Data window is a way to show the connection between Tableau and data source. You can connect to as multiple different data sources in a single workbook. The small icons associated with data connections provide additional details about the nature of the connection.
Here, a workbook that shows the three different data connection given below:

The green line next to the global superstore data connection indicates that it is the active connection in the worksheet. So, the bar chart in the spreadsheet was created using ‘dimensions and measures‘ from that data source. Thus the bar chart is created using the dimensions and measures from the data source.
The Olympic Athletes data connection is a direct connection that is also indicated by the grey highlights. Those data source fields are currently displayed on the measures and dimensions shelves. The clipboard data source at the top of the data window was dragged and dropped into Tableau.
When you create data connections, Tableau will automatically evaluate the fields and place them on the measures and dimensions shelves.

Usually, Tableau placed most of the fields correctly. If something is incorrectly placed, drag the field to the correct location. Errors sometimes occur when numbers are used to illustrate dimensions.
For example, if you want to connect a spreadsheet that contains Olympic Athletes details and you want to know how many gold medals were won by different countries in last years, that field is placed into the measures shelf. Dragging gold medal field from the measures shelf and dropped into the worksheet would result in the field being summed. Properly placed on the dimension shelf, the athletes country would behave like a dimension and be expressed in a column or row. In the same way, the gold medal and country are represented in the above Figure.

Data Types in Tableau

Tableau expresses fields and assigns data types automatically. If the data source appoints the data type, Tableau will use that data type. If the data source doesn’t individually assign a data type, Tableau will assign one. Tableau consist of the following data types:
• Date values
• Text values
• Numerical values
• Date and time values
• Boolean values (True or False conditions )
• Geographic values (longitude and latitude used for maps)

In the above figure, focus on the icons next to the fields in the measures and dimension shelves. These icons denote specific data types. A calendar with a clock is a date or time field. Numeric values have pound signs, and “abc” icons indicate text fields. Boolean fields have “True or False” values.
Data Aggregation in Tableau
It is useful to look at numeric values using different aggregations function. Tableau supports many different aggregation types, such as:
• Sum
• Average
• Count
• Count Distinct
• Median
• Minimum
• Maximum
• Variance
• Variance of Population
• Standard Deviation
• Standard Deviation of Population
• Attribute
• Dimension
In Tableau, you can create aggregation dimensions and measures. Whenever you add measures to your view, an aggregation is applied to those measures by default. The type of Aggregation used depends on the context of the view.
If you are not familiar with the database, then refer to Tableau manual for detailed definition of these aggregate types. You are adding fields into the visualization by default then it will be displayed.
Tableau allows you to change or alter the aggregation level for a specific view. To change the default aggregation, do right click on that field inside the data shelf and change its default by selecting the menu options (default properties or Aggregation).
You can also change the Aggregation of a field for specific use in a worksheet.
For example: By right-clicking on the SUM (Sales) pill and selecting the Measure (SUM) menu option, you can choose any of the aggregations highlighted.

The data source used in the above figure is a data extract of an Excel spreadsheet. It is important to understand that if you depend on a direct connection to Excel, the median and count (distinct) aggregations would not be available. Access, Excel, and text files do not support these aggregate types. Tableau’s extract engine do this task.

Aggregating Measures

When you add a measure to the view, Tableau automatically aggregates its value. Average, sum and median are the common aggregation functions. The current Aggregation looks like part of the measure’s name in the view.
For example: Sales becomes SUM (Sales), and every measure has a default aggregation, which is set by Tableau when you connect to a data source. You can change or view the default aggregation for measures.
• You can aggregate a measure using Tableau only for relational data sources.
• Multidimensional data sources contain data sources which are already aggregated.
• In Tableau, the multidimensional data source is supported only in windows.

