This section will walk you through the data types available in Tableau and the ways in which the default behaviors of different field types can be set, as well as their implications.

Data Types

Tableau data types are the methods by which Tableau understands the information stored within a field. Fields can be either the text or string type, numeric, date, date and time, Boolean, or geographic.

Numeric data types can be specified as either whole numbers or decimal numbers. Both have the same symbol in Tableau, and there is no significant difference in functionality. If it is not known whether a numeric field contains details down to the decimal level, it is recommended to set the field to decimal anyway just in case.

By default, Tableau will assign a data type to a field, depending on what is most appropriate. For example, a field with only 1 and 0 values will be assigned as Boolean, whereas a field with values in a format such as 01/01/2024 will...

This section will walk you through the Tableau Desktop and Tableau Prep cleaning functionalities, starting with what is important when assessing data quality. Cleaning in Tableau Desktop will be followed by using the data interpreter, using folders to organize data sources, and finally, cleaning in Tableau Prep.

Cleaning data refers to either removing unwanted data or fixing broken data. This can include processes such as removing duplicates and outliers or fixing incorrect values and formatting. For example, an invoice system may produce a data source with duplicate invoice records in error. For the data source to be usable in Tableau, the duplicate records would have to be removed to ensure that invoices are not double counted, resulting in inflated totals.

Ensuring data is clean before analysis is a key step, as the results of the analysis can only be trusted once the data source is confirmed as accurate and reliable. Every data source is different in terms of...

Tableau Desktop’s Data Interpreter is a methodology available to clean datasets from Excel, CSV, PDF, and Google Sheets files that are often created to be human-readable as opposed to machine-readable. The Data Interpreter helps clean a file into a usable dataset, getting rid of things such as titles, notes, and stacked headers. Additional tables and sub-tables within a file can also be identified and selected using Tableau’s Data Interpreter. The data in the file will not be updated when using Data Interpreter, only Tableau’s interpretation of the data.

To use Data Interpreter, go to the Data Source page using the tab in the bottom-left corner. If the data source is the correct file type, there will be an option in the Sheets pane to tick a box labeled Use Data Interpreter. Once this has been clicked, the label will switch to Cleaned with Data Interpreter, and there will be a Review the results link, which will take you to an Excel file that...

Once a data source has been cleaned and is ready for analysis, it can be organized in Tableau Desktop’s Data pane. By default, a Tableau data source with multiple tables will group fields by the table they came from. Dimensions will be listed first, followed by measures for the first table, and then there will be the second table’s list of fields, and so on.

To switch from table organization to grouping by folders, open the Data pane menu using the carrot in the top right and select Group by Folder. If using a multi-tabled data source, the Data pane will switch to a single list of fields, with dimensions above measures as opposed to table-specific lists. If the data source is a single table, then Group by Folder will be selected by default. Folder information is retained when switching between the Group by Folder and table views.

To create a folder, select the field that should be grouped within it, then right-click one of the fields...

When a more comprehensive cleaning of a data source is required to ensure the analysis is accurate, the best tool to use is Tableau Prep. An example of this could be three distinct Sales data sources from offices in three different regions, each of them with their own methodology. Although the general metrics might be the same, the specific names of fields, the calculation logic applied, and the level of data aggregation may differ between each office. In this case, all three of the data sources can be cleaned in separate streams in Tableau Prep until they all have a common format, before combining them all together.

Tableau Prep offers a wide variety of data cleaning functionalities. Tableau Prep works by inputting data, which then goes through a series of logical steps. One of the logical steps available is called the clean step, and it provides all of the functionalities available for cleaning data in Tableau Prep.

To filter out outliers or unnecessary...

This section covers the data transformation functionality available in both Tableau Desktop and Tableau Prep. For basic transformations, such as splitting fields on a given character to create new fields or restructuring data by pivoting specific columns, Tableau Desktop can be used.

