Connecting and Analyzing Data from Multiple Sources - Textnotes

Connecting and Analyzing Data from Multiple Sources

This module focuses on integrating Power BI with various data sources, including SQL Server, Azure, SharePoint, and Web APIs. Learners will understand the differences between DirectQuery and Import modes, how to combine multiple data sources using Power Query, and the integration of Power BI with Excel for advanced analytics. Hands-on exercises will guide students through connecting to SQL Server and creating live, interactive dashboards.

1. Importing Data from SQL Server, Azure, SharePoint, and Web APIs

Power BI can connect to multiple data sources, enabling comprehensive analysis across platforms.

  1. SQL Server: Connect by providing server name and database.
  2. Example: Importing sales data from an SQL Server database for analysis.
  3. Azure: Connect to Azure SQL Database or Azure Blob Storage.
  4. Example: Pulling data from Azure SQL to analyze cloud-based sales records.
  5. SharePoint: Import lists or files stored on SharePoint Online or On-Premises.
  6. Example: Analyzing HR data stored in a SharePoint list.
  7. Web APIs: Connect to REST APIs to pull JSON data.
  8. Example: Fetching weather data from a public API for trend analysis.

2. DirectQuery vs Import Mode

Power BI offers two primary data connectivity modes:

  1. Import Mode: Data is imported into Power BI, allowing for faster performance and more complex modeling.
  2. Example: Importing a monthly sales file for detailed analysis.
  3. DirectQuery Mode: Data stays in the source system, enabling real-time access but with some limitations in DAX functions.
  4. Example: Connecting live to a large SQL Server database for up-to-the-minute data.

3. Combining Multiple Sources Using Power Query

Power Query allows for data transformation and merging from different sources.

  1. Example: Combine sales data from an Excel sheet with customer information from SQL Server to create a unified dataset.
  2. Steps:
  3. Connect to Excel and SQL Server.
  4. Merge tables based on common keys (e.g., Customer ID).
  5. Transform and clean data as needed.

4. Power BI & Excel Integration: PivotTables, Power Query, Power Pivot

Power BI and Excel can work together seamlessly:

  1. PivotTables: Use Power BI data in Excel PivotTables for advanced analysis.
  2. Example: Import Power BI data model into Excel to create dynamic PivotTables.
  3. Power Query: Leverage Power Query in Excel to transform data before importing it into Power BI.
  4. Example: Clean and shape data in Excel using Power Query before loading it into Power BI.
  5. Power Pivot: Create complex data models in Excel and use them in Power BI for enriched analytics.
  6. Example: Use Power Pivot to create relationships and measures in Excel, then connect Power BI to the same model.