With a Strong Focus on Consumption: Power BI & Conversational BI with Copilot

Microsoft Fabric introduces a unified analytics platform that brings together data integration, engineering, modeling, and consumption under one SaaS experience.
While Fabric is extremely powerful, it can also feel overwhelming at first — especially for teams coming from classic Power BI or traditional ETL + data warehouse setups.

This article presents a simple, pragmatic Fabric architecture designed to:

• Lower the entry barrier into Fabric
• Avoid unnecessary complexity in early stages
• Put data consumption at the center of the design

The guiding principle is straightforward:

If users cannot easily consume and interact with data, the architecture has failed — no matter how elegant the pipeline looks.

Architecture Overview

The architecture consists of four logical layers:

1. Data Source
2. Preparation (Staging & Loading)
3. Lakehouse
4. Consumption (Semantic Model, Power BI, Copilot)

Each layer has a clear responsibility, minimal overlap, and is designed to evolve independently.

1. Data Source Layer – Keep It Boring (on Purpose)

The data source layer represents operational systems such as:

• Relational databases
• APIs
• Files (CSV, Excel, JSON)
• SaaS applications

In this architecture, no transformation logic lives here.
The goal is not to optimize sources, but to extract data reliably and predictably.

Why this matters:

• Fabric works best when transformations are centralized
• Source systems remain untouched
• Governance and lineage stay clear

2. Preparation Layer – Controlled Entry into FabricThe preparation layer acts as a gentle onboarding zone into Fabric.

Tools used:

• Dataflows Gen2
• Notebooks (Spark)

Responsibilities:

• Initial ingestion
• Light transformations
• Standardization (types, naming, basic cleansing)
• Loading data into the Lakehouse

This layer is intentionally flexible:

• Dataflows are ideal for low-code ingestion and business-friendly pipelines
• Notebooks allow advanced logic when needed (joins, enrichment, Python/SQL logic)

Key principle:

Prepare data just enough to be usable — not perfect.

Heavy business logic is postponed to the semantic layer where it belongs.

3. Lakehouse – The Single Source of TruthThe Lakehouse is the central storage layer of this architecture.

What lives here:

• Delta tables
• Structured, queryable datasets
• Clean but not over-modeled data

Why Lakehouse?

• Native Fabric experience
• Supports Direct Lake
• One copy of data for multiple use cases
• Clear separation between storage and semantics

In this design, the Lakehouse is:

• Stable
• Predictable
• Consumption-ready

It is not overloaded with reporting logic, KPIs, or business rules.

4. Consumption Layer – Where Value Is Created

This is the most important layer of the architecture.

Components:

• Semantic Model
• Power BI
• Copilot (Conversational BI)

Everything upstream exists to serve this layer.

4.1 Semantic Model – The Real Business Layer

The semantic model is where raw data becomes business-understandable data.

Responsibilities:

• Relationships
• Measures (DAX)
• Business logic
• Time intelligence
• KPIs
• Security (RLS / OLS)

Using Direct Lake, the semantic model:

• Reads directly from Lakehouse tables
• Avoids data duplication
• Offers high performance with minimal latency

This is the layer where:

• Finance definitions live
• Metrics are standardized
• “One version of the truth” is enforced

4.2 Power BI – Interactive Analytics

Power BI sits on top of the semantic model and provides:

• Dashboards
• Reports
• Exploratory analysis
• Self-service analytics

Because the model is well-defined:

• Report developers move faster
• Business users trust the numbers
• Changes propagate consistently

This architecture supports:

• Thin reports
• Reusable datasets
• Scalable report development

4.3 Copilot – Conversational BI on Top of Semantics

Copilot represents a new consumption paradigm.

Instead of navigating visuals, users can:

• Ask questions in natural language
• Explore KPIs conversationally
• Get explanations based on the semantic model

Why this only works with a clean semantic model:

• Copilot relies on measures, relationships, and metadata
• Poor modeling leads to poor answers
• Clear naming and definitions become critical

In this architecture:

Copilot does not replace Power BI — it complements it.

Power BI remains the primary analytical interface, while Copilot lowers the barrier for:

• Executives
• Occasional users
• Exploratory questions

Why This Architecture Works Well for Fabric Adoption

1. Low Cognitive Load

• Few moving parts
• Clear responsibilities
• Easy to explain to stakeholders

2. Consumption-First Design

• Architecture decisions are driven by how data is consumed
• Semantic model is treated as a first-class citizen

3. Scalable Without Redesign

• Can start small
• Easily extendable with:
  • More Lakehouses
  • Warehouses
  • Advanced governance
  • CI/CD

4. Future-Proof for AI

• Copilot-ready
• Metadata-driven
• Semantic-first approach

Final Thoughts

Microsoft Fabric enables extremely sophisticated architectures — but not every team needs complexity on day one.

This simple architecture:

• Gets data flowing
• Delivers value quickly
• Prioritizes user interaction
• Builds a solid foundation for Power BI and Copilot

If users can:

• Trust the numbers
• Understand the model
• Ask questions naturally

…then the architecture is doing its job.