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Google Data Studio vs Power BI


What is Power BI?

Power BI is one of the popular business intelligence tools developed by Microsoft Corporation to offer various data modeling capabilities like data preparations, data visualizations, data discovery, and generating interactive data analytic dashboards. With the help of Power BI users can make powerful business-related decisions. This Power BI tool helps users to pull the data using various formats such as images, excel sheets, spreadsheets, and videos. The Power BI tool also helps to centralize the database management system, and you can also visualize the data model on it.

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What is Google data studio?

Google data studio is an easy-to-use tool that is used to create a beautiful data visualization within a minute. This is a cloud-based application that can be run on the various top web browsers like chrome, safari, and firefox. This type of tool is able to deliver stunning, and insightful reports without much action from the user. Google data studio also comes up with a free-to-use license for personal or commercial use. With the help of Google data studio dashboard, you can access various types of charts such as charts, bars, pie, lines, Areas, scatter, etc, and this also adds up value to the data reports.

Comparison of Power BI and Google data studio:

Here we are going to explain the major comparison between power BI and Google data studio based on a few functionalities. Let’s start it;

Installation:

Power BI tool has a number of ways to install or configure it, and it all depends on the license. The basic, and very best way to install Power BI as a desktop application on your desktop. Power BI application is quick and straightforward so all you need is just a modern PC with 4GB of RAM (ideally 8GB of RAM) and running windows 8 operating system version or later. Power BI is a Microsoft product, and it is not available for MAC operating systems. 

Google data studio is completely web-based, so you do not need to install it on your computer/pc. Directly you can access them by logging into the Google page like other Google products. 

Data source:

Power BI consists of various pre-built connectors, and they enable you to connect to a wide variety and ever-expanding data sources. PowerBI’s web connectors help you to import data from almost any data source that includes Amazon, Google, and other Microsoft competitors.

Whereas Google data Studio data connectors are much smaller and offer only 18 connectors that are built by Google. inc itself. There are also 400+ connectors built by Data studio partners. These types of connectors you get to see only for advertising, media, and content-related websites.

Data transformation:

Power BI offers a full range of data transformations by using a power Query engine. Power Query lets you perform ETL (Extract, transfer, and loading) operations on the data which you have and prepare it for visualization. Users can also make use of data analysis expression (DAX) to build formulas and expressions. Power BI has a lot of capabilities to perform data transmission operations. 

Whereas Google data studio lags behind this feature. In Google data studio, the only way to perform data transformation is by using JOIN keys. So by default google assumes that you already have clean data for the visualization.

Ease of use:

If you are familiar with MS Excel, then Power BI would be a great choice.  But to access power BI, some sort of struggle is required to connect and integrate with different data sources. Power BI has also offered better functionalities than the Google data studio and also helps you with a drag-and-drop feature. 

Whereas Google data studio is very easy to navigate and the learning curve is minimal. The user interface of this platform is extremely intuitive as long as you have clean datasets. Only one thing you need to concentrate on is a drag-and-drop feature that helps you to create quick reports.

Sharing:

In Power BI report sharing is a complex task that is compared to the Google data studio. The biggest concern is that it is impossible to share a report directly from the desktop applications. Every time you need to publish the reports in the Power BI services. However mobile applications let you share a dashboard or report directly from the application.

In power BI you can share your reports and dashboards with internal and external collaborators. 

On the other hand, sharing data with google data studio is an easier task. With the help of email or links, you can share the reports in the Google data studio. In google, the recipients dont need to pay any subscription fees because Google offers a free google data studio tool.

Mobile compatibility:

Power BI consists of the native applications for Android, Windows, and IOS smartphones. In Power BI smart mobile application, you can perform the below vital tasks:

  • Visualizing data reports
  • Filtering of the data from a report.
  • Adding notes to the reports.
  • Getting a notification and setting alerts.
  • Exploring on-premise data reports.

Whereas Google data studio does not have a mobile application version so that users can visualize the data reports using the mobile browser, editing the report could be a challenging task. 

Community and online resources:

We already know that Microsoft Power BI has a huge knowledge base community to learn and resolve the issues about the platform usage. When you login into the communities, you will get to see a lot of content which are related to the platform guide. For example, themes gallery, custom visual studios, data stories gallery, developer, etc.

