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Data Blending using Tableau

Data Blending and its need   Layman Learning

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The process of combining fields from multiple tables spanning different data sources is called Data Blending. In Tableau when we need to create a visualization which requires fields from multiple data sources, we need to do Data Blending.

Let's understand different ways Data Blending can be done.

Case 1 – Automatic Data Blending

- For two fields having same name and at least one member/value is commonly present in both fields, tableau automatically blends/creates link from secondary data source field to primary data source field.

E.g. 'State' field from Coffee chain datastore (MS Access) and 'State' field from Superstore datastore (MS Excel) have same name and both have at least one common data value, i.e. 'California'

Case 2 – Manual Data Blending

- For two fields having different names but at least one member/value is commonly present in both fields, then we need to manually establish relationship between these two fields.

E.g. 'Market' field from Coffee chain datastore (MS Access) and 'Region' field from Superstore datastore (MS Excel) have different name but both have at least one common data value, i.e. 'West'

Let us look at both these cases once we cover common steps.

Common Steps

1. Add a new worksheet in your current Tableau Workbook by Worksheet >> New Worksheet.
2. Lets add 'Sample - Coffee Chain (Access)' data source in addition to 'Sample – Superstore Subset (Excel)' data source already in our workbook.

 

Tableau - Connecting to data

 

In real life, data is generated from different data sources. With the versatile nature of data, it becomes very difficult to do analysis on top of it. Tableau proves handy in such situations. It facilitates connection to a variety of data sources by providing rich set of features. These features include :
Native Data Connectors
Live or In Memory Connection
Ability to Combine Data Sources

We'll discuss about Native Data Connectors and Live or In Memory Connection

 

Understanding Tableau Data Extract Mechanism

1. Data Source Type

If your data source is a large text file or Excel file, you will find immediate benefits from creating a data extract. This is because the queries running in those files are slow. There’s no amount of optimization in your deployment that can fix the performance inadequacies in these types of files.

The reason you’ll find that performance has improved is that text and Excel data are extracted from the source files and loaded into the Tableau high-performance columnar data engine, specifically designed for visual analytics.

 

 

Difference between Tableau Desktop & Server

Tableau Desktop is used to create workbooks,dashboards & stories.Basically all the development is done on desktop.That is creating charts, formatting the charts, creating dashboards by using different charts and filters is all done on Desktop.

Tableau Server is used to distribute the dashboards and stories created on desktop. When the dashboard created on desktop is published to the Tableau Server, other people can also see that dashboard provided that they are granted the credentials. Workbooks and Dashboards can be edited on server also, but new workbooks or dashboards can’t be created. Also, the editing options are very limited in server compared to desktop

 

Visualizing Data using Charts and Dashboards in Tableau

After connecting to database, Tableau categorizes each data fields into dimension and measure according to type of every filed.

Dimensions : The field which contains String value or which holds discrete data is categorized into dimensions. For example Customer name, Order Date, Region.
Measures : The field that holds numerical which can be aggregated, is categorized into measures. For example Profit, Sales, Unit Price.

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