For reference, Power BI is a data visualization and analytics software developed by Microsoft. Power BI can be used for static as well as interactive data visualization. Before you can create actual visualizations with Power BI, you can perform data preprocessing using Power BI Query editor.Read More
Microsoft’s Power BI is a data analytics tool that can be used for interactive data visualization, using data from various sources such as relational and NoSQL databases, flat files, web servers, etc.Read More
For starters, Microsoft Power BI is one of the most commonly used data analytics and visualization tools. With its help, you can import and visualize data from various sources, such as text and CSV files, relational databases, web servers, etc. Power BI comes in two flavors: Desktop and Cloud-Based Service.Read More
- How to create a table.
- How to create a Power BI pie chart.
- Creating a line chart in Power BI.
- Report creation.
The final article will take us through Power BI Visualizations and how to create them using the data sets created previously in these series. The walkthrough will cover 3 basic Visualizations: Table, Pie Chart, and Line Chart. These will then combine to create a basic report/dashboard within Power BI.Read More
- Importing data from Excel, SQL Server, and the Web.
- Data Manipulation Basics in Power BI.
- Managing relationships within Power BI.
This article will cover off importing data, manipulating data, and finally managing relationships within Power BI. Again, like the previous article, this one will focus on the basic principles of using Power BI, as opposed to an in-depth walkthrough.Read More
This article will help you get to grips with the basics of Power BI and what it has to offer as an analytics tool. Along the way, we’ll cover the following: what Power BI is; why we need Power BI; Excel Vs Power BI; and, finally, installing Power BI.Read More
Aggregate and Analytic functions in SQL Server operate on a set of rows. However, unlike such aggregate functions as sum, count and average that return scalar values, analytic functions return a group of rows that can be further analyzed. In this article, we will see some of the most commonly used analytic functions in SQL server. We will be discussing the following functions:
Suppose you are designing an SQL Server database application for a company’s CEO and you have to display the fifth most highly paid employee in the company.
What would you do? Read More
In this article, we will introduce SQL transformations in action. We will also see how to handle missing values in our dataset.
Consider a scenario of a movie rating dataset containing records of different movies along with the average user ratings associated with each movie. The ratings are in numeric form ranging from 1 to 10 with 1 as the lowest rating and, respectively, 10 as the highest rating (though no movie in the history has achieved 10 rating:). Suppose that we want to convert the numeric ratings into categorical ratings. For instance, we want to replace ratings of 1-3 with the categorical value “poor”, of 4-6 with “average” while ratings of 7-10 will have the value “good”. We can accomplish it with SQL transformation in Azure ML Studio. Read More
Security is one of the most important requirements for a data-driven system. Encryption is one of the ways to secure the data. Wikipedia defines encryption as:
“Encryption is the process of encoding a message or information in such a way that only authorized parties can access it and those who are not authorized cannot.”
In SQL Server 2016, Microsoft introduced an encryption feature called Always Encrypted. In this article, we will see what Always Encrypted is, and how it can be used to encrypt and decrypt data, with the help of simple examples.