Similarly, Google data studio also has robust communities. Here the community gallery section of this tool gives you access to various third-party visualization widgets. 

Cost:

Power BI desktop is free but can be accessed only by beginners who want to know the basic things about the tool. For the business purpose, you need to share a desktop, and the cost it will going to charge is $9.99 per user/per month (premium subscription). Power BI Pro is a cloud-based application that can be used for collaboration, sharing, publishing, etc. If you are an expert, and you want power BI for both the on-premise and cloud-based reporting, support for big data and analytics, dedicated cloud computes, and storage resources you can opt for Power BI premium at $4995/year. 

Whereas the Google data studio is free to use application. And all you need is just a Google account.

Functionalities:

Power BI tool is available for both beginners and advanced level experts due to its tons of data manipulation features. It also helps you to access data through the data visualization elements like charts, bars, tables, graphs, etc. one more functionality of the Power BI is, the tool gets refreshed itself  8 times a day.

Whereas Google data studio comes up with fewer features when compared to the Google data studio as it is only a data visualization application. However, its in-memory BI engine is a powerful feature that helps users to eliminate the data visualization lags, especially used for the larger number of data sets. The Google data studio application gets refreshed itself every 12 hours.

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Pros and Cons of the Power BI:

First, let’s start with the Power BI:

Advantages of the Power BI:

Below are the primary advantages of the Power BI:

Offers easy data exploration and data visualization:
This is the world of data exploding, this tool offers the ability to explore valuable data and also perform data visualization tasks to get greater results. When compared with other business intelligence tools, I think this is an awesome tool in the data visualization process.

Acts as a managed self-service Business intelligence tool:
This Microsoft business intelligence tool provides an effective self-service business intelligence tool. The MSBI also acts as a Microsoft Excel that is used by everyone in their day-to-day activities to produce and report the data analytics.

This tool makes use of Native MS excel features:
The MSBI tool makes use of Microsoft Excel features to the core in order to produce effective data analysis. By using excel Microsoft excel features it’s very easy to collect data from multiple data sources.

MSBI tool supports Web service applications:
MSBI tool works well with programming languages like .NET and SQL database servers to build an effective web service application and also offers abundant benefits to the clients.

End-to-end Business solutions:
MSBI provides you with a great business solution for your organization and enables users to make effective business decisions. This tool offers entire top-to-bottom business solutions.

Data warehouse applications:
Business intelligence tools offer greater data analytical solutions. You can collect the data warehouse from various sources. This type of warehousing is more suitable to extract the information to carry out the data analytical task effectively.

Limitations of the Microsoft Power BI:

Below are the few drawbacks of the Microsoft Power BI:

  • This tool is very difficult to implement. You need to loop them in the development team, the IT team to get it executed.
  • To implement row-level security in Power BI and tie your web application users with Power BI users.
  • With users coming and leaving an organization, it becomes a nightmare to manage.
  • Requires considerable investment.
  • You need to buy a premium capacity.
  • Not feasible for pro users.
  • Still users need to visit your web page and the information is not delivered to them.

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Advantages of the Google data studio:

Below are the few benefits of the Google data studio;

It’s a free tool to use:
If you have a Google account, then it’s free to access the tool.

Offers customized report dashboard:
 Google data studio tool allows users to customize the report dashboards with a wide range of text formatting options available such as designs, Styles, graphs, and text headers. If you have new raw data, that will be customized on the dashboard based on your requirement with the help of this pre-built text formatting feature.

Here are some of the things you can format the data:

  • Page layout
  • Text formats 
  • Graphs 
  • Metrics
  • Style element

Offers modern and interactive data visualization:

In Google data studio with the help of a few clicks, you can easily create an engaging and visually appealing dashboard that will attract your audience for sure.

Automated report sharing dashboards:
Google data studio allows you to build an automated report sharing dashboard with the help of real-time data, this process saves your time while reporting.

Offers shareable dashboards:
Google data studio offers a sharing dashboard with the users for multiple tasks. This feature also helps you to communicate among peers and business partners.

You can share the report with the help of these things:

  • Link sharing 
  • Email permissions.
  • Schedules email deliveries.

