Architect and govern
Define capacities, domains, workspaces, access boundaries, OneLake patterns, source connectivity, naming, deployment, and cost controls.
Microsoft / Data & AI
Turn scattered data into an AI-ready operating foundation.
Design and implement Microsoft Fabric across OneLake, Data Factory, engineering, warehousing, real-time intelligence, data science, and Power BI.
Your data platform should not make every new use case a new integration project.
We start with business workloads and the current estate, then design the target capacity, workspace, security, data, and delivery model. Migration is phased so teams can modernize without treating every source, pipeline, model, and report as a big-bang cutover.
What we deliver
Engineer and migrate
Build or modernize ingestion, transformation, Lakehouse, Warehouse, notebooks, event-driven analytics, and Azure Data Factory or Synapse workloads.
Activate and operate
Connect governed semantic models and Power BI experiences, establish CI/CD and monitoring, validate performance, and transfer operational ownership.
An illustrative use case
Modernize an Azure Data Factory and warehouse estate in controlled waves.
Assess pipeline compatibility, map linked services to Fabric connections, choose the right Lakehouse or Warehouse pattern, run source-to-target reconciliation, and move production workloads only after parallel validation.
The final design depends on your systems, permissions, licensing, and agreed scope.
A practical starting point
Start with a Fabric readiness and workload assessment.
What you can expect
A Fabric readiness assessment, target architecture, migration waves, configured workspaces and workloads, security model, validation evidence, operating runbooks, and enablement.
Architecture & implementation
Engineer the platform around workloads, not product checkboxes.
A production Fabric implementation needs an explicit capacity, workspace, security, data, deployment, and operating model. Migration decisions must be made item by item.
Tenant, capacity and workspace architecture
We define capacity placement and sizing assumptions, domain and workspace boundaries, administrative roles, network requirements, naming, and lifecycle separation. The design aligns each workload with reliability, security, cost, operational, and performance expectations. Microsoft Fabric overview ↗
OneLake and data architecture
We choose between Lakehouse, Warehouse, SQL database, Mirroring, shortcuts, and ingestion based on ownership, latency, transformation, transaction, and consumption requirements. Medallion zones or domain layers are used only where they clarify accountability and validation—not as decorative architecture.
Data engineering and real-time workloads
Implementation can include Fabric Data Factory pipelines, Dataflow Gen2, notebooks and Spark, Warehouse and T-SQL, Eventstream, Eventhouse, and Real-Time Intelligence. Each pipeline has a source contract, schema-change behavior, retry path, reconciliation check, and operational owner.
ADF and Synapse modernization
We assess Azure Data Factory and Synapse artifacts, categorize compatibility, map linked services to Fabric connections, replace environment parameters appropriately, and plan gateway or network changes. Ready items can move first while exceptions remain operational until redesigned. Microsoft migration planning guidance ↗
Governance, security and delivery
We design Microsoft Entra group-based access, item and workspace roles, sensitivity and endorsement practices, lineage expectations, secrets ownership, Git integration or deployment pipelines, and promotion controls. Negative-access tests are part of acceptance.
Performance and capacity operations
We baseline refresh, Spark, SQL, Direct Lake, and interactive demand; monitor capacity pressure and throttling; and define alert, scaling, and workload-isolation decisions. New Fabric capabilities are reviewed against tenant availability and production status before adoption. Fabric release updates ↗
Frequently asked questions
Clear answers before we begin.
What does a Microsoft Fabric implementation include?
Scope can include tenant and capacity planning, domains and workspaces, OneLake, Data Factory, Lakehouse, Warehouse, notebooks, Real-Time Intelligence, security, governance, Power BI, CI/CD, monitoring, cost controls, and operating handover.
Can SilverSoft migrate Azure Data Factory or Synapse to Fabric?
Yes. SilverSoft can assess pipelines and dependencies, categorize compatibility, map connections and parameters, identify gateway or network changes, redesign unsupported items, and move workloads in validated waves.
How does SilverSoft choose between a Lakehouse and Warehouse?
The choice is based on data shape, engineering and SQL workloads, transformation style, transaction and performance needs, team skills, governance, and downstream consumption. A mixed architecture can be appropriate when ownership remains clear.