For more advanced transformations, such as aggregation of data and complex pivoting and unpivoting of data sources, Tableau Prep is best. Some of what could be considered data transformation was covered in the section on Tableau Prep’s clean step. In general, data cleaning refers to removing incorrect data and fixing issues, whereas data transformation refers to modifying the structure of the data. This can include adding new fields, increasing or decreasing the level of detail, or flipping/pivoting rows and columns.

Tableau Desktop Transformations

Tableau Desktop allows for some basic data transformations such as splitting fields on a given delimiter to create new fields...

Tableau Prep offers a wider range of functionalities when it comes to transforming the shape of a data source. The Aggregate logical step in Tableau Prep allows users to group values in a data source at a different level of detail. For example, a dataset with a row for every single order could be aggregated to return the total sales for each product. This would mean the data source has fewer records and therefore will perform faster. It also means it could be combined with another data source at the product level.

To aggregate data in Tableau Prep, add the Aggregate step and drag any field that should be grouped from the left-hand list of fields into the Grouped Fields section. Drag any field that should be aggregated into the Aggregated Fields section and select the method of aggregation at the top of the field. Numeric fields will usually be aggregated and all other required fields will be grouped. However, there are some cases where numeric fields...

Pivoting data in Tableau Prep is similar to Tableau Desktop, but with a wider range of functionalities. The data can be pivoted from Columns to Rows, which works in the same way as in Tableau Desktop, taking a wide dataset and making it thinner and taller by pivoting the selected fields into two new columns. The first column is Pivot Names, which returns the names of the columns that have been pivoted, and the second created column is Pivot Values, which contains the corresponding original field values. Pivots can also be created with separate value columns for other metrics, all within the same pivot.

The additional functionality offered by Tableau Prep is the ability to pivot from Rows to Columns. This means taking a dataset and making it smaller and wider. A field is selected to pivot from rows to columns, meaning the distinct values of that field will now become column headers. Therefore, if a region column is selected to be pivoted and it has the...

This section covers the methodologies for combining data sources together in Tableau. How to join data sources on common fields in Tableau Desktop is covered first, followed by how to union data sources in Tableau Desktop. Relationships in Tableau Desktop and Tableau Blending will also be discussed. Finally, how to join and union data sources in Tableau Prep is looked into.

Tableau Desktop Joins

A common method for combining two data sources is to join them side by side on a common field or on multiple common fields. Tableau Desktop allows datasets from any valid data source type to be combined together (Tableau Data sources cannot be used in joins). There are multiple ways to configure a join in Tableau Desktop such as deciding which fields to match with between the two data sources and then which data to keep as a result of the join.

Choosing which fields to match on can be as simple as selecting a common field with the same name in two data sources. However...

Data sources can be combined in Tableau Desktop by placing one data source on top of the other structurally, aligning common columns, and adding null values in columns that are not common between data sources. This method of combining data is called unioning the data sources.

Table headers should be the same across the data sources being unioned if the data from one column should be placed on top of the same from the other data source. Data types must also be common across common columns. An example of a union might be a sales file from 2022 and a sales file from 2023. Both files have the same structure in terms of columns, each with the same name and data type. Therefore, when the union is created, the 2022 data can be placed neatly on top of the 2023 data structurally. By default, unions match columns based on the header names so the order of the fields does not matter. However, the union can be configured to generate field names automatically, in which...

Relationships are a way to combine tables together on Tableau, side by side, on a common field(s). However, instead of creating a fixed large table that merges the two data sources, the tables are kept separate at their individual level of detail and joins are created automatically based on the fields used in the view.

Relationships are more flexible than joins as only the relationship (similar to a join clause) has to be configured. A join type does not need to be selected because what is included or excluded from the data depends on the fields used in the view. The fact that relationships keep tables distinct means no data is lost and that tables can be at different levels of aggregation because they are not physically joined, so there is no chance of data duplication. The aggregation of measures and the joins required are all calculated by Tableau; the user only needs to define the relationship that connects the tables.