Limitations of the Google data studio:

Here are the few drawbacks of the Google data studio:

  • Lack of real-time updates with respect to the dashboard.
  • Slow speed in case of live connections.
  • There is no on-premise deployment option available.
  • Lack of native data connectors availability.
  • A limited number of charts per dashboard report
  • Limited interaction with the elements.

Which is the best tool between Power BI and Google data studio?

Here we come to the conclusion part of the article, you can make use of Google data studio sources such as Google data analytics with the data that is readily usable, no need for sharing any complex information. Google data studio tool is a good fit for quick reporting. Most importantly it is a free tool to use unless you need some data connectors.

On the other hand, we have Power BI, this tool is used if you have a wide variety of data transformation options, all you need is a BI tool that has advanced features like greater data transformation capabilities, customization options, and if you don’t mind in paying. Power BI is a powerful robust data visualization and analytical tool, it requires some sort of excel knowledge to perform Power BI dashboards.

You can choose the tool as per your requirements, and data connectivity sources.

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Final thought:

We tried to clear all our audiences’ doubts regarding choosing the best tool between Google data studio and power BI. Both the tools are scalable and robust to use and configure. In this Power BI VS google data studio post, we have mentioned major differences based on the different categories, pros, and cons of the tools.

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PowerBI with SQL server | Guide On PowerBI with SQL server


PowerBI with SQL Server- Table of Content

What is Power BI?

Power BI is indeed a set of software tools, applications, and interfaces which works together to transform disparate data sources into logical, visualizations, and engaging information. The information could be in the form of an Excel spreadsheet or even a set of blended data warehouses which are both cloud-based and on-premises. Power BI makes it simple to link to various data sources, display and uncover what matters, and share your findings with whomever you choose.

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Power BI’s elements:

Beginning with the three fundamentals, Power BI is made up of various components working together:

  • Power BI Desktop for Windows is a desktop app.
  • The Power BI service is a cloud-based SaaS (Software as a Service) application.
  • In iOS, Windows, & Android smartphones, there are Power BI mobile apps.

Power BI Desktop, the platform, as well as the mobile applications, are all intended to help you generate, distribute, and utilize market intelligence in the most efficient way possible.Apart from them, Power BI has two more characteristics: the Power BI Report Builder, which allows you to create paginated reports that you can distribute through the Power BI service, and the Power BI Data Warehouse. On-premises data server powered by Power BI. After developing your Power BI reports in Power BI Desktop, you may upload them to Power BI Report Server, a report server.Power BI's elements

The Benefits of Power BI

Power BI has a number of features that set it apart from other analytical tools:

  • It has both a cloud-based and a desktop interface.
  • Data warehousing, analytic tools, and interactive dashboards are some of the features available.
  • Customized visualizations can be loaded.
  • It’s simple to scale throughout the entire company.

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What is SQL Server?

SQL (Structured Query Language) is a crucial skill in the field of data science, and it’s quite simple to master. Most people, however, overlook the fact that SQL is more than just generating queries, but that’s only the first step. It’s a different matter to ensure that inquiries are efficient or even that they match the environment in which you’re working.

Microsoft’s SQL Server is a relational database management system (RDBMS). It was created largely to compete with the MySQL and Oracle databases. The standard SQL language is supported by SQL Server. T-SQL, SQL Server’s own version of a SQL language, is included (Transact-SQL).  T-SQL stands for Transact-SQL, a Microsoft proprietary language. It also has the ability to declare variables, handle exceptions, and run stored procedures, among other things. Another important interface tool for SQL Server is SQL Server Management Studio (SSMS), which works in both 32-bit and 64-bit settings.

SQL Server helps to run several services at the same time, each with its own ports, logins, databases, and so on. These are separated into two categories:

  • Primary Instance.
  • Instances with a Name

There are two methods for gaining access to the primary instance. We can start with the server name. We can also utilize its IP address. By attaching a backslash and the instance name, you can access named instances.

Is SQL important for Power BI?

Power BI Desktop’s ability to conduct T-SQL queries gives you useful resources for retrieving SQL Server data. Perhaps you’ll be able to get the most out of the SQL Server query engine, and you’ll also be able to reduce the amount of the datasets are imported into Power BI Desktop and the number of modifications you have to do.

