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Sets are manually-created or conditionally computed subsets of input data to facilitate customized depiction elements in the view.

Use Parameter in Calculations: Parameter values returned from the parameter control can be used in calculations, functions or in logical statements so as to provide a provision to users for depicting desired measure/statistic.

Show Parameter Control: Parameter controls like Filter boxes, gauges, sliding bars, etc., can be made visible in the view by selecting the ‘show parameter control’ by right-clicking against the parameters in the parameter pane.

Create New Parameter: Every instance of entering a constant during view creation supports the option – ‘create new parameter’ to enable tuning of the concerned aspect in the published view.

Generally, the process for the parameters used in Tableau involves the below steps: The range or list of parameter values users can change or choose should be of the same data type as defined in parameter properties.

Reference line definition for threshold-based classification.

The common use-cases that need usage of parameters are: Visualization interaction can be enhanced by parameterizing a few aspects of the view.

INCLUDE: Aggregation is performed post grouping the INCLUDED list of dimension fields.

The three expressions supported by Tableau to traverse levels of detail are: The independent fields that cannot be aggregated are considered as Dimensions and those fields that can be aggregated and are context-dependent are referred to as Measures.įor example, consider the Superstore dataset in which Region, Year are dimensions that provide context to add meaning to the Sales figure, which is a summarizable measure. It classifies the data into Dimensions and Measures.

Working with TableauĪ brief description of key concepts to work is: 1. One of the most popular data visualization tools proved to be the best in visual-based data exploration. Support for semantic visualization to perform an automatic selection of suitable visualization primitives such as line charts, bar charts, scatter plots, Tree diagrams, etc., for nominal, ordinal, and numerical data. Integration of view manipulation features like slicing and dicing, roll up to the required level of detail, filtering the data to help in detailed explorative data analysis.ĥ. Cloud data storage like Oracle Cloud, AWS and Microsoft Azure, etc.Ĥ.Unstructured data in NoSQL databases like MongoDB, Couchbase, etc.Semi-structured data like XML files, JSON payloads.Structural data as in Relational Databases (Oracle, MySQL, PostgreSQL).Ability to handle heterogeneous data by possessing data-connectors or parsers for various sources that hold or store. Scalability to the rapidly growing data size and to the increased data complexity comprising scalar, vector, and tensor data types without compromising the performance of the tool.ģ.

First, ease use of tool functionalities avoids the need for in-depth training on technology for the creation or interaction of views in order to derive valuable insights.Ģ. As a result, the requisite efforts and time to create and maintain graphics packages and the level of expertise to understand graphics jargon is greatly reduced.ġ. The overwhelming growth rate of available data volumes and the pressing need for the decision-makers in all domains of business and research to make quick and accurate decisions increased the scope for data visualization tools to understand data with graphics.

It helps to explore data, visualize and prepare reports for the same data. It is also used in reporting and is mostly called a reporting tool. Visualizations are made in the form of dashboards, and data should be represented in tabular format. It is mostly used in the Business Intelligence industry, and raw data is simplified easily to any format understandable by the users. A data visualization tool developed in Salesforce to connect with any database, be it SQL or MongoDB, and interact freely is called Tableau.