DEV Community

Cover image for End-to-End Data Ingestion in Power BI: Connecting and Preparing Data from Multiple Sources
Chiemerie Aneke
Chiemerie Aneke

Posted on

End-to-End Data Ingestion in Power BI: Connecting and Preparing Data from Multiple Sources

#analytics #analyst #datascience #techtalks

Introduction

In modern data analytics, the quality of your insights is only as good as the data behind them. Before any visualization or reporting happens, the most critical step is data ingestion,the process of collecting data from multiple sources and preparing it for analysis.

In real world scenarios, data rarely exists in one place. As a Data Analyst, you are often required to work with heterogeneous data sources such as Excel files, CSVs, SQL databases, APIs, PDFs, and cloud platforms like SharePoint or Azure. Managing these diverse sources efficiently is a core analytical skill.

This is where Power BI Desktop becomes a powerful tool. With its Get Data feature and Power Query Editor, Power BI enables seamless connection, transformation, and preparation of data from multiple environments into a unified model.

In this guide, you will learn how to:

  • Connect Power BI to multiple data sources
  • Use Power Query to explore and prepare datasets
  • Identify and resolve data quality issues early
  • Build a reliable foundation for data modeling and reporting

Architecture Overview

At a high level, the data ingestion architecture in Power BI consists of:

  • Power BI Desktop (Data modeling and visualization layer)
  • Multiple Data Sources, including:
  • Excel and CSV files
  • SQL Server and MySQL databases
  • JSON and PDF files
  • SharePoint, Web, and Azure services

All data flows into Power Query, where it is cleaned, transformed, and validated before being loaded into the data model.

Connecting Data from Multiple Sources
Power BI provides connectors for a wide range of data sources. Below is a structured step-by-step approach for each.

Step 1: Connecting to Excel

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → Excel
    Image 2

  3. Browse and select your file
    Image 3

  4. In the Navigator, select sheets/tables
    Image 4

  5. Click Load(to PowerBI) or Transform Data(to Power query)
    Image 5

Step 2: Connecting to Text/CSV Files

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → Text/CSV
    Image 2

  3. Select your CSV file and click open
    Image 3

  4. Preview the dataset
    Image 4

  5. Click Load or Transform Data
    Image 5

Step 3: Connecting to PDF

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → More → PDF → Connect
    Image 2A
    Image 2B

  3. Select the file and click open
    Image 3

  4. Allow Power BI to detect tables
    Image 4

  5. Choose required tables
    Image 5

  6. Click Load or Transform Data
    Image 6

Step 4: Connecting to JSON

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → More → JSON → Connect
    Image 2A
    Image 2B

  3. Select file or API endpoint

  4. Load into Power Query
    Image 3

  5. Converted to a table & Expand nested fields Automatically

  6. Click Close & Apply
    Image 5

Step 5: Connecting to SharePoint Folder

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → More → SharePoint Folder → Connect
    Image 2A
    Image 2B

  3. Enter SharePoint URL and Click Ok

  4. Authenticate if required
    Image 3

  5. Select files

  6. Click Transform Data
    Image 5

Step 6: Connecting to MySQL Database

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → More → MySQL Database → Connect
    Image 2A
    Image 2B

  3. Enter server and database

  4. Provide credentials
    Image 4

  5. Select tables

  6. Click Load or Transform Data
    Image 6

Step 7: Connecting to SQL Server

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → SQL Server
    Image 2

  3. Enter server name (e.g., localhost) and Database Name

  4. Choose authentication method (Import OR Directquery)
    Image 4

  5. Select database and tables (e.g., FactSales, DimProduct)

  6. Click Transform Data
    Image 6

Step 8: Connecting to Web Data

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → Web
    Image 2

  3. Enter URL or API endpoint
    Image 3

  4. Select detected actual data structure from the list

  5. Click Load (Power BI) or Transform Data (Power Query)
    Image 5

Step 9: Connecting to Azure Analysis Services

  1. Open Power BI Desktop
    Image 1

  2. Go to Home → Get Data → More → Azure → Azure Analysis Services
    Image 2A
    Image 2B

  3. Enter server name

  4. Select model/database

  5. Choose Live Connection

  6. Click Connect/OK
    Image 5

Conclusion

Connecting to multiple data sources is not just a technical requirement, it is the foundation of effective data analytics. In today’s data-driven environments, analysts must seamlessly integrate data from files, databases, APIs, and cloud platforms.

Power BI simplifies this complexity through its rich ecosystem of connectors and the flexibility of Power Query. However, the true value lies in what happens after connection; data preparation and validation.

Strong data ingestion practices lead to:

  • Accurate and reliable insights
  • Better business decision-making
  • Scalable and maintainable data models

As a Data Analyst or Generative AI Data Analyst, mastering data connectivity is essential. It ensures that your dashboards are not only visually compelling but also trustworthy and impactful.

Ultimately, every great dashboard starts with one thing: well-connected, well-prepared data.

Top comments (0)