To take this technique, you’ll need to know how to utilize T-SQL, and if you do or can find someone who does, you’ll have a lot of flexibility when dealing with SQL Server data. Even if you extract data table by table, you still have a variety of powerful tools for interacting with SQL Server data & organizing it for use in Power BI visualizations. Whereas the Power BI service allows you to retrieve data from a variety of sources, it will not have a SQL Server data connector. One could export SQL Server files and then import them into the service, but you can’t connect immediately from service to SQL Server, regardless of the fact that SQL Server seems to play a crucial role in many organizations today. The more you know about importing and transforming SQL Server data in Power BI Desktop, the better.

Prerequisites to connect Power BI To SQL Server

  • Before you begin, join up for the free trial of Power BI, in case you do not even already have one.
  • On the local computer, install the Power BI Desktop.
  • Download SQL Server within a local computer and recover a backup of the demo database.
  • In the local computer as SQL Server, install the data gateway.
  • Consult a network administrator in the organization if you aren’t a network administrator and wouldn’t want to set up a gateway yourself. They’ll be capable of creating the data source configuration needed to link your dataset to the SQL Server database. Import Data connectivity mode must be used if you’re going to employ a saved procedure.

Step by Step approach to Connect Power BI to SQL Server

1. Power BI Desktop and installation

Power BI Desktop seems to be a free tool that you can download and install on your computer. It may link to a variety of data sources. Normally, an analytical project starts with report development in Power BI Desktop. The data is subsequently uploaded to the Power BI service, where it may be associated with the Power BI Mobile applications, allowing users to access the reports on their mobile devices.

Power BI Desktop and installation

Installation

Power BI is only available on Windows computers. Users on Macs may either create a Windows VM on Azure and install Power BI there, including using Turbo.net, that could broadcast Power BI instantly towards the Mac from either the cloud.

There are two ways to use Power BI:

  • We can download this from the Microsoft Store as an application and simply login in and get going. This would be the device’s digital equivalent.
  • We often have to operate with the information in offline mode. In these kinds of cases, download the software first rather than install it locally. Take the time to read through the entire installation guide.

Download the application to the system, based on the products you’ve chosen. Check the installation via selecting the Power BI Symbol after approving the license agreement. If the screen below shows, you’re ready to begin.

Power BI Symbol

2. Workspace

Let’s have a look at how Power BI Desktop works now. We’ll have a look at it in another part to get an idea of how it works.

The graphic below depicts the key characteristics of Power BI’s workspace..

Workspace

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3. Data Source

A variety of data sources could be connected to Power BI. The Get Data button toggles all of the data sources that may be used to import data into Power BI.

Data Source

4. Connecting to SQL Server

Let’s look at an example of connecting a SQL server dataset to Power BI Desktop and using that to examine the data.

Database Example

We’ll have to use a publically available SQL Server installation on AWS for the demonstration, as well as a database built from the Superstore dataset.

Our goal as Data Analysts would be to assess the data and identify significant areas of concern inside this hypothetical firm. 

Getting into the sample database

SQL Server Management Studio should be installed (SSMS). SSMS seems to be a free and fully integrated management environment for every SQL system. SSMS allows you to install, manage, and modify the data-tier components that your applications rely on, and also create inquiries and workflows.

After you’ve downloaded and installed the software, you’ll see the page below, which will prompt you for particular credentials.

Connecting to SQL Server

1. Insert the given information:

Server Name: ec2-52-14-205-70.us-east-2.compute.amazonaws.com

Authentication: SQL Server Authentication

Login: SQL

Password: SQL

The ‘SuperStoreUS’ database will indeed be accessible as a ‘Read Only’ user. As our sample, the instance is ec2–52–14–205–70.us-east

2. compute.amazonaws.com, the databases are SuperstoreUS and Test, and the tables inside the SuperstoreUS database are Customers, Orders, and so on. As a result, there could be lots of instances, each of which can have multiple databases, each of which would contain various tables.

SQL Server Authentication

5. Importing SQL data into Power BI

The data is organized into searches by Power BI Desktop. It implies that all of the information is organized in a table-like format.

