In today’s tutorial, we shall explore the Tableau data handling engine and a real world example of how to use it.
Tableau’s data-handling engine is usually not well comprehended by even advanced authors because it’s not an overt part of day-to-day activities; however, for the author who wants to truly grasp how to ready data for Tableau, this understanding is indispensable. In this section, we will explore Tableau’s data-handling engine and how it enables structured yet organic data mining processes in the enterprise.
To begin, let’s clarify a term. The phrase Data-Handling Engine (DHE) in this context references how Tableau interfaces with and processes data. This interfacing and processing is comprised of three major parts: Connection, Metadata, and VizQL. Each part is described in detail in the following section. In other publications, Tableau’s DHE may be referred to as a metadata model or the Tableau infrastructure. I’ve elected not to use either term because each is frequently defined differently in different contexts, which can be quite confusing.
Tableau’s DHE (that is, the engine for interfacing with and processing data) differs from other broadly considered solutions in the marketplace. Legacy business intelligence solutions often start with structuring the data for an entire enterprise. Data sources are identified, connections are established, metadata is defined, a model is created, and more. The upfront challenges this approach presents are obvious: highly skilled professionals, time-intensive rollout, and associated high startup costs. The payoff is a scalable, structured solution with detailed documentation and process control.
Many next generation business intelligence platforms claim to minimize or completely do away with the need for structuring data. The upfront challenges are minimized: specialized skillsets are not required and the rollout time and associated startup costs are low. However, the initial honeymoon is short-lived, since the total cost of ownership advances significantly when difficulties are encountered trying to maintain and scale the solution.
Tableau’s infrastructure represents a hybrid approach, which attempts to combine the advantages of legacy business intelligence solutions with those of next-generation platforms, while minimizing the shortcomings of both. The philosophical underpinnings of Tableau’s hybrid approach include the following:
- Infrastructure present in current systems should be utilized when advantageous
- Data models should be accessible by Tableau but not required
- DHE components as represented in Tableau should be easy to modify
- DHE components should be adjustable by business users
The Tableau Data-Handling Engine
The preceding diagram shows that the DHE consists of a run time module (VizQL) and two layers of abstraction (Metadata and Connection). Let’s begin at the bottom of the graphic by considering the first layer of abstraction, Connection. The most fundamental aspect of the Connection is a path to the data source. The path should include attributes for the database, tables, and views as applicable. The Connection may also include joins, custom SQL, data-source filters, and more. In keeping with Tableau’s philosophy of easy to modify and adjustable by business users (see the previous section), each of these aspects of the Connection is easily modifiable. For example, an author may choose to add an additional table to a join or modify a data-source filter. Note that the Connection does not contain any of the actual data. Although an author may choose to create a data extract based on data accessed by the Connection, that extract is separate from the connection.
The next layer of abstraction is the metadata. The most fundamental aspect of the Metadata layer is the determination of each field as a measure or dimension. When connecting to relational data, Tableau makes the measure/dimension determination based on heuristics that consider the data itself as well as the data source’s data types. Other aspects of the metadata include aliases, data types, defaults, roles, and more. Additionally, the Metadata layer encompasses author-generated fields such as calculations, sets, groups, hierarchies, bins, and so on. Because the Metadata layer is completely separate from the Connection layer, it can be used with other Connection layers; that is, the same metadata definitions can be used with different data sources.
VizQL is generated when a user places a field on a shelf. The VizQL is then translated into Structured Query Language (SQL), Multidimensional Expressions(MDX), or Tableau Query Language (TQL) and passed to the backend data source via a driver. The following two aspects of the VizQL module are of primary importance:
- VizQL allows the author to change field attributions on the fly
- VizQL enables table calculations
Let’s consider each of these aspects of VizQL via examples:
Changing field attribution example
An analyst is considering infant mortality rates around the world. Using data from h t t p://d a t a . w o r l d b a n k . o r g /, they create the following worksheet by placing AVG(Infant Mortality Rate) and Country on the Columns and Rows shelves, respectively. AVG(Infant Mortality Rate) is, of course, treated as a measure in this case:
Next they create a second worksheet to analyze the relationship between Infant Mortality Rate and Health Exp/Capita (that is, health expenditure per capita). In order to accomplish this, they define Infant Mortality Rate as a dimension, as shown in the following Screenshot:
Studying the SQL generated by VizQL to create the preceding visualization is particularly Insightful:
SELECT [‘World Indicators$’].[Infant Mortality Rate] AS [Infant Mortality
AVG([‘World Indicators$’].[Health Exp/Capita]) AS [avg:Health
FROM [dbo].[‘World Indicators$’] [‘World Indicators$’]
GROUP BY [‘World Indicators$’].[Infant Mortality Rate]
The Group By clause clearly communicates that Infant Mortality Rate is treated as a dimension. The takeaway is to note that VizQL enabled the analyst to change the field usage from measure to dimension without adjusting the source metadata. This on-the-fly ability enables creative exploration of the data not possible with other tools and avoids lengthy exercises attempting to define all possible uses for each field.
If you liked our article, be sure to check out Mastering Tableau which consists of more useful data visualization and data analysis techniques.