# Getting Data from Different Sources in Power BI

Kelvin Isioma Adigwu — Mon, 30 Mar 2026 11:42:02 +0000

Executive Introduction
Key Objectives
Power BI Data Architecture
Connecting to Data Sources
Data Quality Best Practices
Conclusion

Executive Introduction

The foundation of every successful Power BI report is reliable data ingestion. Regardless of how visually compelling a dashboard may be, insights are only as strong as the quality, completeness, and integrity of the underlying data.

In modern business environments, data rarely resides in a single location. Organizations operate across diverse systems—spreadsheets, databases, cloud platforms, APIs, and document repositories. Power BI is designed to seamlessly integrate these environments through its powerful data connectivity framework and Power Query transformation engine.

Key Objectives

Connect Power BI to multiple data sources efficiently.
Explore data structures using Power Query.
Detect and resolve data quality issues early.
Establish a scalable foundation for accurate reporting.

Power BI Data Architecture Overview

At a high level, the Power BI data architecture consists of three major layers:

Data Sources: Excel, SQL Server, MySQL, JSON APIs, SharePoint, etc.
Power Query (ETL Layer): Extraction, transformation, cleansing, and validation.
Data Model & Reporting Layer: Relationships, DAX calculations, and visuals.

All data flows through Power Query before entering the data model to ensure consistency and quality control.

Connecting to Multiple Data Sources

Most connectors follow a similar workflow. Click below to see the specific steps for each source:

File-Based Sources

Excel, CSV, or PDF

Navigate to Home → Get Data.
Select the specific file type (Excel, Text/CSV, or PDF).
Authenticate/Select the file.
Preview data in the Navigator window.
Choose Transform Data.

Databases

SQL Server or MySQL

Navigate to Home → Get Data.
Select the database engine.
Enter Server/Database credentials.
Choose between Import or DirectQuery.
Select tables and click Transform Data.

Cloud & API Sources

SharePoint, JSON APIs, or Azure

Navigate to Home → Get Data.
Select the appropriate Web or Cloud connector.
Paste the URL or API endpoint.
Preview the schema and click Transform Data.

Data Quality and Preparation Best Practices

Review Data Types: Check column types immediately after loading.
Handle Nulls: Check for missing values that could skew averages.
Standardize: Ensure date formats and naming conventions are consistent.
Validate: Match row counts against your source systems.

Architecture Diagram Overview

The Visual Flow

Data Sources (Left) → Power Query ETL (Middle) → Data Model & Reports (Right)

(Note: If you have an image for this, use the syntax below:)

Conclusion

Connecting to multiple data sources is the strategic foundation of reliable analytics. Effective data ingestion enables accurate insights and confident decision-making. Great analytics begins with great data—and great data begins with disciplined ingestion and transformation

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