Lesson 2.2: Connecting to Data Sources in Power BI Desktop

Introduction:
In the previous lesson, we learned how to navigate the Power BI Desktop interface and imported a sample sales dataset from a CSV file. Now, in Lesson 2.2, we will focus on connecting Power BI to various data sources. This lesson will demonstrate step-by-step how to connect to a common source (an Excel workbook) and also show an example of connecting to a second source type (a CSV file). We’ll also discuss other popular data sources (like SQL Server databases and cloud services such as Azure or SharePoint) to illustrate Power BI’s versatility. The goal is to emphasize the ease of the Get Data experience – with just a few clicks, you can import data from numerous sources – and to establish that Power BI can aggregate data from disparate sources into one model for analysis.

By the end of this lesson, you will know how to:

• Connect to an Excel workbook in Power BI Desktop.
• Connect to a CSV file (and understand that the process is similar for other file types).
• Recognize other data source options (databases like SQL Server, online services like SharePoint or Azure) and the basic steps to connect to them.
• Combine data from multiple sources in a single Power BI data model.
• Verify and manage data connections, and understand common challenges and how to troubleshoot them.

Let’s get started with our first scenario: connecting to an Excel workbook.

Connecting to an Excel Workbook (Step-by-Step Example)

Many professionals store data in Excel, so Power BI Desktop provides a straightforward way to import Excel data[5]. We will use the sample sales data (SalesData) in an Excel format for this demonstration (you can imagine the CSV from the previous lesson saved as an Excel file, SalesData.xlsx, with a table of sales transactions). Follow these steps to connect Power BI to an Excel workbook:

1. Open Power BI Desktop and start a new report: Launch Power BI Desktop and close any start screen or dialogs so that you have a blank report canvas ready. Ensure you’re in Report view (which is the default view for building reports).

2. Click “Get Data” from the Home ribbon: On the Home tab of the ribbon, click the Get Data button. This opens a menu of common data sources. Select Excel from this list (if you don’t see it directly, click More… and find Excel Workbook under the File category)[3]. This tells Power BI that you want to connect to an Excel file.

3. Choose the Excel file to connect: A file picker dialog will appear. Navigate to the location of your Excel workbook (for example, SalesData.xlsx) and select it, then click Open[5]. Power BI will establish a connection to that Excel file.

4. Navigator window – select data to import: After connecting, the Navigator window will appear, showing the contents of the Excel file. Here you will see a list of sheets or tables detected in the workbook. In our example, you might see a sheet named “SalesData” (or a table if the data range was formatted as an Excel Table).

   • Select the table or sheet that contains your data. For instance, check the box next to SalesData.
   • A preview of the data will display on the right side of the Navigator, letting you verify that you’ve chosen the correct data. You should see columns like Order Date, Customer Name, etc., with a few sample rows[7] (this matches our sales transactions dataset).
   • (Optional): If you have multiple tables/sheets and want to import all of them, you can check multiple boxes. Power BI allows importing several tables from one Excel file in one go. For example, if the Excel had separate sheets for “Sales” and “Customers,” you could select both.

5. Load the selected data: Once you have the desired table(s) selected, click the Load button. Power BI will import the data from the Excel workbook into the Power BI data model. You’ll see a loading progress indicator.

   • If you needed to perform any data cleaning or transformations before loading (such as filtering rows or changing data types), you could click Transform Data instead, which opens Power Query Editor. In this simple case, we assume the Excel data is already clean, so loading directly is fine.

6. Verify the data in Power BI: After loading, the Navigator window closes and you return to the main Power BI interface. Look at the Fields pane on the right – it will now list a dataset (table) with the same name as your Excel sheet or table (e.g., SalesData). Expand it to see the fields (columns) that were imported – you should see all the columns from the Excel file (Order No, Order Date, Customer Name, etc.)[7]. This confirms that the data from Excel is now available in Power BI.

   • You can also click on the Data view (the table icon on the left sidebar) to inspect the imported data in a spreadsheet-like view. This is useful to ensure everything looks correct (dates are properly recognized, numbers are in the right format, etc.).

7. (Optional) Rename the table or fields: It’s a good practice to rename the table or any fields if needed for clarity. For example, if the Fields pane shows the table as “Sheet1” (a generic name), you can double-click it and rename it to Sales Data for clarity. Likewise, ensure fields have intuitive names (Power BI usually takes them directly from Excel’s header row, so they should already be descriptive). Renaming doesn’t affect the source file; it only changes how things are labeled in Power BI.

