As the popularity of databases started increasing 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 program called Data Transformation Service (DTS). Since then, ETL has been the dominant method for processing data, later to be used for analysis in Businesses Intelligence (BI) Applications.
Microsoft’s Power BI is one of the leading Data Visualization BI Tools that use Power BI Extract Transform Load (ETL) mechanisms to process data. This article is a comprehensive guide covering Power BI ETL. We’ll share the basic terminology and key concepts behind Extract, Transform Load (ETL) processes in Power BI, ideas around Power BI data transformation engine – Power Query, and some useful features you can benefit from in Power BI. Read on to get started.
Table of Contents
Prerequisites
A basic knowledge of Business Intelligence (BI) Tools and Data Pipelines can be helpful.
What Is Power BI?
Image Credits: Microsoft 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 the year 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, that include 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.
Power BI is a set of software applications, services, and connectors that work together to transform data from different sources into visually appealing, analytical, and interactive insights.
The cloud-based Power BI offers services known as Power BI Service, as well as a desktop-based interface known as Power BI Desktop. It includes data preparation, data discovery, and interactive dashboards, among its data warehouse features.
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 at your disposal – Ultimate Guide on Power BI Visuals: 20+ Types to Use in 2022.
What Is ETL?
Image Credits: Microsoft Docs
The Extract, Transform, and Load (ETL) is a process that 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 further and handle the data. It also acts as a single point for accurate and consistent data requirements of teams and businesses.
Businesses use ETL to integrate and migrate data from many databases into a single repository that has been appropriately organized and certified for analysis. ETL cleanses and organizes data using a set of business rules to meet business intelligence requirements, such as handling more complex analytics, enhancing back-end operations, and monthly reporting.
In this guide, we’ll be exploring Power BI Extract Transform Load mechanism that works the same as a traditional ETL mechanism. Both, Power BI Extract Transform Load mechanism and ETL mechanism consist of these three basic steps:
Extract
The extraction stage is the process of pulling or extracting broad data from its original data source. Using a variety of data connector alternatives, such as Excel, Azure, Microsoft Flow Databases, or Web Data, users can set up a connection for each data source as per their use.
Transform
The transformation stage may be divided into three discrete steps:
- Data Cleansing: It consists of data organizing processes like filtering rows, splitting columns, altering data types, and formatting.
- Data Integration: It consists of adding lookup keys, connecting tables, and aggregating data.
- Data Enrichment: It consists of creating calculated columns and dynamic tables.
Load
The loading stage is the final stage, where the transformed data is sent from the staging area to the target data warehouse. This step entails an initial load of all data, followed by recurrent loading of incremental data updates, and, less frequently, complete refreshes to wipe and replace data in the warehouse.
Hevo Data is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ Data Sources (including 30+ Free Data Sources) and will let you directly load data to a Data Warehouse to be visualized in a BI tool such as Power BI.
It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.
Get Started with Hevo for Free
Let’s look at some of the salient features of Hevo:
- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Scalable Infrastructure: Hevo has in-built integrations for 100’s of sources that can help you scale your data infrastructure as required.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-Day Free Trial!
What Is the Need for Power BI Extract Transform Load?
Having been briefed on the elementary steps of the Power BI Extract Transform Load mechanism, let’s look at the reasons you need Power BI ETL. Because we are simply making a copy of the data, the whole Power BI Extract Transform Load process does not and cannot modify or influence anything in the original data source.
However, when declining performance becomes a problem, we can examine the query to discover what is causing the slowdown and what modifications should be made. But, we must first ensure that the query is correct before working on increasing query performance.
The Power Query available in the Power BI does Power BI Extract Transform Load (ETL) to process the data and increase the performance. In this article, we will glance at how Power BI’s Power Query handles data processing using Power BI Extract Transform Load mechanism.
Understanding Power Query Power BI Extract Transform Load Mechanism
Power BI’s Power Query is a tool for data transformation and preparation. The Power Query comes along with an intuitive Graphical User Interface (GUI) and a Power Query Editor. The Power Query Power BI Extract Transform Load Interface enables you to obtain data from multiple sources, while the Power Query Editor allows us to perform transformations on these data.
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, Power Query Power BI Extract Transform Load 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.
The transformation tool in the Power Query contains a number of prebuilt transformation functions that can be accessed using the Power Query Editor’s Graphical Interface. Power Query Power BI Extract Transform Load interface cottages a broad range of transformations ranging from simple ones like column elimination, row filtering, first row as a table header, or advanced ones like merge, append, group by, pivot, and unpivot.
