Power BI is a Business Intelligence (BI) tool and a Data Visualization software from Microsoft used by organizations to transform the way Data Analytics is used to solve business problems. Power BI offers real-time high-level analytics, extensive modeling, and custom development which makes working with data really easy.
However, ingesting, transforming, and loading data from various sources into Power BI has always been challenging. This article is aimed at helping you with Power BI Data Ingestion.
Data has always been on a rise, the amount of data produced every day is truly staggering. And because of this, it has become seemingly difficult to collect, process, and store big or complex datasets. Thankfully, Power BI Desktop gives its users the ability to ingest, transform, and load data from various sources into internal tables in Power BI, which can then be used as a source for Power BI visualizations. Let’s get started with Power BI Data Ingestion.
Table of Contents
- What is Power BI?
- What is Data Ingestion?
- Steps for Incorporating Power BI Data Ingestion
What is Power BI?
Power BI is a Business Intelligence (BI) tool and a Data Visualization platform offered by Microsoft that allows organizations to solve business problems by analyzing business data and generating custom reports. Power BI comes with a set of built-in tools, apps, and connectors that can work with data to generate actionable insights, immersive visuals, and interactive reports.
Power BI is self-service Business Intelligence which means that you can easily aggregate data, analyze data, visualize data, and produce some fantastic-looking visual reports. Power BI allows you to pull data from multiple sources such as Oracle, SAP, or a Data Warehouse of your choice. It can handle everything from your simple Excel file all the way to massive amounts of data.
Key Features of Power BI
Let’s discuss some of its key features responsible for the immense popularity of Power BI.
- Easy Integrations: Power BI offers integrations with multiple connectors that allow users to fetch data from various Data Sources.
- AI Support: Power BI allows users to deploy Artificial Intelligence (AI) techniques such as Image Recognition and Text Analytics to prepare data, develop Machine Learning models, and quickly extract actionable insights from structured and unstructured data.
- Report Sharing: Power BI is built for developing security that allows teams to share access in a moderated and controlled manner. Users can easily share their reports with other team members without compromising data security.
- Real-Time Dashboards: Power BI has the capability to display real-time data and visuals in any report or dashboard. Power BI dashboards update in real-time allowing users to instantly solve issues and discover opportunities.
- Customized Visualization: Power BI is highly customizable and allows users to leverage its custom visualization library to create visualizations as per their requirements. In addition to that, Data Analysts can also generate highly customizable visuals for their Power BI report by using open-source data-viz modules from R and Python.
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What is Data Ingestion?
There’s a tremendous amount of data coming from disparate sources, it’s coming from your Website, it’s coming from your Mobile Application, REST Services, External Queues, and it’s even coming from your own Business Systems. Data needs to be collected and stored securely without data losses and with the lowest possible latency. This is where Data Ingestion comes in.
Data Ingestion refers to the process of collecting and storing mostly unstructured sets of data from multiple Data Sources for further analysis. This data can be real-time or integrated into batches. Real-time data is ingested on arrival, whereas batch data is ingested in chunks at regular intervals. There are basically 3 different layers of Data Ingestion.
- Data Collection Layer: This layer of the Data Ingestion process decides how the data is collected from resources to build the Data Pipeline.
- Data Processing Layer: This layer of the Data Ingestion process decides how the data is getting processed which further helps in building a complete Data Pipeline.
- Data Storage Layer: The primary focus of the Data Storage Layer is on how to store the data. This layer is mainly used to store huge amounts of real-time data which is already getting processed from the Data Processing Layer.
Now that you’re familiar with Power BI and Data Ingestion, let’s dive straight into Power BI Data Ingestion.
Steps for Incorporating Power BI Data Ingestion
Before the data is available for reporting in Power BI, it needs to go through the processes of Power BI Data Ingestion, Transformation, and Loading. Data is extracted from a Data Source, it is then transformed, standardized, validated, and ultimately loaded into Power BI for analysis and reporting. The challenges in Data Ingestion? You’re bound to face Data Integrity issues such as fragmented and incomplete data, complex system integration, structural inconsistency, etc. On top of that, Power BI Data Ingestion demands a high and advanced skillset.
Follow the below-mentioned steps to get started with Power BI Data Ingestion.
