The unique and exciting experience of working with data in Tableau is a result of VizQL (Visual Query Language).

VizQL was developed as a Stanford University research project, focusing on the natural ways that humans visually perceive the world and how that could be applied to data visualization. We naturally perceive differences in size, shape, spatial location, and color. VizQL allows Tableau to translate your actions, as you drag and drop fields of data in a visual environment, into a query language that defines how the data encodes those visual elements. You will never need to read, write, or debug VizQL. As you drag and drop fields onto various shelves defining size, color, shape, and spatial location, Tableau will generate the VizQL behind the scenes. This allows you to focus on visualizing data, not writing code!

One of the benefits of VizQL is that it provides a common way of describing how the arrangement of various fields in a view defines a...

There is virtually no limit to the data that Tableau can visualize! Almost every new version of Tableau adds new native connectors. Tableau continues to add native connectors for cloud-based data. The web data connector allows you to write a connector for any online data you wish to retrieve. The Tableau Hyper API allows you to programmatically read and write extracts of data, enabling you to access data from any source and write it to a native Tableau format. Additionally, for any database without a built-in connection, Tableau gives you the ability to use a generic ODBC connection.

You may have multiple data sources in the same workbook. Each source will show up under the Data tab on the left sidebar.

Data sources in Tableau store information about the connection(s). In addition to the connection itself (for example, database server name, database, and/or filenames), the data source also contains information about all the fields available (such as field name, data type, default format, comments, and aliases). Often, this data about the data is referred to as metadata.

Right-clicking a field in the data pane reveals a menu of metadata options. Some of these options will be demonstrated in a later exercise; others will be explained throughout the book. These are some of the options available via right-clicking:

• Renaming the field
• Hiding the field
• Changing aliases for values of a non-date dimension
• Creating calculated fields, groups, sets, bins, or parameters
• Splitting the field
• Changing the default use of a date or numeric field to either discrete or continuous
• Redefining the field as a dimension or...

Nearly all data sources allow the option of either connecting live or extracting the data. A few cloud-based data sources require an extract. Conversely, OLAP data sources cannot be extracted and require live connections.

Extracts extend the way in which Tableau works with data. Consider the following diagram:

Figure 2.19: Data from the original data source is extracted into a self-contained snapshot of the data

When using a live connection, Tableau issues queries directly to the data source (or uses data in the cache, if possible). When you extract the data, Tableau pulls some or all of the data from the original source and stores it in an extract file. Prior to version 10.5, Tableau used a Tableau Data Extract (.tde) file. Starting with version 10.5, Tableau uses Hyper extracts (.hyper) and will convert .tde files to .hyper as you update older workbooks.

The fundamental paradigm of how Tableau works with data...

Sets and groups are related concepts in Tableau and both allow you to group related data together. In both cases, the grouping is defined in Tableau rather than at the source.

A group is a combination of values from one or more dimensions. For example, with the four regions Central, East, South, and West, you might choose to create two groups: East-Central and South-West. This would allow you to make new comparisons and find new insights at higher levels of geography than you might have previously. You can create a group using the context menu for a field by selecting Create | Group.

That will open a dialog box that allows you to define the groups, like this:

Figure 2.25: The Create Group dialog box allows you to group together values for a given dimension

The result is a field, Region (group), that you can use as you would any other dimension in your data.

Often, you will want to filter data in Tableau in order to perform an analysis on a subset of data, narrow your focus, or drill into details. Tableau offers multiple ways to filter data.

While you may apply any number of filters to any given view, if you want to limit the scope of all your analysis to a subset of data, you can filter the data at the source using one of the following techniques:

• Data source filters are applied before all other filters and are useful when you want to limit your analysis to a subset of data.
• Extract filters limit the data that is stored in an extract (.tde or .hyper). Data source filters are often converted into extract filters if they are present when you extract the data.
• Custom SQL filters can be accomplished using a live connection with custom SQL, which has a Tableau parameter in the WHERE clause. We’ll examine parameters in Chapter 4, Starting an Adventure with Calculations and Parameters.

This chapter covered key concepts of how Tableau works with data. Although you will not usually be concerned with what queries Tableau generates to query underlying data engines, having a solid understanding of Tableau’s paradigm will greatly aid you as you analyze data.

We looked at multiple examples of different connections to different data sources, considered the benefits and potential drawbacks of using data extracts, considered how to manage metadata, and considered options for filtering data.

Working with data is fundamental to everything you do in Tableau. Understanding how to connect to various data sources, when to work with extracts, and how to customize metadata will be key as you begin deeper analysis and more complex visualizations, such as those covered in Chapter 3, Moving Beyond Basic Visualizations.

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