Microsoft Business Intelligence Tools For Stand Out Experts Pdf Download And Install – Are you looking for cutting-edge business intelligence and data visualization tools? Microsoft Power BI and Google Data Studio are gaining a lot of momentum these days. Both are one-of-a-kind, where one lets you create customized charts and reports in seconds, while the other offers interactive charts with powerful business intelligence capabilities. The following post by our SharePoint development team provides a brief comparison of Google Data Studio vs Power BI
Ease of creating data visuals – User does not need to be an expert in manipulating charts.
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Google Data Studio is now available for free to all visitors. You need a Google account to get started. To get started, navigate to datastudio.google.com and create your first dashboard.
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1. Power BI Pro: Individual users can purchase Power BI Pro to access real-time, self-service insights that enable them to view data and exchange ideas with live real-time dashboards across their business. This package is USD 9.99 per member per month.
2. Power BI Premium per user: Individual users can license Power BI Premium to accelerate the introduction of insights with advanced artificial intelligence, enable self-service data preparation for large databases, and streamline data processing and use at the corporate level. This package will cost you USD 20 per member per month.
3. Power BI Premium Per Capacity: This is comparable to Power BI Premium per account, no user limit. This subscription is priced at USD 4,995 per month and is recommended for enterprises with a large number of Power BI users.
Power BI integrates with a variety of Microsoft technologies, including Azure, SharePoint, MS Office, and Excel, as well as marketing apps like Google Analytics, Salesforce, Zendesk, Marketo, and Meta. Additionally, you can link to databases, CSV files, and other types of files. Furthermore, you may extract information from the Website.
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Google Data Studio connects with many other Google services, including Analytics, Ads, BigQuery, and Sheets. However, it doesn’t inherently handle a large number of different cloud-based inputs, which is one of the reasons people look for Data Studio options. Google allows third-party developers to create connections, but these are often paid services.
Because Data Studio is a Google product, it is pre-integrated with a variety of Google products, including Google Analytics, Google Ads, Google Sheets, BigQuery, and cloud storage. Additionally, more than 400 partner connectors are available to connect to data sources such as Adobe Analytics, JASON, and Facebook. A nice feature of Data Studio Connections is that you can create your own if you can’t find one that fits your needs. Note that unlike many other business intelligence products, Google Data Studio supports both SQL and noSQL data sources.
Many of you may be wondering: Can I combine data from multiple sources to create charts and figures on a dashboard? Yes, it is. Google Data Studio has a feature called Blending, which is essentially a left-external merge that lets you create visualizations using data from multiple sources. You can combine up to four different data sources. To link data sources, each source must follow a number of dimensions referred to as a join key. It is important to note that this process only happens at the report level; They do not appear on the Data Sources home page.
Power BI integrates with over 100 data sources, including SQL and NoSQL. In summary, we can categorize Power BI data sources into two categories:
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1. Files: Files are used to represent many data sources, including PDF, Excel, JSON, Google Analytics, and Adobe Analytics.
2. Databases: Databases are often used to refer to SQL-based data sources such as BigQuery, Amazon Redshift, MySQL, and Azure Database.
To integrate data sources into Microsoft Power BI, you can use the “Add Queries” or “Merge Queries” tools. Now, let’s compare and contrast append and merge queries.
Adding rows from one query to another creates a new request that contains all the rows from the first query triaged through all the rows from the second query. At least two requests are required for the affiliate process. This is similar to the outer join operation in a SQL database. In contrast, a merge query generates a new request from two existing ones. A single query result contains all the columns from the primary table, with one column acting as a relationship column for the secondary table. An associated table contains all rows related to each row in the main table by the shared column value. This is similar to a left join in a SQL database. Power BI is very good at exploring data relationships.
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Unlike other business intelligence and data analytics tools on the market, Google Data Studio lacks significant predictive analytics features such as categorical regression or time series forecasting. Therefore, the lack of this capability prevents Google Data Studio from being a useful tool for businesses with complex use cases, including forecasting and forecasting.
The program does not support Python or R natively. However, users can combine R and Python with Data Studio using third-party tools such as Panoply. Additionally, you can link Data Studio to an updated Google Sheet using Python. This approach is best suited for small datasets. You can use BigQuery to work with large datasets.
The report can be embedded in any website or application that accepts the HTML iframe tag. An iframe code containing a link to your analysis is generated instantly by Data Studio. No HTML skills required. The embedded report is shown in the preview pane (viewers are not allowed to change the report).
Power BI offers a variety of analytical capabilities for a variety of use cases. Following are some of the prominent ones:
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To use Power BI effectively, people must understand the basics of extraction, transformation and loading or ETL process. When dealing with the M language with PowerQuery, ETL is critical.
Additionally, you should be familiar with the DAX language for statistics and metrics as well as PowerPivot for data modeling. In summary, you’ll need instruction to master using Power BI.
In contrast, Data Studio is a technology that requires no programming. If you have a working knowledge of spreadsheets, tallies, and graphs, you’re ready to get started. When creating expert data visualizations, you can connect a data source and drag widgets to the board.
Sharing data with Power BI is a time-consuming process. You cannot share a document directly from the desktop application. You must publish the report using the Power BI publishing feature. Mobile applications, on the other hand, allow you to share a summary or report right from the application.
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You can work with external or internal collaborators on Power BI reports and dashboards. To view such files, the receiver must have a Premium Per User (PPU) or Power BI Pro subscription.
There are approx. 110 data connectors divided into 6 different categories.1. File: Excel, CSV, XML, etc.2. Database: SQL, Access, Teradata, SAP, etc.3. Power BI: Power BI Dataset and Power BI Data Flow.4. Azure: Azure SQL Database, Azure SQL Data Warehouse, Azure Blob Storage etc.5. Online Services: Dynamic 365, SharePoint Online, Salesforce, Google Analytics etc.6. Other: Microsoft Exchange, ODBC, OLEDB, Spark, R Script, etc.
Currently, approx. 180 data source connectors are divided into 3 main categories.1. Google Connectors: BigQuery, Google Sheets, Google Ads etc.2. Partners Connectors: Compatible JSON/CSV/XML, Facebook Ads, Amazon Ads, Instagram Ads, LinkedIn Ads, Twitter, Vimeo, YouTube, etc.3. Open source connectors: GitHub, Stack Overflow
Users can download the report in PDF and schedule it to be mailed to you or your customers weekly/monthly.
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Microsoft Power BI has a pricing plan. Free version with basic reporting feature and Pro version gives you full features with real time dashboard updates. For pricing, see this link.
Finally, we studied the comparison between two business intelligence tools – Microsoft Power BI vs Google Data Studio. If cost is a constraint, one can go with the free version of Power BI or Google Data Studio platforms. Basically, if one doesn’t want to pay, already has good and clean data, doesn’t need to do any data clean-up, doesn’t need to mix & merge before visualizing, doesn’t need to display data from different sources in a single chart, etc. then stick to Google Data Studio. On the other hand, if one expects to perform complex data transformations, is willing to pay, and needs to view information from advertising systems outside of Google’s globe, Microsoft Power BI is a better choice.
Sheetal Patel VP with high level expertise and technical precision in SharePoint development. Over the past two decades of experience has helped businesses solve complex challenges that have led them from startups to Fortune 500 companies to growth and performance.
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