Identifying and connecting to data sources is a fundamental skill for Power BI Data Analysts. This process involves discovering where your organization's data resides and establishing connections to retrieve that data for analysis and visualization.
Power BI supports a vast array of data sources, …Identifying and connecting to data sources is a fundamental skill for Power BI Data Analysts. This process involves discovering where your organization's data resides and establishing connections to retrieve that data for analysis and visualization.
Power BI supports a vast array of data sources, categorized into several types. File-based sources include Excel workbooks, CSV files, XML, and JSON files stored locally or in cloud storage like OneDrive and SharePoint. Database sources encompass SQL Server, Azure SQL Database, Oracle, MySQL, PostgreSQL, and many other relational databases. Cloud services include Azure Data Lake, Azure Synapse Analytics, Salesforce, Dynamics 365, and Google Analytics. Additionally, Power BI can connect to web-based data through APIs and online services.
To connect to data sources, you use the Get Data feature in Power BI Desktop. This presents a comprehensive catalog of available connectors organized by category. After selecting your source type, you provide connection details such as server names, database names, file paths, or URLs. Authentication methods vary by source and may include Windows credentials, database credentials, organizational accounts, or API keys.
When identifying appropriate data sources, analysts must consider data freshness requirements, security policies, and performance implications. Understanding whether data should be imported into the Power BI model or accessed through DirectQuery mode is essential. Import mode loads data into memory for faster performance, while DirectQuery maintains a live connection to the source.
Best practices include documenting data source locations and connection parameters, establishing consistent naming conventions, and validating data quality upon connection. Analysts should also verify they have appropriate permissions to access each data source and understand any data governance requirements.
Successfully identifying and connecting to the right data sources ensures your Power BI reports reflect accurate, timely information that drives informed business decisions across your organization.
Identify and Connect to Data Sources in Power BI
Why It Is Important
Identifying and connecting to data sources is the foundational step in any Power BI project. Before you can create visualizations, build reports, or derive insights, you must first bring data into Power BI Desktop. Understanding the various data source types, connection methods, and authentication options is critical for the PL-300 exam and real-world scenarios. Poor data source selection or connection configuration can lead to performance issues, security vulnerabilities, and inaccurate reporting.
What It Is
This topic covers the process of selecting appropriate data sources and establishing connections to retrieve data for analysis in Power BI. Data sources can include:
• Files: Excel workbooks, CSV files, XML, JSON, PDF, and SharePoint folders • Databases: SQL Server, Azure SQL Database, Oracle, MySQL, PostgreSQL • Online Services: SharePoint Online, Dynamics 365, Salesforce, Google Analytics • Azure Services: Azure Blob Storage, Azure Data Lake, Azure Synapse Analytics • Other Sources: Web pages, OData feeds, ODBC connections, R scripts, Python scripts
How It Works
In Power BI Desktop, you access data sources through the Get Data button on the Home ribbon. The process involves:
1. Selecting the data source type from the Get Data dialog 2. Providing connection information such as server names, file paths, or URLs 3. Choosing the connectivity mode: Import or DirectQuery 4. Authenticating using appropriate credentials (Windows, Database, Microsoft Account, or organizational credentials) 5. Selecting specific tables, views, or objects to load or transform
Import Mode loads data into the Power BI model for fast query performance. DirectQuery queries the source in real-time, useful for large datasets or when real-time data is required.
Storage Modes also include Dual mode in composite models and Live Connection for Analysis Services.
Exam Tips: Answering Questions on Identify and Connect to Data Sources
• Know the differences between Import, DirectQuery, and Live Connection modes. Understand when each is appropriate based on data size, refresh requirements, and performance needs.
• Understand authentication methods. Questions may ask which authentication type to use for specific scenarios like Azure SQL Database (organizational account) versus on-premises SQL Server (Windows authentication).
• Be familiar with data gateway requirements. On-premises data sources require a gateway for scheduled refresh in the Power BI service. Cloud sources do not need a gateway.
• Know file-based data source options. Understand differences between connecting to a single file versus a folder of files, and when to use each approach.
• Remember connector-specific limitations. Some connectors only support Import mode, while others support both Import and DirectQuery.
• Understand privacy levels. Know the three privacy levels (Private, Organizational, Public) and how they affect data source combinations.
• Practice with common scenarios. Be ready for questions about connecting to SharePoint lists versus SharePoint folders, or choosing between Azure Blob Storage and Azure Data Lake.
• Pay attention to performance considerations. DirectQuery has query limitations and may result in slower report performance compared to Import mode.