To create a relationship in Tableau, drag...

Data blending in Tableau Desktop is another method of combining data from multiple data sources in a side-by-side structure via common fields that does not create a physically joined single table. Blending functions differently to both joins and relationships in Tableau in that the data is not actually joined directly. Each source is aggregated separately and then presented together in the view.

The benefit of blending over relationships or joins is that multiple connecting fields can be established between data sources, and these can be toggled on or off with each view in a workbook. This makes them much more dynamic and therefore suitable to problems that require multiple conflicting connections at different levels of aggregation between two Tableau data sources. For example, a Sales data source with values by Country and Product Category could be blended with a Sales Target data source, also broken down by Country and Product Category. Instead of fixing a join or relationship...

Tableau Prep is preferable to Tableau Desktop when creating combined data sources as it is generally better for creating data sources. If any additional data cleaning or transformation needs to occur before or after a join, then Tableau Prep is better. Tableau Prep also offers more flexibility in the join methodology/output.

In general, Tableau Prep works the same way as Tableau Desktop when it comes to a join where two tables/data sources are being combined – one from the left and one from the right. A join clause needs to be created, and the functionality is the same again, with the ability to select fields from either side and have them be compared to see whether they are equal, not equal, greater than, less than, greater than or equal to, or less than or equal to each other. Join clauses do not include Tableau calculations in Tableau Prep but a Calculated field can be made in a clean step prior to the join step.

When it comes to the output of the...

Unions in Tableau Prep work the same way as in Tableau Desktop – by placing one dataset on top of the other based on field name, and where field names do not match, records are filled using null. Columns also need to be the same data type to be unioned on top of each other.

To create a union, drag one step in the flow onto another so that the Join and Union popups appear. Then, drop the step on Union. This will create the Union logical step, which has a setting pane on the left-hand side that includes an inputs legend at the top. The legend lists all of the data sources included in the union (there can be up to 10, with new ones being added by dragging and dropping onto the union step), and each distinct step is color-coded. There is then a description of the total number of fields resulting from the union and the amount of them that are mismatched, meaning not included in all data sources in the union. Mismatched fields are then listed below with a square...

It is important to consider data quality before conducting analysis in Tableau. If data has not been assessed for consistency, accuracy, and completeness, then the insights derived from it cannot be trusted.

There are multiple data types for fields in Tableau, including string, numeric, Boolean, geographic, date, and date and time. Fields behave differently depending on data type but also depending on color. Blue fields are discrete, which means that they essentially consist of a finite number of grouping values. Green fields are continuous, which means there is theoretically an infinite number of values that are possible. Fields can also be either dimensions or measures. Dimensions are used to break up views, whereas measures are used to aggregate metrics.

Tableau Desktop offers a wide range of functionality for cleaning, transforming, and combining data to ensure that it is ready for analysis. Fields can be cleaned by renaming, filtering out unneeded values, setting...

Apart from a solid understanding of key concepts, being able to think quickly under time pressure is a skill that will help you ace your certification exam. That is why working on these skills early on in your learning journey is key.

Chapter review questions are designed to improve your test-taking skills progressively with each chapter you learn and review your understanding of key concepts in the chapter at the same time. You’ll find these at the end of each chapter.

How to Access these Resources

To learn how to access these resources, head over to the chapter titled Chapter 9, Accessing the Online Practice Resources.

To open the Chapter Review Questions for this chapter, perform the following steps:

1. Click the link – https://packt.link/TDA_CH02.

   Alternatively, you can scan the following QR code (Figure 2.57):

Figure 2.57: QR code that opens Chapter Review Questions...

Target: Your aim is to keep the score the same while trying to answer these questions as quickly as possible. Here’s an example of how your next attempts should look like:

Table 2.2: Sample timing practice drills on the online platform

Note

The time limits shown in the above table are just examples. Set your own time limits with each attempt based on the time limit of the quiz on the website.

With each new attempt, your score should stay above 75% while your “time taken...