Before getting started with Power BI Data Ingestion, it is necessary to know about the Data Sources supported by Power BI. Power BI Desktop allows you to connect to and import data from best-known Databases and Data Sources using various file formats. Check out Power BI Data Sources for a full list of available Data Sources. After importing data, you then need to work on cleaning and transforming the data for further analysis and reporting.
Click on the “Get data” button to see the most common data types menu.
The “Get Data” dialog box categorizes the data types in the following way.
- File: Excel, CSV, XML, JSON, etc.
- Database: SQL Server, Oracle, MySQL, etc.
- Power Platform: Power BI datasets, Power BI dataflows, Dataverse, etc.
- Azure: MS Azure SQL Database, MS Azure Marketplace, etc.
- Online Services: Google Analytics, Zendesk, Asana, GitHub, Marketo, etc.
- Other: Web, R script, Python script, Spark, Google Sheets, etc.
Power BI Desktop and Power Query allow users to automate the process of Power BI Data Ingestion. Let’s discuss an example to understand this better. Before proceeding, make sure you’ve Power BI Desktop installed on your system. For the purposes of this demonstration, the General Aviation 2013 Excel dataset file (available for public use from Data.gov) has been used. Download the Excel file and save it in a local folder.
Follow the below-mentioned steps to load data into Power BI.
- Launch Power BI Desktop and click on the “Get Data” button from the Power BI toolbar. Now, choose the Excel connector and click on “Connect”.
- Locate the General Aviation Excel file you just downloaded and click on “Open”.
- Now, the “Navigator” dialog box will appear. Select “Data_GA” and click on “Edit”.
The Power Query Editor opens to shape and transform the data. You can now edit loadable tables and create calculated columns and measures based on their columns. This is necessary in order to prepare and structurize the information for Power BI Data Ingestion.
As you can see, the Power Query Editor displays the list of Applied Steps in the “Query Settings” pane located on the right. “Source”, “Navigation”, and “Changed Type” steps were automatically applied to indicate the source path, discovered columns, and detected data types respectively.
These steps will accurately do the job for you in cases where the data is presented in a table with appropriate headers and no empty rows or hull values. However, in some cases, the source data/file may need some cleaning up. The next few steps will help you in shaping and transforming the data.
Set Appropriate Column Headers
In this dataset, the Column Headers are actually in the second row, hence, the first row needs to be removed. Click on “Remove Rows” from the Power Query Editor toolbar. Then, click on “Remove Top Rows” and enter “1” in the “Number of rows” dialog box to remove the first row.
After removing that row, click on the “Use First Row as Headers” option to use the new first row as the Column Headers.
Now, the table is displaying the correct Column Headers.
Remove Redundant Columns
Here, you can remove all the columns that are not required for your analysis. Select the columns that you wish to remove while pressing the CTRL-key. Then, right-click and click on “Remove Columns” to eliminate columns.
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Check out what makes Hevo amazing:
- 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 in a BI tool such as Power BI.
- 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 sources that can help you scale your data infrastructure as required.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Replace Null Values
Standardization is an important step to ensure accurate analysis. Here, you need to replace the Null values with the number “0” to maintain consistency.
In this example, the null values in the “inj_tot_f” and “inj_tot_s” columns can be replaced with “0”. Right-click on any row with a null value and click on “Replace Values…”. Then, enter “0” in the “Replace With” field and click on “OK” to replace the values.
You can follow the same procedure for any remaining column with Null values.
As you can see, the above data shaping and transformation steps have been recorded in the “Applied Steps” pane. You can refresh your Power BI file for the changes to be reflected.
You can follow these steps to streamline the process of Power BI Data Ingestion.
Power BI is all about Data Analytics, Data Visualization, and Business Intelligence. Using the Power BI functionalities for ingesting, transforming, and visualizing data is a smooth experience. This article introduced you to Power BI and helped you understand the process of Power BI Data Ingestion.
With the right amount of tools and knowledge, Power BI can be used to extract useful analytics from a diverse range of Data Sources. However, as your business scales, extracting complex data from a diverse set of Data Sources can be a challenging task. This is where Hevo comes in.visit our website to explore hevo
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Share your experience of understanding Power BI Data Ingestion in the comments section below.