8. Create a quick visual to test the data (optional): To make sure everything is working, you can create a simple visualization using the Excel data. For instance, try a quick check:

   • Select a card visual from the Visualizations pane (this is a simple visual that shows a single number).
   • Drag the Order No field onto the card visual and change its aggregation to Count (Distinct). This will display the number of unique orders in the Excel data, giving you a quick sense that data is being read (e.g., “10” if there were 10 orders in the sample data).
   • This step is just to validate that Power BI can use the imported data. You can delete the visual afterward or keep it as part of exploration.

You have now successfully connected to an Excel data source in Power BI Desktop. The key steps were choosing Get Data > Excel, selecting the file, and loading the data – Power BI handled the rest with just a few clicks[5]. Next, let’s connect to another type of source to illustrate the process for a different format.

Connecting to a CSV File (Step-by-Step Example)

Now we’ll demonstrate connecting to a CSV file, which is another very common data source. The process for other text-based files (like CSV, TXT) is quite similar to Excel, with just minor differences. We will use the provided sample SalesData.csv (which contains the same sales transactions data in CSV format) as our example. Imagine this CSV could be a second dataset we want to bring into the report (for instance, perhaps new sales records or data from another system exported as CSV). Follow these steps:

1. Get Data > Text/CSV: In Power BI Desktop (with your report still open, you can continue from the previous steps), go to the Home ribbon and click Get Data again. This time, select Text/CSV from the data source options (it might appear under the Common data sources list; if not, click More… and find Text/CSV under the File category)[3]. This tells Power BI you want to connect to a comma-separated values file.

2. Choose the CSV file: In the file browser dialog that appears, find SalesData.csv on your computer and select it, then click Open. Power BI will initiate a connection to that CSV file. Since CSV is just text, this is straightforward – no special driver needed.

3. Preview and confirm delimiter: Once the file is selected, Power BI directly shows a preview of the data (often it will directly open a simplified Navigator for CSV). You should see a preview of the rows and columns from the CSV, with Power BI automatically splitting columns by the delimiter (comma by default). Verify that the columns (Order No, Order Date, Customer Name, etc.) are correctly identified. If the CSV uses a different delimiter (e.g., semicolon), you can change the delimiter option at this stage. In our case, it’s a standard comma-delimited file, so the preview should look correct (similar to the Excel data preview)[6].

4. Load the CSV data: Click Load to import the data from the CSV into Power BI. The data will be added to your data model. If this is the second dataset in the report, it will come in as another table in the Fields pane. For example, it might be named SalesData CSV or just SalesData (if it has the same name, Power BI might append a “2” or something to distinguish, since we already have a SalesData table from Excel; you can always rename it). After loading, check the Fields pane: you should now see two tables – one from the Excel workbook and one from the CSV file. Each contains the fields of the respective data source.

5. Verify the CSV data: Expand the new CSV-based table in the Fields pane to see its fields, confirming all columns were imported. You can also switch to Data view and inspect a few rows. The contents should match the CSV (which in this case, are the same sample sales records)[6]. This step is just to ensure the CSV was read correctly. If something looks off (e.g., all data is jammed into one column), it might mean the delimiter was wrong – you would then use the Edit option instead of Load and specify the correct delimiter in Power Query. But if the preview looked fine, the data will likely be fine.

Now you have imported data from a CSV file as well. The “Get Data” experience for files is very consistent – whether it’s Excel, CSV, JSON, XML, etc., you select the connector, pick the file, maybe adjust a setting or two, and load. In fact, the Get Data dialog categorizes sources for convenience (All, File, Database, Online Services, etc.), but the core steps (choose source > provide path/credentials > select data > load) remain similar for most sources.

A note on difference between Excel and CSV import:

• Excel: can contain multiple sheets or tables; the Navigator lets you pick which ones to import. Power BI can import more than one table at once from a single Excel file. Excel data might need to be formatted as a table for best results (Power BI will list named tables or sheets in Navigator).
• CSV: contains a single table of data (no multiple sheets). When connecting, Power BI goes straight to preview since there’s only one thing to import. Once loaded, you get one table in the model.

