills, performance, and cost. Combined patterns are common.
02Can Rokad migrate Power BI and Azure data workloads into Fabric?
Can Rokad migrate Power BI and Azure data workloads into Fabric?
Yes. We assess pipelines, gateways, lakes, warehouses, models, measures, reports, security, refresh, capacities, licences, history, and compatibility before migration.
03Can Rokad implement Fabric deployment pipelines and Git integration?
Can Rokad implement Fabric deployment pipelines and Git integration?
Yes. We design workspaces, branches, item support, parameters, dependencies, promotion, validation, permissions, rollback, and release evidence.
04Can Rokad manage Fabric capacities and operations?
Can Rokad manage Fabric capacities and operations?
Yes. Managed scope can cover capacity, pipelines, refresh, quality, models, reports, permissions, gateways, monitoring, performance, cost, incidents, and platform changes.
Microsoft Fabric · Data platform engineering
Make Microsoft Fabric a governed data and BI operating platform, not a collection of workspaces.
Rokad can design capacities and OneLake, build pipelines and models, migrate Power BI and data workloads, and establish lifecycle operations.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.