Business Intelligence Software Prices Contrast

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Business Intelligence Software Prices Contrast – Looking for cutting-edge business intelligence and data visualization tools? Microsoft Power BI and Google Data Studio have been gaining quite a bit of traction lately. Both allow you to create customized graphs and reports in seconds, and the other provides interactive graphs with powerful business intelligence capabilities. The following post from the SharePoint development team provides a quick comparison between Google Data Studio and Power BI.

Easy to create data visuals – users don’t need to be graph manipulation experts.

Business Intelligence Software Prices Contrast

Google Data Studio is currently free to all visitors. You need a Google Account to get started. To get started, go to and create your first dashboard.

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1. Power BI Pro: Individual users can purchase Power BI Pro to access the latest self-service insights, view data in live real-time dashboards, and exchange ideas across the business. Masu. This package costs $9.99 per member per month.

2. Power BI Premium per user: Individual users can license Power BI Premium to accelerate populating advanced artificial intelligence insights, enable self-service data preparation for large databases, and deploy at enterprise scale. Streamline data processing and consumption. This package costs $20 per member per month.

3. Power BI Premium per capacity: This is equivalent to Power BI Premium per account, except there are no user limits. This subscription is priced at $4,995 per month and is recommended for businesses with a large number of Power BI users.

Power BI integrates with various Microsoft technologies such as Azure, Sharepoint, MS Office, Excel, and marketing apps such as Google Analytics, Salesforce, Zendesk, Marketo, and Meta. Additionally, you can link to databases, CSV files, and other file types. Additionally, we may extract information from the website.

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Google Data Studio connects with a variety of other Google services, including Analytics, Advertising, BigQuery, and Sheets. However, it does not inherently process many different cloud-based inputs. This 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.

Data Studio is a Google product, so it comes pre-integrated with a variety of Google products, including Google Analytics, Google Ads, Google Sheets, BigQuery, and Cloud Storage. Additionally, over 400 partner connectors are available to connect to data sources such as Adobe Analytics, JASON, and Facebook. One of the great features of Data Studio connections is the ability to create your own connections if you can’t find one that meets 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 diagrams on my dashboard?” Yes, it is. Google Data Studio has a feature called blending, which is essentially a left-outer merge that allows you to generate 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 called join keys. It is important to note that this step is only done at the report level. It does not appear on the data source home page.

Power BI integrates with over 100 data sources, including SQL and NoSQL. Basically, Power BI data sources can be divided into two categories:

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1. Files: Files are used to reference multiple data sources such as PDF, Excel, JSON, Google Analytics, Adobe Analytics, etc.

2. Database: Database is often used to refer to SQL-based data sources such as BigQuery, Amazon Redshift, MySQL, and Azure databases.

To integrate data sources into Microsoft Power BI, you can use the Add Query or Merge Queries tools. Now let’s compare and contrast append and merge queries.

Adding rows from one query to another generates a new request where every row from the first query is followed by every row from the second question. The add process requires at least two requests. This is similar to the behavior of outer joins in SQL databases. Conversely, a merge query generates a new request from two existing requests. One query result contains all columns from the main table, and one column acts as a relationship column to the secondary table. A related table contains all the rows that correspond to each row in the main table by shared column values. This is similar to a left join in SQL databases. Power BI is great for exploring relationships in your data.

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Unlike many other business intelligence and data analysis tools on the market, Google Data Studio does not have substantial predictive analysis capabilities such as classification regression or time series forecasting. Therefore, without this functionality, Google Data Studio may cease to be a useful tool for companies with complex use cases such as forecasting and forecasting.

This program does not natively support Python or R. However, users can combine R and Python with Data Studio using third-party tools such as Panoply. Additionally, you can link Data Studio to Google Sheets that are updated using Python. This approach is suitable for small datasets. You can also leverage BigQuery to work with larger datasets.

Reports can be embedded in any website or application that accepts HTML iframe tags. The iframe code containing the link to your analysis is instantly created by Data Studio. No HTML expertise required. The embedded report appears in the preview pane (viewers cannot modify the report).

Power BI offers a variety of analytics capabilities for different use cases. Some of the most famous ones are listed below.

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To use Power BI effectively, you need to understand the basics of extract, transform, and load, or ETL processes. ETL is important when working with the M language in PowerQuery.

Additionally, you should be familiar with the DAX language for statistics and measurement, and PowerPivot for data modeling. In summary, you need guidance to become proficient in using Power BI.

In contrast, Data Studio is a technology that requires no programming. If you have a working knowledge of spreadsheets, aggregations, and graphs, you can get started right away. When generating expert data visualizations, you can connect data sources and drag widgets onto the board.

Sharing data using Power BI is a time-consuming process. You can’t share documents directly from your desktop application. You must release your report using Power BI’s publishing feature. Mobile applications, on the other hand, allow you to share summaries and reports right from the application.

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You can collaborate with external or internal collaborators on Power BI reports and dashboards. To view such files, the recipient must also have a Premium Per User (PPU) or Power BI Pro subscription.

There is an approx. 110 data connectors are available divided into 6 different categories. File: Excel, CSV, XML, etc. 2. Databases: SQL, Access, Teradata, SAP, etc. 3. Power BI: Power BI datasets and Power BI data flows. 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. Others: Microsoft Exchange, ODBC, OLEDB, Spark, R Script, etc. .

Approximately 180 data source connectors are currently available, divided into three main categories: Google Connectors: BigQuery, Google Sheets, Google Ads, etc. 2. Partner Connectors: Custom JSON/CSV/XML, Facebook Ads, Amazon Ads, Instagram Ads, LinkedIn Ads, Twitter, Vimeo, YouTube, etc. 3. Open Source Connectors: GitHub, Stack Overflow

Users can schedule the process of downloading reports in PDF and sending them to your company, or emailing weekly/monthly reports for your customers.

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Microsoft Power BI has pricing plans. A free version with basic reporting functionality and a pro version with all features including real-time dashboard updates are available. See this link for pricing.

Finally, we looked at a comparison between two business intelligence tools: Microsoft Power BI and Google Data Studio. If cost is a constraint, you can use the free version of Power BI or Google Data Studio platform. Basically, you’re not willing to pay for it, you already have nice, clean data, you don’t have to do data cleanup or joins or merges before you visualize it, and you can display data from different sources in one chart. There’s no need to. etc., then stick to Google Data Studio. On the other hand, if you plan to perform complex data transformations, are willing to pay a fee, or need to display information from advertising systems other than Google, Microsoft Power BI is the best choice.

Shital Patel is a Vice President with a high level of proficiency and technical precision in SharePoint development. His experience over the past 20 years has helped companies solve complex challenges, leading to growth and performance from startups to Fortune 500 companies.

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