After connecting to both an Excel and a CSV, you have two datasets within the Power BI model simultaneously. Next, we’ll discuss how Power BI can handle many other data sources in a similar manner, and how we can combine data from different sources.

Other Data Sources in Power BI (Databases, Online Services, etc.)

Power BI Desktop is designed to connect to a wide array of data sources beyond just Excel or CSV files. In fact, it supports hundreds of connectors for various databases, platforms, and services[3]. The Get Data menu organizes these connectors into categories for easy browsing. Here are some of the popular data source types and how connecting to them generally works:

• Relational Databases: Examples: SQL Server, Azure SQL Database, Oracle, MySQL, PostgreSQL, etc.
  To connect to a database, select its connector from the Database category in Get Data. For instance, choose SQL Server database. You will be prompted to enter the server name and optionally the database name, and your access credentials (which could be Windows authentication or a SQL login)[4]. After providing these and hitting Connect, Power BI will reach out to the database. If the connection succeeds, a Navigator window (similar to the Excel one) will list the tables and views in that database. You can then select one or multiple tables to import. Once you click Load, those tables become part of your model just like Excel sheets did.
  Ease factor: You don’t need to write SQL to get the data (though you can, via native query if needed) – Power BI fetches the table data for you. With a few fields filled in and clicks, you can pull in entire database tables.

• Cloud Services and Online Platforms: Examples: Azure services, SharePoint Online, Dynamics 365, Salesforce, Google Analytics, etc.
  Power BI has dedicated connectors for many cloud and online services under categories like Azure and Online Services[3]. For instance:

  • To connect to an Azure SQL Database (a cloud database), you’d use the Azure SQL Database connector. The dialog is similar to SQL Server – you provide the server (e.g., <servername>.database.windows.net), database name, and credentials (likely your Azure AD or SQL login) and then proceed to select tables to load.
  • To connect to a SharePoint Online list or file, you can use the SharePoint Online List connector or SharePoint Folder connector. You would enter the URL of your SharePoint site. For a SharePoint list, Power BI will list available lists at that URL for selection. For a SharePoint folder (useful when you have Excel/CSV files on SharePoint), you provide the site URL and then Power BI can enumerate the files in the document library. For example, if you stored an Excel on SharePoint, you could connect to it this way (Power BI can combine files in a folder or pick a specific file from the list). You will typically need to authenticate with an Organizational account (your Microsoft 365 credentials) when connecting to SharePoint or other Office 365 services.
  • Other services like Dynamics 365 or Salesforce have connectors where you simply log in with your credentials through a OAuth dialog that Power BI presents, then you can select entities (tables) to import.
    Ease factor: Power BI’s connectors handle the intricate details of connecting to cloud APIs or services. As a user, you mostly just need to provide a URL or select an account and sign in, then click through to get your data.

• Other File Types: Examples: JSON, XML, PDF, Parquet, Folder of files.
  In addition to Excel and CSV, the File category of Get Data includes connectors for JSON files, XML files, PDF documents, etc.[3]. The experience is again similar: provide the file path, then Power BI will try to interpret the file. For JSON/XML, it will allow you to navigate the structure of the file to pick elements to import. For PDF, it attempts to find tables in the document. There is also a Folder connector, which allows you to specify a folder path — Power BI will list all files in that folder, and you can then use Power Query to combine them (useful for when you have, say, a monthly CSV drop and want to append all CSVs together automatically).
  Ease factor: No matter the file format, Power BI gives a unified way to extract tabular data from it. This often saves time compared to manually converting data.

• Web and Other Sources: Examples: Web pages, OData feeds, REST APIs.
  If you select Web from the Get Data dialog (under Other category), you can connect to a web resource by entering a URL. For example, you might input a URL to a web page that has tables (like a Wikipedia table) or a link to a raw data file. Power BI can scrape tabular data from HTML or connect to web endpoints. Similarly, OData Feed allows connecting to any data source that exposes an OData service (you’d provide the service URL). These connectors might require additional parameters or credentials depending on the source, but Power BI will prompt as needed.
  Ease factor: The ability to pull data from the web means you can integrate public data (e.g., population stats, currency exchange rates) into your model easily – again through a few clicks and entering a URL.

👉 Tip: The Get Data UI in Power BI is search-friendly. If you’re overwhelmed by the list of connectors, there’s a search box in the Get Data dialog where you can type, for example, “SharePoint” or “Oracle” to quickly find the connector you need. Power BI is continually adding connectors, and as of now, it can connect to an ever-expanding list of sources (files, databases, online services, Azure, etc.) – making it a very flexible tool for combining data[2].