Dataflows
Power BI Extract Transform Load (ETL) Dataflow is a cloud-based platform that helps users in data preparation. 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.
The 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 on a set timetable, ensuring that data is constantly updated.
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 of the many available data sources.
The Power Query Formula Language is used to represent any type of data mashup (informally known as “M”). The M language 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
Source = Exchange.Contents("xyz@contoso.com"),
Mail1 = Source{[Name="Mail"]}[Data],
#"Expanded Sender" = Table.ExpandRecordColumn(Mail1, "Sender", {"Name"}, {"Name"}),
#"Filtered Rows" = Table.SelectRows(#"Expanded Sender", each ([HasAttachments] = true)),
#"Filtered Rows1" = Table.SelectRows(#"Filtered Rows", each ([Subject] = "sample files for email PQ test") and ([Folder Path] = "Inbox")),
#"Removed Other Columns" = Table.SelectColumns(#"Filtered Rows1",{"Attachments"}),
#"Expanded Attachments" = Table.ExpandTableColumn(#"Removed Other Columns", "Attachments", {"Name", "AttachmentContent"}, {"Name", "AttachmentContent"}),
#"Filtered Hidden Files1" = Table.SelectRows(#"Expanded Attachments", each [Attributes]?[Hidden]? <> true),
#"Invoke Custom Function1" = Table.AddColumn(#"Filtered Hidden Files1", "Transform File from Mail", each #"Transform File from Mail"([AttachmentContent])),
#"Removed Other Columns1" = Table.SelectColumns(#"Invoke Custom Function1", {"Transform File from Mail"}),
#"Expanded Table Column1" = Table.ExpandTableColumn(#"Removed Other Columns1", "Transform File from Mail", Table.ColumnNames(#"Transform File from Mail"(#"Sample File"))),
#"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}})
in
#"Changed Type"
Code Credits: Microsoft
Power Query Experiences
Power Query can connect to a variety of data sources, allowing users to work with the data they want. Currently, Microsoft offers two Power Query experiences:
- Power Query Online
- Power Query Desktop
Power Query Online
Image Source: Microsoft Docs
The following are the steps for getting data in Power Query Online:
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 information that is needed to create a connection to your data source is defined in the connection settings section.
- Connection credentials: The connection credentials necessary to establish a connection to your data source are made up of the privacy levels, credentials, and the rest of the connector-specific fields.
- Authentication: In Power Query, each and every connection must be authorized. Authentication techniques differ from connection to the connector.
Data Preview
The purpose of the data preview stage is 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
Query Editor
For Power Query Online, it is required to load the data into the Power Query editor, where users can further transform and enrich the query they choose.
Power Query For Desktop
Image Source: Microsoft Docs
The following are the stages for gathering data in the Power Query Desktop experience:
Connection Settings and Authentication
- Connection Settings: Most connectors require at least one parameter to create a connection to the data source. In conjunction with the connection, the connector parameters specify your data source path.
- Authentication: In Power Query, each and every connection must be authorized. Authentication techniques differ from connector to connector.
Data Preview
The purpose of the data preview stage is 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
Query Destination
In the Query destination stage, users can define where the query should be loaded. 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.
Conclusion
This comprehensive article is a complete guide on Power BI Extract Transform Load features used in Power BI’s Power Query. We learned about Power BI, ETL, Power Query, as well as Power Query experiences in Power BI Extract Transform Load mechanism. Today, worldwide, Power Query is employed by organizations to handle large volumes of data and analyze and extract meaningful insights to propel business operations to new heights.
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 at your disposal. Configuring and running your own ETL pipelines would require recurring assistance from your engineering teams, with a great deal of training for non-data teams 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. Hevo Data is a No-code platform that supports data ingestion from multiple sources be it your frequently used databases or SaaS applications like MySQL, PostgreSQL, Salesforce, Mailchimp, Asana, Trello, Zendesk, and other 100+ Data Sources. Hevo migrates your data to a secure central repository like a Data Warehouse in minutes with just a few simple clicks.
Visit our Website to Explore Hevo
Hevo lets you migrate your data from your favorite applications to any Data Warehouse of your choice like Amazon Redshift, Snowflake, Google BigQuery, or Firebolt, within minutes to be analyzed in Power BI. The best part – you don’t need to conduct extensive training for your non-data teams. Hevo can set up and run your data pipelines using pre-configured connectors with ease within a few minutes.
Why not try Hevo and the action for yourself? Sign Up here for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also check out our pricing plans to choose the best-matched plan for your business needs.
Share your experiences after reading our comprehensive guide on Power BI Extract Transform Load mechanism in the comment section below! We would love to hear your thoughts.