End-to-end Microsoft Fabric consulting: capacity planning, OneLake architecture, Lakehouse and Warehouse design, Data Factory pipelines, and Direct Lake semantic models, implemented with governance from day one.
The same disciplined framework behind our enterprise migrations: documented, validated, and led by senior expertise from start to finish.
Inventory every report, data source, and dependency. Usage analysis identifies what actually matters.
Retire duplicates and unused reports. Typically 30 to 50 percent never need migrating.
Rebuild on Power BI and Fabric with governed semantic models and validated outputs.
Workspaces, row-level security, certified datasets, and documented standards.
Training and handover so your team owns the platform, not a vendor.
A free 30-minute discovery call: bring your questions about microsoft fabric consulting, leave with a clear next step and an honest read on effort and cost.
Power BI is now part of Fabric. The question is when to adopt the data-platform side: OneLake, Lakehouse, and pipelines. We assess your current architecture and give a sequenced adoption plan rather than a big bang.
Through capacity SKUs (F2 and up) plus Power BI Pro licenses depending on capacity size. We model your workloads against SKU options so you buy the capacity you need, not the one the calculator guessed.
Frequently yes. Lakehouse and Warehouse on OneLake cover most warehouse workloads, and we run migrations from SQL marts, Synapse, and legacy platforms as phased, validated projects.
Either advisory (architecture, review, roadmap) or delivery (we build it). Most clients start with a 2 to 4 week architecture engagement that produces a costed implementation plan.
Tell us what you are working with. A senior specialist will respond within one business day.
Prolytix is a Vancouver, BC–based Microsoft Fabric and Power BI consultancy that helps organizations design, size, and implement Microsoft Fabric—from capacity planning and lakehouse architecture to Power BI on Fabric for clients across Canada, the US, and the UK.
Microsoft Fabric unifies data engineering, warehousing, real-time analytics and Power BI on one SaaS foundation — OneLake — under a single capacity license. That simplicity is real, but so is the design work: capacity sizing, lakehouse architecture and governance decisions made in week one echo for years. Our engagements focus on getting those foundations right.
| Fabric component | What it replaces / does |
|---|---|
| OneLake | Single tenant-wide data lake; one copy of data shared across all engines |
| Lakehouse + notebooks | Spark-based engineering — the Synapse/Databricks-style workload |
| Warehouse | T-SQL analytics warehouse over the same OneLake data |
| Data Factory pipelines / Dataflows Gen2 | Ingestion and orchestration, successor to ADF and classic dataflows |
| Direct Lake semantic models | Power BI reads Delta tables directly — import-mode speed without refresh windows |
| F-SKU capacity | One pooled compute license replacing Premium P-SKUs (retired 2024/25) |
Fabric readiness and capacity sizing — workload analysis, F-SKU selection (F2 through F64+), cost modelling against your current Premium or Pro spend. Legacy BI to Fabric — Crystal, SSRS or Cognos estates landed on a medallion lakehouse (bronze/silver/gold) with Direct Lake models on top. Premium-to-Fabric transition — migrating P-SKU workspaces, right-sizing, and enabling Fabric workloads safely. Governance — domains, workspace topology, deployment pipelines, Purview integration and capacity monitoring.
Do we need Fabric to use Power BI? No — Pro licensing still works for classic import models. Fabric matters when you need paginated reports at scale, Direct Lake on large data, engineering workloads, or you are exiting retired Premium P-SKUs.
Fabric vs Power BI Premium — what changed? Premium per-capacity (P-SKU) has been retired in favour of Fabric F-SKUs. Same Power BI engine underneath, but the capacity now also runs lakehouses, warehouses and pipelines, and can be paused or scaled hourly on Azure billing.
What size capacity do we need? Most mid-market analytics estates start comfortably on F8–F16; heavy Direct Lake models and Spark jobs push toward F32+. We benchmark with your real workloads during a pilot rather than guessing from user counts.
Can we adopt Fabric gradually? Yes — a single F2 capacity alongside existing Pro workspaces is a common on-ramp: land one dataset in a lakehouse, prove Direct Lake, then migrate by domain.
Is OneLake another data silo? The opposite — it is ADLS Gen2 under the hood, supports shortcuts to existing lakes (ADLS, S3, Dataverse), and every Fabric engine reads the same Delta tables without copies.