Now that we’ve seen how to connect to different sources, let’s talk about using multiple data sources together in one Power BI report, which is a powerful feature of the tool.

Combining Data from Disparate Sources into One Model

One of the key advantages of Power BI is that you can bring data from completely different sources into a single data model and analyze them together[2]. For example, you could import sales data from an Excel file, customer info from a SQL Server database, and targets from a SharePoint list, and combine them in one report. Power BI will treat all imported data as part of the model, allowing you to create relationships and build visuals across these sources.

Continuing our example, we have two tables in our model: one from an Excel workbook and one from a CSV file (both happen to contain similar sales data for demonstration). In a real scenario, these might be different but related datasets. Here’s how we can work with multiple sources:

• Viewing multiple tables: In the Fields pane, you can see both tables. If they contain related information (e.g., one could be “Sales” and another “Customers”), you can manage their relationship. Switch to the Model view (the third icon on the left, which looks like a diagram). There, Power BI might automatically detect relationships between the tables. For example, if both tables had a column like CustomerID, Power BI would likely infer a relationship between them[2]. Automatically detected relationships will appear as lines connecting the tables in the Model view.

• Creating or editing relationships: If Power BI doesn’t auto-detect a relationship (or does it incorrectly), you can create one manually. In Model view, simply drag a field from one table onto the matching field in the other table to create a relationship[2]. For instance, if our Excel data had a “Customer Name” column and we had another table (say from a database) that also has “Customer Name”, we could link those. Once related, you can use fields from both tables in a single visual. (If no common field exists, you might not combine those tables directly in a visual, but you could still analyze them separately in the same report.)

• Example – combining Excel and CSV data: Since our current two tables actually contain the same kind of data (sales transactions), let’s imagine a slightly different scenario to illustrate combining: Suppose SalesData.xlsx contained the sales transactions, and we also had a Customers.csv file with customer details (Customer ID, Name, Region). We could import both. In Power BI, we’d create a relationship between SalesData[Customer Name] (or ID) and Customers[Customer Name/ID]. With that in place, we could make a visual like “Total Sales by Customer Region” – taking the Total measure from the SalesData (Excel) table and the Region field from the Customers (CSV) table. Power BI would be able to aggregate sales by region thanks to the relationship. This demonstrates how disparate sources (Excel and CSV in this case) can work together after being brought into Power BI.

• One model, multiple sources: All the imported data, regardless of source, resides in the Power BI in-memory model once loaded (when using the default Import connectivity mode). This means the data from your Excel, CSV, etc., is now part of one dataset (the .pbix file stores it). You can add as many sources as needed. For instance, you can next connect to a SQL database and bring another table in – it will just appear as another table in the model. You can create relationships as needed among all these tables. Essentially, Power BI acts as a central repository where data from different places comes together.

• Handling updates and refresh: When combining data sources, keep in mind each source will retain its origin information. If the underlying data changes (say, the Excel file gets new rows or the SQL database updates), you’d need to refresh the Power BI model to get those changes. In Power BI Desktop, you do this by clicking Refresh on the Home ribbon, which will re-query each source. The ability to combine sources doesn’t mean they automatically stay in sync – you control when to pull new data. If you publish the combined model to the Power BI Service, you might need a gateway for on-premises sources and you can schedule refresh to keep data up to date (more on that in future lessons, perhaps).

In summary, Power BI enables powerful data mashups. Business users often face data scattered in different files and systems; Power BI lets you connect to all of them and unify the data for analysis. This eliminates the need to manually consolidate data in Excel. Once the data is in one model, creating calculations or visuals that use fields from multiple sources is seamless. We even saw that Power BI can auto-detect some relationships when keys match, saving you effort[2].

Key takeaway: You can have a single Power BI report that draws from Excel files, CSVs, databases, and cloud sources all together. This flexibility is a major advantage in real-world scenarios where data is not all in one place.

