Power BI - Questions & Answers | Business Analytics & Intelligence | Processes & Tools | Part 2



Question: Describe the different layers involved in Power BI architecture.

Suggested Answer:


The Power BI architecture consists of multiple layers that work together to transform raw data into actionable insights. A breakdown of the key layers is given below:

1. Data Sources Layer

Purpose: Contains raw data from various sources.

Examples:

  • Databases (SQL Server, Oracle, MySQL)
  • Files (Excel, CSV, JSON)
  • Web APIs & SaaS apps (Salesforce, Google Analytics)
  • Big Data (Azure Synapse, Hadoop)
  • Streaming data (IoT, Kafka)

2. Data Transformation Layer (Power Query)

Purpose: Cleans, transforms, and prepares data for analysis.

Key Tasks:

  • Data extraction (connectors)
  • Filtering, merging, pivoting
  • Handling missing values
  • Query folding (pushing transformations back to the source)

Tools:
  • Power Query Editor in Power BI Desktop

3. Data Modeling Layer

Purpose: Structures data for efficient analysis.

Key Components:

  • Star Schema: Fact tables (metrics) linked to dimension tables (categories)
  • Relationships: Define joins between tables (1:1, 1:many)
  • Calculated Columns & Tables: DAX-based logic
  • Measures: Dynamic aggregations (e.g., SUMX, CALCULATE)
  • Optimizations: Aggregations, partitioning, and data type tuning

4. Data Storage Layer

Purpose: Stores processed data for quick access.

Options:

  • Import Mode: Data loaded into Power BI’s in-memory engine (VertiPaq)
  • DirectQuery: Live queries to the source (no local storage)
  • Composite Model: Hybrid of Import and DirectQuery
  • Power BI Datasets: Reusable models in the Power BI Service

5. Visualization Layer

Purpose: Presents data through interactive reports/dashboards.

Components:

  • Visuals: Charts, tables, maps, custom visuals
  • Filters/Slicers: User-driven data segmentation
  • Bookmarks/Drill-through: Navigation and context switching
  • Tools: Power BI Desktop (authoring) or Service (publishing)



6. Analytics Layer

Purpose: Advanced analysis (AI, forecasting, etc.).

Features:

  • Quick Measures: Prebuilt DAX calculations
  • AI Insights: Anomaly detection, Q&A natural language
  • Python/R Integration: Custom machine learning scripts

7. Power BI Service (Cloud) Layer

Purpose: Hosts and shares reports securely.

Components:

  • Workspaces: Collaboration environments
  • Gateways: On-premises data access (via Data Gateway)
  • Apps: Published report bundles for end-users
  • Power BI Premium: Dedicated cloud capacity for large datasets

8. Security & Governance Layer

Purpose: Controls access and monitors usage.

Features:

  • Row-Level Security (RLS): Filters data by user roles
  • Azure AD Integration: Authentication & SSO
  • Audit Logs: Track report usage in the Power BI Admin Portal
  • Data Sensitivity Labels: GDPR/Compliance tagging

9. Integration Layer

Purpose: Connects Power BI with other tools.

Examples:

  • Power Automate: Trigger workflows from reports
  • Azure Synapse: Big data processing
  • SharePoint/Teams: Embed reports in collaboration tools

10. Consumption Layer

Purpose: End-user interaction with reports.

Access Methods:

  • Power BI Mobile App: On-the-go access
  • Embedded Analytics: Reports in custom apps (via APIs)
  • Subscriptions: Automated email reports

Visual Representation:

Data Sources → Power Query → Data Model → Storage → Visualization → Service → Security → Integration → Consumption

⚠ Key Points to Remember ⚠ 
  • Each layer serves a distinct role in the data pipeline.
  • Optimizing each layer (e.g., query folding in transformations, star schema in modeling) improves performance.
  • Power BI’s flexibility supports both self-service and enterprise-scale analytics.
Location: India

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