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Introduction to Power BI

Developed by Microsoft, Power BI quickly established itself on the Business Intelligence market despite arriving late, thanks to a winning bet: making Power BI Desktop free on Windows. This decision allowed thousands of users to discover the tool, build their first reports, and develop a taste for data analysis.

Its massive adoption is mainly due to Power BI’s ability to combine simplicity and power. At the heart of this success is Power Query, the preparation and transformation tool. With its Excel-like interface, it enables users to easily perform common operations (create calculated columns, transform existing ones, format data, merge datasets...) while also offering advanced capabilities through the M language for complex transformations.

Today, Microsoft has gone further with Microsoft Fabric, a platform where Power BI is no longer just a visualization tool but a gateway to a complete data ecosystem: ingestion, storage, pipelines, governance, and AI. The message is clear: the value is no longer only in the chart, but in the entire data pipeline that powers it.

The Three Pillars of Power BI​

The Power BI ecosystem rests on three complementary pillars:

  • Power BI Desktop: the free, local application and true starting point. This is where users connect to data, apply transformations with Power Query, and build visualizations using DAX expressions. Most reports are created here before being shared.

  • Power BI Service: the cloud platform designed for sharing and collaboration. It transforms an individual report into a collective tool. Users can publish reports, create dashboards combining visuals from different reports, and organize multiple reports and/or dashboards into thematic apps accessible to teams.

  • Microsoft Fabric: the newest and now central pillar. Fabric integrates Power BI Service into a complete data platform. It goes beyond BI to offer real-time ingestion, pipelines, lakehouse, governance, and automation. Power BI thus becomes the analytical and visualization layer of this broader ecosystem.

From Data to Insight​

Power BI stands out because it does more than just “show” data. It orchestrates an entire logical process:

  1. Connect & prepare – Power Query includes a wide range of connectors for data extraction and provides many functions for cleaning and transforming information. Whether through the low-code interface or by writing in M language, this is where raw data becomes usable.
  2. Model – structuring comes next. Data is organized into a star schema: a central fact table (transactional data) linked to dimension tables (contextual data such as time, products, or customers). This design lightens calculations and improves responsiveness for end users exploring the data.
  3. Visualize – once prepared and modeled, data comes to life in reports and interactive dashboards. Users can filter, explore, compare, and understand with just a few clicks.
  4. Deploy & govern – finally, through Power BI Service and now Fabric, reports become shared and secured assets. Pipelines ensure automation, and governance guarantees that everyone accesses reliable data.

This continuity — from raw data to collective insight — is what makes Power BI unique.

Storage Modes and Optimization​

Power BI provides different storage modes to suit various needs, especially as data volumes grow:

  • Import: data is loaded in memory and compressed by VertiPaq, delivering high performance for medium-sized datasets.
  • DirectQuery: queries run directly on the source, useful for large databases but dependent on source performance.
  • Composite models: a mix of both, balancing speed and freshness.

Efficiency relies on optimization, and the role of the data architect is key: keeping only relevant information, minimizing joins, and storing data in an efficient format.

The Central Role of DAX​

Beyond Power Query, another powerful feature of Power BI is DAX (Data Analysis Expressions). This language is applied within Power BI itself (not in Power Query) and allows users to:

  • create calculated columns row by row,
  • define dynamic measures (revenue, margin, year-to-date growth),
  • build calculated tables for targeted analysis.

With hundreds of functions, DAX covers everything from simple aggregations to advanced time intelligence and comparative analysis. It is important to note that, for efficiency, most data transformations should be handled in Power Query; DAX is mainly used for defining measures (KPIs) to be analyzed.

Collaboration and Sharing​

A report only reveals its full value when it circulates. That’s where Power BI Service (now integrated into Fabric) comes into play:

  • Interactive reports for detailed exploration,
  • Dashboards that synthesize visuals from multiple reports,
  • Apps that organize content by team or department.

Sharing goes hand in hand with governance: role-based access, secure gateways, and automated refreshes. This ensures that insights are not isolated but become a shared language within the organization.