Verifying and Managing Data Connections

After connecting to various data sources, it’s important to know how to view and manage those connections in Power BI Desktop. Here are a few tips for verifying and managing data source settings:

• Data Source Settings: Power BI Desktop provides a central place to see all the data sources used in your report. Go to File > Options and settings > Data source settings. A window will list all the sources you’ve connected to in this report (it might show the file paths of Excel/CSV, the server name of databases, etc.). From here, you can:

  • View or Edit Permissions: You might see each source with an entry; selecting one and clicking Edit Permissions lets you change the credentials or privacy level. For instance, if you connected to a SQL database and now need to use different credentials, you’d update it here.
  • Change Source Path: If your file moved to a new folder or your database server name changed, use Change Source to update the path/connection info without rebuilding the report. For example, if you initially connected to C:DataSalesData.xlsx and the file moved to D:NewDataSalesData.xlsx, you can change the path here and Power BI will use the new file (assuming structure is the same).

• Refreshing data: As mentioned, if you want to verify that Power BI can still retrieve data from sources, click the Refresh button on the Home ribbon (the circular arrow icon). This will re-connect to each source and pull in any new data. If there’s an issue (say, the file path is broken or credentials expired), you’ll get an error at refresh time, indicating something needs attention. Regularly refreshing ensures your connections remain valid and data is current.

• Power Query Editor (Query view): If you click Transform Data, you open the Power Query Editor which shows queries for each data source. Each query usually has a first step called Source. By inspecting that step (at the top of the Applied Steps list), you can see exactly what source is being used (it might show the file path or server and SQL query). This is another way to verify you’re pulling from the correct location. For advanced checks, you could even adjust queries here (like filtering data or merging tables from different sources).

• Verify data after import: We already did this by looking at the Fields pane and Data view. It’s always good to spot-check that numeric fields are aggregating properly (no unexpected text values) and dates are recognized as dates (so you can use date hierarchies). If something seems off, you might need to Transform Data and fix a data type or value.

Managing connections is mostly about ensuring that as things change in your data environment, your Power BI report keeps working. The Data source settings dialog is your friend for maintaining those links.

Common Challenges & Troubleshooting Data Connections

Connecting to data in Power BI is designed to be simple, but in practice you may encounter some challenges. Here are some common issues and tips on troubleshooting them:

• Authentication and Access Issues: One of the most frequent problems is not having the right credentials or permissions for the data source. For example, you might not have access to a particular database or a SharePoint site. If you get errors while connecting (like “Access denied” or credential prompts), double-check that you’re using correct credentials. You can update credentials via Data source settings. Also, ensure any required drivers or network access are in place (e.g., to connect to Oracle you might need an Oracle client installed).
  Troubleshoot: If a SQL database connection fails, confirm the server name, database name, and that your user has read access. For web services, ensure your organizational account is used if required. Often, simply re-entering the correct username/password will resolve connection errors.

• Data Format and Compatibility: Sometimes Power BI might have trouble interpreting a file. For instance, connecting to an older Excel (.xls) might require a correct driver (Power BI prefers .xlsx or .xlsm for Excel). Or a CSV file might not be reading correctly if it has an unusual encoding or delimiter.
  Troubleshoot: For Excel, if you encounter an error like “the provider is not registered”[1], you might need the Access Database Engine installed (for .xls files) or to save as a newer format. For CSV, ensure you select the right file encoding and delimiter in the preview window. Power BI usually guesses correctly, but if you see gibberish or all data in one column in the preview, adjust settings (there’s an option to specify delimiter manually).

• Connection Timeout or Large Data Issues: If you try to import a very large table from a database or a huge text file, you might hit performance issues or timeouts. The Get Data process might seem to hang or error out.
  Troubleshoot: Consider filtering the data in the connector or using the Query Editor to apply a filter (like import only last 2 years of data, or specific columns). You can also look into using DirectQuery mode instead of import for very large databases (DirectQuery doesn’t import all data, it queries on-demand, but has its own limitations). Also ensure your machine has enough memory for large imports. For slow connections, verify network stability to the data source.

• Privacy Level and Formula Firewall: If you combine data from different sources, sometimes Power BI’s privacy settings might prevent them from being mashed up (to protect sensitive data from being sent to an untrusted source). You might see a privacy error when merging queries from different domains.
  Troubleshoot: You can adjust privacy level settings in Data source settings (e.g., set both sources to Public if appropriate, or disable privacy checks for combining data in the Project options if you’re comfortable doing so). But caution: adjust privacy settings only if you understand the implications for data governance.

