Differences Between Power BI vs Tableau
Microsoft Power BI is a business Intelligent Tool to handle data from different sources and provides visualization after the cleaning and integration process. It enables Adhoc report generation and helps in the analysis of the data. Effective and easily understandable Dashboards are generated and can be published on the web. It can be used by naïve users to experienced users. It is used to run Adhoc queries to identify patterns and trends.
Tableau is a business intelligence tool with an appealing user interface to generate reports, dashboards, and analysis of the huge data from multiple data sources. It provides interactive data visualization to understand the data and make insights. It makes the users understand the data without the need for technical knowledge and enables understanding of even any complex process in a simple and efficient approach.
Head to Head Comparison Power BI vs Tableau (Infographics)
Below is the Top 7 Comparison Power BI vs Tableau:
Key Differences Between Power BI vs Tableau
Power BI vs Tableau differs majorly in the visualization standpoint ability in extracting the data from different servers. Below are the most important key Differences Between Power BI vs Tableau
Power BI cannot connect to Hadoop databases whereas it enables data extraction from Azure, Salesforce and googles analytics. Tableau allows accessing data in the cloud and connecting to Hadoop databases. It also identifies the resource automatically.
Power Bi provides numerous data points to provide visualization. It has around 3500 data points for drilling down across the dataset and conduct an analysis. Without any coding language, using the drag and drop method, users can create charts, scatter plots in the tableau and it also does not restrict the number of data points.
Power BI provides limited customer support. Tableau has a strong customer support and has community forums for the discussions. It has categorized the support into online, desktop and server support.
Power BI is available in three categories. Desktop, mobile, and service. The very basic set up is Azure Tenant. Tableau makes it possible to share the results generated in Tableau desktop over Tableau Online or Tableau Server.
Power BI is Saas model ie. Software as a Service. Tableau is available in both on-premises and cloud. When huge data is available in the cloud, it produces the best result.
Power BI vs Tableau Comparison Table
Below is the Comparison table between Power BI vs Tableau
|Power BI is the business data analytics tool to analyze the business and derive insights from it.||Tableau is the business intelligence and data analytics tool for generating reports and data visualization with high flexibility.|
Limited access to other databases and servers
When compared to Tableau.
SQL Server Database, Access Database, SQL Server Analysis Services Database, Oracle Database, IBM DB2 Database, IBM Informix database (Beta), IBM Netezza, MySQL Database, PostgreSQL Database, Sybase Database, Teradata Database, SAP HANA Database, SAP Business Warehouse Application Server, SAP Business Warehouse Message Server (Beta), Amazon Redshift, Impala, Google BigQuery, Snowflake, Exasol.
It has access to numerous database sources and servers.
Excel,Text File,Access,JSON File ,PDF File ,Spatial File ,Statistical File ,Other Files (such as Tableau .hyper, .tds, .twbx) ,Connect to a Published Data Source on Tableau Online or Server ,Actian Matrix ,Actian Vector ,Amazon Athena ,Amazon Aurora ,Amazon EMR ,Amazon Redshift ,Anaplan ,Apache Drill ,Aster Database ,Box ,Cisco Information Server ,Cloudera Hadoop ,DataStax Enterprise ,Denodo ,Dropbox ,EXASOL ,Firebird ,Google Analytics ,Google BigQuery ,Google Cloud SQL ,Google Sheets ,Hortonworks Hadoop Hive ,HP Vertica ,IBM BigInsights ,IBM DB2 ,IBM PDA (Netezza) ,Kognitio ,MapR Hadoop Hive ,Marketo ,MarkLogic ,MemSQL ,Microsoft Analysis Services ,Microsoft PowerPivot ,Microsoft SQL Server ,MonetDB ,MongoDB BI Connector ,MySQL ,OData ,OneDrive ,Oracle ,Oracle Eloqua ,Oracle Essbase ,Pivotal Greenplum Database ,PostgreSQL ,Presto ,Progress OpenEdge ,QuickBooks Online ,Salesforce ,SAP HANA ,SAP NetWeaver Business Warehouse ,SAP Sybase ASE ,SAP Sybase IQ ,ServiceNow ITSM ,SharePoint Lists ,Snowflake ,Spark SQL ,Splunk ,Teradata ,Teradata OLAP Connector ,Web Data Connector,Other Databases (ODBC)
Each workspace/group could handle up to 10 GB of Data.
For more than 10GB, Either Data needs to be in a cloud(Azure), if it is in local databases Power BI just selects or pulls the data from a database and does not import.
|Tableau works on the columnar based structure which stores only unique values for each column making it possible to fetch Billions of rows.|
Power BI is integrated with Microsoft Azure, It helps in analyzing the data and understanding the trends and patterns of the product/business.
|Python machine learning capacities are inbuilt with Tableau, making it efficient for performing ML operations over the datasets.|
It can handle a limited volume of data.
|It can handle a huge volume of data with better performance.|
|Even though access is easy and simple, Analysts and Experienced users use it for their analytics purposes.|
It is very cheap when compared to Tableau.
|Tableau is costlier than power BI. It needs to be paid more when connected to third-party applications.|
Business Intelligent tools play a vital role in taking business decisions. As far as Power BI vs Tableau is concerned, both Power BI and Tableau has its own features, pros, and cons. It all depends upon the business needs and requirements. If the business requirement is to analyze the limited amount of data and functionality Power BI is the best way to opt for as it is cheaper than Tableau. But, when it comes to handling huge data from various sources and needs to perform any statistics and fantastic data visualization over the data, tableau provides a lot and a lot of functionality and drilling down options. At the same time, the investment cost is high. So it highly depends on the business scale and requirement. Both Power BI vs Tableau tools perform outstandingly, so we would not be able to conclude only one tool outperforms the other. As the features like Data Preparation, Data Storage, Data validation and ETL operations are performed by both tools inefficient and without any latency.
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