![]() Now, we can indicate that the first row should be used as our column headers. To remove the first row, click on “Remove Rows” from the Power Query Editor toolbar, click on “Remove Top Rows” and type “1” in the Number of rows dialog box. In this step, we’ll need to remove the first row because our column headers are actually in the second row. In our case, the source excel file needs a little bit of clean-up-so we’ll shape and transform the data as described below: In cases where data is presented as a table with all of the appropriate headers and no empty rows, these steps will do the job for us accurately. These steps were applied automatically to indicate the path of the source file (Source), the columns that were automatically discovered (Navigation) and the data types automatically detected (Changed Type). You can see that, on the right, the Query Settings pane lists all of the Applied Steps taken so far. ![]() The Power Query Editor opens to shape and transform our data. On the Navigator dialog, select “Data_GA” and click “Edit.”.Browse for the Excel file, select it from the list and click “Open.”.From the Power BI splash screen or toolbar, click on “Get Data,” select the Excel connector and click “Connect.”.The first step is to launch Power BI Desktop, then follow these steps: Note: If using Office 2016 or later, you may need to open the file in Excel and save it as an Excel 2016 workbook. Download the excel file and save it to a local folder. For the dataset, we will use the General Aviation 2013 Excel file available for public use from. To follow this example, you will need to download Power BI Desktop directly from Microsoft-don’t worry, it’s free-and install it. Now, let’s take a look at Power Query in Power BI Desktop. Once source data is refreshed, all Power BI visualizations are refreshed with the updated data as well. ![]() Because Power Query maintains a step-by-step record of every action taken to ingest, transform and load data, you can repeat these same steps when you need to refresh the data. Power BI Desktop and Power Query give users the ability to automate the process of ingesting, transforming and loading data into internal tables in Power BI, which can then be used as a source for Power BI visualizations. What is Power Query? And How Does it Work with Power BI Desktop? However, Microsoft has a tool that can make this process much easier and faster: Power Query in Power BI Desktop. The challenge with this method? The ETL process in Excel is usually a time-consuming, manual process that is not easy to automate. But in some organizations, business users do some of this ETL work right in Excel, often referred to as data shaping and transformation. Typically, the ETL process is carried out by robust enterprise-grade ETL applications such as SQL Server Integration Services (SSIS) or other third-party tools. During the ETL process, data is extracted from a data source, then transformed, validated, standardized, corrected, quality checked and ultimately loaded into a data repository-such as a data mart or data warehouse-where it is streamlined for analysis and reporting. In most organizations, data goes through an ETL (extract, transform and load) process before it is available for reporting.
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