Set the default Aggregation for Measures

You can set the default aggregations for any measures. It is not a calculated field that itself contains an aggregate, such as AVG ([Discount]). A default aggregation is the preferred calculation for summarizing a discrete or continuous field. The default aggregation is used when you drag a measure to a view automatically.
To change the default Aggregation
Right-click on a measure menu option in the Data field and select Default Properties then select Aggregation, and then select one of the aggregation options.

• You cannot set default aggregation for the published data source. The default aggregation is set only when the data source is initially published.

How to Disaggregate the Data

When you add a measure to your view, then Aggregation is applied to that measure automatically. This default is controlled by the Aggregate Measures setting in the Analysis menu.
If you want to see all of the marks in the view at the most detailed level of the model, you can disaggregate the view. Disaggregating your data means that the Tableau will display a separate mark for every data value in every row of your data source.
Disaggregation in all Measures in the view
Click on the analysis then go to aggregation measures option. When Aggregate Measures is selected, then automatically Tableau will attempt to aggregate measures in the view. Means that it collects individual row values from your data source into a single value that is adjusted to the level of detail in your view.
The different aggregations available for measures determine how the individual values are collected: they can be averaged (AVG), added (SUM), or set to the minimum (MIN) or maximum (MAX) value from the individual row values.

If it is already selected, click aggregation measures once for deselecting it. Then, you can see the changes.

Disaggregating data can be useful for analyzing measures which you want to use both dependently and independently in the view.
Note: If your data source is very large, then, as a result, disaggregating the data can degrade in significant performance.

Aggregating Dimensions

You can aggregates dimension in the view as Maximum, Minimum, Count, and Count Distinct. When you aggregate a dimension, you have to create a new temporary measure column, so the dimension takes on the characteristics of a measure.

Note: The Count Distinct aggregation does not support the Text File and Microsoft Excel data sources using the inheritance connection. If you are connected to one of these types of data sources, then the Count Distinct aggregation is unavailable, and it shows the remark “Requires extract.” If you save the data sources as an extract, you will be able to use the Count Distinct aggregation.
Another way to view a dimension as an attribute. You can change it by choosing the Attribute from the context menu for the dimension.
The attribute aggregation has several uses:
• It ensures a consistent level of detail when blending multiple data sources.
• It provides a way to aggregate the dimension when computing table calculations, which require an aggregate expression.
• It improves query performance due to locally computed.
Tableau calculates the Attribute using the below given formula:
1. If MIN (dimension) = MAX (dimension) then MIN (dimension) else “*” end
• This given formula is calculated in Tableau after the data is retrieved from the initial query.
• The asterisk (*) is a visual indicator of a special type of Null value it occurs when there are multiple values.

Above is an example of using Attribute in a table calculation. This table shows the market, market size, state, and sales by the market that is SUM (sales). Suppose, you want to compute the percent of the total sales according to each state contribution for the market. When you add some Percent of Total in table calculation that calculates along State, the calculation computes within the black area shown above figure just because the Market Size of dimension is partitioning the data.
When you aggregate the Market Size as an Attribute, the calculation is computed within the Market (East), and the Market Size information is used as a label in the display.

Tableau File Types

Tableau’s output after data analysis can be saved into different formats, which further can be distributed into different platforms.
There are various forms of different file categories, and the multiple different extensions identify them. Their extension depends on how it produces and for what purposes they are used in which format.
These all are generally stored as xml file format, and it can be easily open and edited.
You can save your work using several different Tableau specific file types such as bookmarks, workbooks, data extracts, packaged data files, and data connection files. Each of these files is described below in detail:

Type	File Extension	Purpose
Tableau workbook	(.twb)	Tableau workbook can hold one or more worksheets, and also hold zero or more stories and dashboards.
Tableau Bookmarks	(.tbm)	Tableau bookmarks can hold a single worksheet that can be easily shared, and pasted into other workbooks.
Tableau Packaged workbook	(.twbx)	Tableau packaged workbook is a single zip file which contains a workbook along with any supporting local file data and background images. This is the best way to package your work for sharing with others who don’t have access to the original data.
Tableau data Extract	(.hyper or .tde)	Tableau data extract is a local copy of the entire data set. It is used to share the data with others when you worked offline, and want to improve the performance.
Tableau data Source	(.tds)	Tableau data source file is a shortcut for quickly connecting to the original data that you use regularly. Data source file does not contain the actual data, and they only contain the necessary information to connect with the actual data. You can modify the top of the actual data such as creating calculated fields, changing default properties, adding groups, and so on.
Tableau Packaged Data Source	(.tdsx)	Tableau packaged data source is very similar to the tableau data source, but it has an addition of data along with the connection details.
Tableau Preferences	(.tps)	This file stores the color preferences, which is used among all the datasheets. It is also used to generate a customized look for the users.

These files are saved in the associated folders in the My Tableau Repository directory, which is created in your My Documents folder by default when you install Tableau. Also, Your work files can be saved in other locations, such as a network directory or your desktop.

How to Change the Tableau Repository Location

You can be specified a new location for the Tableau repository if you are not using the default location in your Document folders.
For example: If you want to have your data on a network server instead of your local machine, then you can see the remote repository.
1. Select File then go to Repository Location.
2. Select a new folder that will be the new repository location in the select a repository dialog box.
3. Restart Tableau then it uses the new repository.
Changing the repository location does not include the original repository. Alternatively, Tableau creates a new repository where you can store your files.
So, this brings us to the end of blog. This Tecklearn ‘Data Window, Data Types, Data Aggregation and File Types in Tableau’ blog helps you with commonly asked questions if you are looking out for a job in Tableau Desktop. If you wish to learn Tableau Desktop and build a career in Business Intelligence domain, then check out our interactive, Tableau Desktop Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

Tableau Desktop

Tableau Desktop Training

About the Course

Tecklearn’s Tableau Desktop Training teach you how to transform raw data into interactive and shareable dashboards using Tableau. Our Tableau Course covers the necessary analytical skills to Advanced data visualizations by incorporating real-world use-case scenarios, labs, and exercises. Some of the topics included are Data Blending, Data Mapping, Graphs, creation of charts, and LOD expression by using different versions of Tableau, such as Tableau Desktop, Tableau Reader, and Tableau Public.

Why Should you take Tableau Desktop Training?

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What you will Learn in this Course?

Understanding Tableau
• Why Tableau
• Tableau Product Platforms
• Tableau Architecture
• Tableau Interface
Data Connection with Tableau Desktop
• Features of Tableau Desktop
• Connect to data from File and Database
• Types of Connections
• Data Blending
• Joins and Unions
• Tableau Desktop User Interface
• Basic project: Create a workbook and publish it on Tableau Online
Basic Visual Analytics
• Visual Analytics
• Basic Charts: Bar Chart, Line Chart, and Pie Chart
• Hierarchies
• Data Granularity
• Highlighting
• Sorting
• Filtering
• Grouping
• Sets
Advanced Visual Analytics
• Parameters
• Tool tips
• Trend lines
• Reference lines
• Forecasting
• Clustering
Calculations in Tableau
• Calculated Fields
• Numeric, String, Data, Logical, Addressing and Partitioning
• Table Calculations
Level of Detail (LOD’s) Calculations
Advanced Charts in Tableau
• Box and Whisker’s Plot
• Bullet Chart
• Bar in Bar Chart
• Gantt Chart
• Waterfall Chart
• Pareto Chart
• Control Chart
• Funnel Chart
• Bump Chart
Parameters
• What If Scenarios
• Parameter in CF (KPI control)
• Parameter in Filter (Top N)
• Parameter in reference line
Dashboards and Stories
• Introduction to Dashboards
• The Dashboard Interface
• Dashboard Objects
• Building a Dashboard
• Dashboard Layouts and Formatting
• Interactive Dashboards with actions
• Designing Dashboards for devices
• Story Points
Data Security in Tableau
Optimizations tips and tricks
Interacting with Tableau Server
Working with Tableau JavaScript API

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