Power BI Extract Transform Load (ETL): Everything About Power BI ETL

As the popularity of databases increased in the 1970s, Extract, Transform, and Load (ETL) was introduced as a process of integrating and loading data for computation. However, at the end of the 1990s, Microsoft standardized the ETL idea with a Data Transformation Service (DTS) program. Since then, ETL has been the dominant method for processing data, and it was later used for analysis in Business Intelligence (BI) Applications. 

Microsoft’s Power BI is one of the leading data visualization BI Tools that uses ETL in Power BI to process data. This article is a comprehensive guide covering Power BI ETL. Power BI extracts, transforms, and loads ideas around the Power BI data transformation engine – Power Query and some valuable features you can benefit from in Power BI. Read on to get started with the Power BI ETL process.

What Is Power BI?

Microsoft Power BI is one of the most powerful Business Intelligence Tools in the data industry. It was first created by Ron George in 2010 and named Project Crescent. Later, Microsoft renamed it Power BI. In 2015, Power BI was made public for the first time. 

Over the years, Microsoft has introduced a slew of features to Power BI, including natural language processing techniques, marvelous dashboards, exciting graphs and dashboards to work upon, Power BI Extract Transform Load (ETL) capabilities, integration with other Microsoft Apps, and robust security standards. 

More information on Power BI parts can be found in this guide here – Understanding Microsoft Power BI: A Comprehensive Guide. To discover the different types and options for creating Power BI visuals, we have another helpful guide: Ultimate Guide on Power BI Visuals: 20+ Types to Use in 2022.

What Is ETL?

The Extract, Transform, and Load (ETL) process extracts, transforms, and loads data from multiple sources to a data warehouse or a unified data repository. This centrally collected data in the repository makes it easier to analyze and handle it further. It also acts as a single point for teams’ and businesses’ accurate and consistent data requirements.

What Is the Need for Power BI Extract Transform Load?

• Data Integration: Power BI ETL allows you to combine data from multiple sources into a unified format for better analysis.
• Data Transformation: Enables cleaning, filtering, and transforming raw data into a usable format, ensuring data quality and consistency.
• Performance Optimization: By transforming data and pre-processing it, ETL reduces the load on Power BI during analysis, leading to faster performance.
• Automation of Data Processes: Power BI’s ETL process can automate repetitive data preparation tasks, saving time and reducing manual errors.
• Enhanced Data Analytics: ETL prepares data for advanced analytics and reporting, helping businesses derive meaningful insights.

This article will look at how Power BI’s Power Query handles data processing using the Power BI Extract Transform Load mechanism.

Understanding Power BI ETL Mechanism Using Power Query

Power BI’s Power Query is a tool for data transformation and preparation. It has an intuitive Graphical User Interface (GUI) and a Power Query Editor. The Power Query interface lets you obtain data from multiple sources, while the Power Query Editor allows you to transform these data. 

1. Data Acquisition

According to a 2017 IDC survey, business users spend nearly 80 percent of their time preparing data, which delays the analysis and decision-making processes. However, the Power Query mechanism can address such issues with its wide range of available features.

Power Query allows users to define data transformations against a subset of the complete dataset, quickly filtering and converting the data to a manageable size. Using the process (query), Power Query can easily be refreshed in the future to receive the most recent data. In addition, Power Query provides connectivity to a wide range of data sources of all sizes and shapes. 

2. Transformation Tool 

The Power Query transformation tool contains several prebuilt transformation functions that can be accessed using the Power Query Editor’s Graphical Interface. Power Query interface cottages a broad range of transformations, from simple ones like column elimination, row filtering, and first row as a table header to advanced ones like merge, append, group by, pivot, and unpivot.

3. Dataflows

Dataflow is a cloud-based platform that helps users prepare data for Power BI ETL. Users can also utilize Power BI Dataflows to ingest, convert, and load data into Microsoft Dataverse environments, Power BI workspaces, or an Azure Data Lake Storage account.

Using Dataflows, users can receive and manipulate the data based on their requirements. Instead of sending the result to Power BI or Excel, users can save the data in alternative storage solutions like Dataverse or Azure Data Lake Storage. 