Making the connection

Proceed to the start button by opening Power BI Desktop. The Get Data tab pane gives you a number of options when it comes to sources of data that could be linked to the Power BI Desktop. We’ll establish a connection to the SQL Server.

connection to the SQL Server

When you select SQL Server, a fresh screen will appear, asking again for the server with which we wish to connect our Power BI Desktop. Fill in the blanks, and you’re finished. We could now inspect the details of the selected table by clicking on it. For uploading a certain table, merely check the box beside it click the load button.

SQL Server

Modes of Data Connectivity

Power BI Desktop may link to a SQL database in two distinct ways, both of which display on the homepage.

Import

The import technique, as the title suggests, ‘imports’ the chosen tables onto Power BI Desktop. The imported data is therefore used by Power BI to create a visualisation or perform any adjustments. We must reload the data to view any modifications in the data, which re-imports the full data set.

DirectQuery

No information is incorporated or transferred to Power BI Desktop if DirectQuery is selected as an option. Power BI Desktop searches the fundamental data source while we generate or process the information through visualisations, ensuring that we’re still dealing with the most recent data. Nevertheless, unlike the importing method, this approach has restricted data processing capabilities.

Power BI Desktop

6. Query Editor

All of the chosen tables would be imported into Power BI Desktop & displayed in the Data view as distinct datasets. .

Query Editor

We can make changes to our datasets from here. We’ll use the Query Editor to accomplish this. The query generator could be used to edit datasets regardless of the data source. The query editor allows us to perform operations such as rebranding a dataset, eliminating a single or several columns, and so on.

rebranding a dataset

The number of functional inquiries is displayed on the left pane, while the Query Settings pane on the right provides all of the stages related with a query.

Advanced  Editor

The script for the inquiry that has been run against the source of data is displayed in the Advanced Editor. The syntax is M, which stands for Power Query Formula Language. It is also possible to write your own code.

Make a backup of your work

Choose Close & Apply from the Query Editor’s File menu once you’ve made the appropriate data changes in the query editor. The adjustments will be applied to the data in Power BI Desktop.

data in Power BI Desktop

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7. Merging Datasets

When we wish to integrate one or maybe more datasets into one, merging datasets proves useful. The Query Editor can also assist with this merger. We’ll pull multiple tables(3) from the SuperstoreUS database this time.  Customers, Orders, and Returns are the three tables.

Merging Datasets

The three specified tables are listed in the Fields column. Now, go to the Edit Queries option and click the Combine option from the dropdown menu, which will reveal the Merge option. We’ll use the merged existing queries to construct a new query.

Merge option

We pick the tables to be combined as well as the kind of join we need in the Merge dialogue box. Columns that are similar to both tables must be chosen.

Let’s start by creating a new query called Orders and Customers. Through this query, we’ll join the Orders and Customers tables. All of the key columns from the orders table, as well as correlation columns from either the customer’s table, were included in the new query. Except for the last one, which reflects the Customers query, eliminate all the correlation columns. Next, from the Customers query, choose columns to merge into the newly merged query.

Customers query

The merged query is composed of a single database that has all of the required columns. Rather than operating with several data sources, which might cause confusion, you could now easily interact with one single database.

8. Building & Publishing a Dashboard

We may begin the Dashboard construction process since we had the dataset prepared along with all the alterations completed. The Power BI dashboard often referred to as a canvas, is a single sheet that contains multiple visualizations that serve to convey a narrative. The reports’ visuals, known as tiles, are attached to the dashboard.

Now just to see what recommendations we can glean from the superstore data set.

  • States seeking profit
  • Profit & Sales by Segment

Building & Publishing a Dashboard

Region-by-Region Sales and Profit

Sub-Category Sales

Profits by location

Quantity

We’ll obtain a dashboard that looks like the one below after we’ve formatted the size, look, and colour.

dashboard

Publishing
Data would be only valuable if it can be transferred across individuals or groups. By publishing the Dashboard or reports to the Power BI Service, they can be shared with others. The Dashboards/Reports can then be seen or interacted with using the Power BI Apps.

Publishing

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Conclusion:

When SQL and Power BI are combined, data analysis is carried out properly. We can integrate SQL Server to Power BI with ease and retrieve information right into it. Customers can switch connections with a single click in Power BI to execute in-memory searches to a bigger dataset. SQL is a handy tool that, when combined with Power BI’s expertise, may assist in monitoring and reporting more effectively and informative.

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