• Gateway Issues (for Power BI Service): This is slightly beyond Desktop, but worth noting: if you publish a report that connects to on-premises data (like a local SQL Server or a file on your local drive), the Power BI Service will require an On-premises Data Gateway to refresh that data. If you don’t configure a gateway, your dataset online can’t update. So, if after publishing you get errors about unable to refresh or needing a gateway, it means you need to install/configure the gateway to bridge the cloud service to your local data source[4].
  Troubleshoot: Install the gateway (in personal mode for simplicity, or standard mode if for enterprise use) on a machine that can access the data source, then add the data source credentials in the Power BI Service to map to the dataset. This ensures your cloud report can refresh from on-prem data.

• General Debugging Tips: If a connection isn’t working, try these steps:

  • Update Power BI Desktop: Ensure you’re on the latest version of Power BI Desktop, as updates often improve connectors and fix bugs.
  • Test outside Power BI: Try connecting to the data outside of Power BI (e.g., open the Excel file in Excel to ensure it’s not corrupted). This can isolate if the issue is with the source or Power BI.
  • Check online resources: Error messages in Power BI are sometimes cryptic. Copy the text of any error and search the Power BI forums or web. The community is large, and chances are someone has faced the same issue. Microsoft’s documentation also has sections for known issues with certain connectors.

Don’t be discouraged by connection challenges. Once set up, connections usually remain stable. It’s often just the initial configuration that requires attention. With practice, you’ll know how to quickly troubleshoot most issues.

Conclusion and Next Steps

In this lesson, we demonstrated how to connect Power BI Desktop to a variety of data sources and highlighted the simplicity and power of the Get Data experience. Here are the key points to remember from Lesson 2.2:

• Connecting to Excel: We walked through importing an Excel workbook. Simply use Get Data > Excel, pick your file, select tables/sheets in the Navigator, and click Load. Power BI makes it easy by providing previews and letting you import multiple sheets at once[5].

• Connecting to CSV (and other files): Using Get Data > Text/CSV, we connected to a CSV file. The process was very similar to Excel – Power BI handles parsing the file and importing the data with just a couple of clicks. The same approach applies to other file types like JSON, XML, etc. No coding required, just point to the file and load.

• Other popular data sources: Power BI can connect to databases (SQL Server, Oracle, etc.) by entering server details and selecting the data, and to cloud services (Azure, SharePoint, Dynamics 365, etc.) by using built-in connectors where you usually provide a URL or sign in with credentials[4]. The user experience is consistent – the Get Data dialog guides you through what’s needed for each source and then shows you a Navigator to pick your data. There are over a hundred connectors, covering everything from basic files to online SaaS datasets[3].

• Ease of use: We emphasized that with just a few clicks, you can bring data from anywhere into Power BI. The interface is user-friendly, hiding technical complexities. For instance, connecting to a web service or SQL database might involve complex APIs or connection strings, but Power BI abstracts that – you just fill in a form and click connect.

• Combining data sources: We saw that Power BI allows you to have multiple data sources in one report. The data from different sources (once imported) can be related and used together. This means you can create unified analytics without manually consolidating data beforehand. We discussed how to create relationships between tables from different sources and how Power BI may auto-detect those relationships when common fields exist[2]. The result is a single data model that can answer questions across datasets (e.g., combining sales data from one system with customer info from another).

• Verifying and troubleshooting connections: We learned about the Data Source Settings dialog to review and adjust connections, and the importance of correct credentials and permissions for each source. We also covered common issues (authentication problems, data format issues, gateway needs for cloud refresh) and how to address them (updating credentials, installing drivers, using gateways, etc.)[1].

Next Steps: Now that you know how to connect to various data sources, you can practice by importing different kinds of data you have. As a follow-up exercise, you might try to import another sample dataset (for example, an Excel sheet with 50 sample rows, as mentioned) on your own to solidify these steps. In future lessons, we will delve into transforming data (cleaning and shaping it in Power Query) and creating more sophisticated visuals and calculations.

For now, take a moment to reflect on the broad connectivity Power BI offers. Having your data in Power BI is the first big step to gaining insights. With Lesson 2.2, you’ve expanded your ability to get data into Power BI from almost anywhere. From here, you can focus on analyzing and visualizing that data, which is where Power BI truly shines.

Keep this lesson as a reference when you encounter a new data source – the steps will likely be a variation of what we’ve done here. Happy data connecting!