Power Query, a unified data connectivity and preparation experience found in many Microsoft applications, including Excel and Power BI, is used to create dataflows. Customers can plan dataflows to run on demand or a set timetable, ensuring that data is constantly updated.

4. M Formula Language

Microsoft Power Query is a powerful “get data” tool that comes with a slew of functions. One of Power Query’s core features is filtering and aggregating the data from one or more available data sources. 

The Power Query Formula Language (informally known as “M”) represents any type of data mashup. It is Power Query’s data transformation language, and everything that happens in the query is written in M. 

You can utilize the advanced editor to access the query’s script (given below) and edit it as needed to execute advanced transformations with the Power Query tool.

let<br>    Source = Exchange.Contents("xyz@contoso.com"),<br>    Mail1 = Source{[Name="Mail"]}[Data],<br>    #"Expanded Sender" = Table.ExpandRecordColumn(Mail1, "Sender", {"Name"}, {"Name"}),<br>    #"Filtered Rows" = Table.SelectRows(#"Expanded Sender", each ([HasAttachments] = true)),<br>    #"Filtered Rows1" = Table.SelectRows(#"Filtered Rows", each ([Subject] = "sample files for email PQ test") and ([Folder Path] = "Inbox")),<br>    #"Removed Other Columns" = Table.SelectColumns(#"Filtered Rows1",{"Attachments"}),<br>    #"Expanded Attachments" = Table.ExpandTableColumn(#"Removed Other Columns", "Attachments", {"Name", "AttachmentContent"}, {"Name", "AttachmentContent"}),<br>    #"Filtered Hidden Files1" = Table.SelectRows(#"Expanded Attachments", each [Attributes]?[Hidden]? <> true),<br>    #"Invoke Custom Function1" = Table.AddColumn(#"Filtered Hidden Files1", "Transform File from Mail", each #"Transform File from Mail"([AttachmentContent])),<br>    #"Removed Other Columns1" = Table.SelectColumns(#"Invoke Custom Function1", {"Transform File from Mail"}),<br>    #"Expanded Table Column1" = Table.ExpandTableColumn(#"Removed Other Columns1", "Transform File from Mail", Table.ColumnNames(#"Transform File from Mail"(#"Sample File"))),<br>    #"Changed Type" = Table.TransformColumnTypes(#"Expanded Table Column1",{{"Column1", type text}, {"Column2", type text}, {"Column3", type text}, {"Column4", type text}, {"Column5", type text}, {"Column6", type text}, {"Column7", type text}, {"Column8", type text}, {"Column9", type text}, {"Column10", type text}})<br>in<br>    #"Changed Type"<br><br>

5. Power Query Experiences

Power Query can connect to various data sources, allowing users to work with their desired data. Currently, Microsoft offers two Power Query experiences:

1. Power Query Online
2. Power Query Desktop

A. Power Query Online

The following are the steps for getting data in Power Query Online:

Step 1: Connection Settings and Authentication

Visit the Power Query Online experience and head to the “Connect to the data source” page. Here, you have to enter these values in two distinct sections:

• Connection settings: The section defines the information needed to connect to your data source. Consider an example where you are using an SQL database as a data source.

• Connection credentials: The credentials necessary to establish a connection to your data source are the privacy levels, credentials, and the rest of the connector-specific fields.

Enter your credentials, such as User ID and Password, to proceed further with the ETL process in Power BI

• Authentication: In Power Query, every connection must be authorized. Authentication techniques differ from connection to the connector.

Step 2: Data Preview

The data preview stage aims to make previewing and selecting your data as simple as possible. You can preview data using one of the following methods, depending on the connector you’re using:

• Navigator window
• Table preview dialog box

Step 3: Query Editor

For Power Query Online, data must be loaded into the Power Query editor, where users can further transform and enrich their chosen query.

Select the Table that you want from the data source. To proceed with transforming data, check all dimensions and fact tables. You can perform transformations and load data to complete the Power BI ETL process. 

