Fabric Without F64: Where Small and Mid-Sized Teams Actually Save Money
Skip the F64 advice. Seven ways small and mid sized teams actually cut Fabric and Power BI cost, starting with the five minute capacity overage check.
Microsoft Solutions Partner · Data and AI
Microsoft Fabric is a new platform, not a Power BI upgrade.
The teams that succeed with it pick patterns that scale. The rest get a surprise capacity bill. Twenty years on the Microsoft data stack tells us which is which.
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Three sessions, one written report, and an honest answer either way.
Fabric workloads we ship
Lakehouse
OneLake-native storage combining files and Delta tables for batch analytics.
Warehouse
Fully managed T-SQL warehouse for relational analytics and reporting.
Notebook
Spark notebooks for data engineering, exploration, and ML workflows.
Pipeline
Orchestrated data movement and transformation across sources and stages.
KQL DB
Real-time analytics over event and telemetry data with sub-second queries.
Eventstream
Streaming ingestion from IoT, Kafka, and SaaS sources into Fabric.
Real-Time Dashboard
Live tiles built directly on KQL for operational monitoring.
Semantic Model
Curated business layer that powers self-service Power BI consistently.
Power BI
Self-service analytics, governed dashboards, and pixel-perfect reporting.
Dataflow Gen2
Power Query at cloud scale, refreshed on schedule into OneLake.
How we structure data
A medallion architecture on OneLake.
Every Fabric platform we ship is layered Bronze → Silver → Gold. Raw data lands once, gets cleansed and conformed in the middle, and surfaces as governed business-ready models. The same pattern works for batch, streaming, and real-time workloads.
Bronze
Raw landing
Source data ingested as-is via Pipelines, Eventstreams, and Dataflows. Schema preserved, history retained.
- Files & Delta tables
- Append-only
- Lineage from source
Silver
Cleansed & conformed
Deduplicated, typed, joined across sources. The trustworthy foundation every downstream model can rely on.
- Notebooks & Pipelines
- Quality & conformance
- Slowly changing dimensions
Gold
Business-ready
Curated semantic models, KQL databases, and warehouses serving Power BI, Real-Time Dashboards, and apps.
- Semantic Models
- Warehouse & KQL DB
- Direct Lake at scale
Built on OneLake. Refreshed by Pipelines and Eventstreams on the cadence each layer needs.
01 — How we help
Three integrated practices: build, optimize, enable.
Most engagements combine all three. We architect the platform, ship the reports, and train your team to own it.
Microsoft Fabric
End-to-end Fabric platforms, from OneLake architecture and Eventhouse ingestion through to governed semantic models and Power BI delivery.
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Power BI
Semantic model design, DAX optimization, and enterprise rollout. Twenty years of analytics experience baked into every report.
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Training
Official Microsoft courses and custom workshops by a Microsoft Certified Trainer. Hands-on labs and Microsoft Learn recognition included.
→02 — Our difference
Why teams choose DataMartIn.
→ 01
Verified Microsoft expertise.
Solutions Partner for Data and AI, Learning Partner, and Microsoft Certified Trainer credentials. The official path, not a workaround.
→ 02
Twenty years of focus.
We’ve worked through every iteration of the Microsoft data stack, from SQL Server Analysis Services to Fabric. We know what lasts and what doesn’t.
→ 03
Delivery patterns, not slideware.
Our methodologies are published openly through our LinkedIn series. You see how we work before you hire us.
→ 04
Toronto-based, North America-wide.
Local presence for in-person workshops, remote delivery for clients across the continent.
03 — From the blog
Latest delivery patterns.
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6 signs you’re outgrowing Power BI
Most companies never outgrow Power BI. Some do, and it happens quietly. Here are six signs you have crossed the line, and what to do next.
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Do you actually need Microsoft Fabric, or is Power BI enough?
When Power BI is genuinely enough, when Microsoft Fabric is worth starting early, and what a capacity really costs. A practitioner’s decision framework.
04 — Get in touch
Let’s build something with your data.
Tell us about your project, your team, and your timeline. We’ll respond within two business days.