Some of the transformations you can do on your data are:

• Removing Unnecessary Columns

To do so, select the columns you want to remove, right-click, and then click on remove columns. 

• Setting Column Headers

Consider an example where you want to remove the top row as actual headers are in the second row. Click “Remove Rows” in the Power Query Editor toolbar to do this. Then click “Remove Top Rows” and enter “1” in the Number of Rows dialog box.

Now, to set the first row as our column headers, click the “Use First Row as Headers” option on the Power Query toolbar.

Your table will now show the correct column headers.

B. Power Query For Desktop

The following are the stages for gathering data in the Power Query Desktop experience:

Step 1: Connection Settings and Authentication

• Connection Settings: Most connectors require at least one parameter to create a connection to the data source. The connector parameters specify your data source path in conjunction with the connection.
• Authentication: In Power Query, every connection must be authorized. Authentication techniques differ from connector to connector.

Step 2: Data Preview

The data preview stage aims to make previewing and selecting the data as simple as possible. Depending on the connector that you’re using, you can preview data by using either:

• Navigator window
• Table preview dialog box

Step 3: Query Destination

Users can define where the query should be loaded in the Query destination stage. The possibilities vary depending on the integration, but loading data into the Power Query editor to further transform and enhance the query is always an option.

How To Use Dataflows for Power BI ETL?

Once you know what ETL is in Power BI, you can carry out this process with Power BI dataflows. They are collections of tables you create and manage in workspaces of Power BI service. It is similar to some tables in your database, which you can update through refresh schedules. Power Query works while the resultant data is loaded in the Azure Data Lake Storage Gen2. To do this, you must have a premium license for Power BI Service. 

You can follow the below steps to create a dataflow with Power BI:

Step 1: Login to Power BI Service. In your workspace, click on new and then dataflow.

You will see four options that allow you to create dataflows using different methods.

Method 1: By defining new tables

Step 1.1: Click on Add new tables and then connect them to your data source. Here, we are using an SQL server database as a source.

Step 1.2: Configure SQL server as a source in Power BI.

Step 1.3: Select the table you need and then click on transform data.

Method 2: Using linked tables

In this method, a link is created to an existing table. For this, select Add Links Tables from four options and choose the table you wish to link from the hierarchies of tables. Then, click on Transform Data. 

Method 3: Using Computed Tables

This is done by including a reference table and doing some storage computations. You can edit the linked tables and start creating computed tables from here. To do this, you can right-click a table and check  Enable load. 

Method 4: Using Import/Export

You can export an existing Dataflow to a JSON file with this. You can then create a Dataflow in another workspace by importing the model using the exported file.

Then, you can refer to the four options for creating a new Dataflow again. You need to go to another workspace and create a new dataflow to import your exported model. Select the Import model. Search for the JSON file you exported and then click OK.

Thus, Power BI dataflows can also be an efficient way of carrying out the ETL process if you know the ETL process in Power BI.

How Does Dataflow Make Power BI ETL More Effective?

• Azure Integration: Dataflows enable direct data ingestion into Azure Data Lake Gen 2, supporting advanced analytics, machine learning, and SQL and enhancing collaboration between analysts and data scientists.
• Common Data Model (CDM): Ensures data consistency by mapping diverse data into a standard schema, allowing seamless collaboration and customization for specific needs.
• Simplified ETL: Tools like Power Query and a strong calculation engine make data cleaning, integration, and transformation faster, easier, and more cost-effective.
• Self-Service & Collaboration: Empowers business users to handle complex transformations independently while promoting unified data analysis across teams.

Conclusion

In this blog, you learned about Power BI extract transform load and how to perform data preparation using Power BI and Power Query.

When it comes to building your own ETL pipelines, integrating and merging heterogeneous data sources can get overwhelming, especially for starters when you don’t have a helping hand. Configuring and running your ETL pipelines would require recurring assistance from your engineering teams, and non-data teams would need much training to become familiar with your setup. 

Luckily, Hevo has done all the legwork for you, so you can set up and configure your ETL pipelines in minutes. Why not try Hevo and the action for yourself? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand.

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