“The art and practice of visualizing data is becoming ever more important in bridging the human-computer gap to mediate analytical insight in a meaningful way.” ―Edd Dumbill
Tableau is a powerful data visualization and discovery tool. It is an important part of a data analyst or data scientist’s – skill set, with many organizations specifying it as a key skill in job adverts.
In this article, we’ll take a look at few things in Tableau you need to know to successfully make a mark in your business intelligence career.
While architecture of traditional BI tools has hardware limitations, Tableau does not have such dependencies and it can function independently and requires minimum hardware support. Traditional tools are based on a complex set of technologies when Tableau is based on Associative Search technology making it intuitive, fast and dynamic. Tableau supports in-memory, multi-thread and multi-core computing and more advanced capabilities while traditional BI tools do not offer such functionalities.
- Various Tableau products
Tableau Desktop is a self service business analytics and data visualization suite that anyone can use. With tableau desktop, you can extract massive data offline from your data warehouse for live up to date data analysis.
Tableau Online / Tableau Server is an online hosting platform designed for enterprise users. It lets users working on Tableau publish and share dashboards across organization and teams.
Tableau Reader is a free desktop application that enables you to open and view visualizations that are built in Tableau Desktop.
Tableau Public is a free Tableau software which you can use to make visualizations but you will need to save your workbook or worksheets in the Tableau Server for anyone else to view them.
- Different data types in Tableau
All fields in a data source have a data type. The data type reflects the kind of information stored in that field, for example integers (410), dates (1/23/2015) and strings (“Wisconsin”). The data type of a field is identified in the Data pane by one of the icons shown below.
Data type icons in Tableau
| Icon | Data type |
 |
Text (string) values |
 |
Date values |
 |
Date & Time values |
 |
Numerical values |
 |
Boolean values (relational only) for example True/False |
 |
Geographic values (used with maps) |
 |
Cluster Group |
Source: Tableau website
- Measures and Dimensions in Tableau
Measures contain numeric, quantitative values that you can measure. Measures can be aggregated. When you drag a measure into the view, Tableau applies an aggregation to that measure (by default).
Dimensions, on the other hand, contain qualitative values (such as names, dates, or geographical data). You can use dimensions to categorize, segment, and reveal the details in your data. Dimensions affect the level of detail in the view.
- Ways to connect data in Tableau
We can either connect live to your data set or extract data into Tableau.
- Live: Connecting live to a data set leverages its computational processing and storage. New queries will go to the database and will be reflected as new or updated within the data.
- Extract: The Extract API allows you to programmatically extract and combine any data sources for use in Tableau. There can be multiple data source connections to different sources in the same workbook. Each connection will show up under the Data tab on the left sidebar.
The benefit of Tableau extract over live connection is that extract can be used anywhere without any connection and you can build your own visualization without connecting to database.
You can read a complete section on how to extract data in Tableau from this book, Learning Tableau 2019 – Third Edition, written by Joshua Milligan. This book takes you through the foundations of the Tableau 2019 paradigm to the advanced topics.
- Joins and Blends in Tableau
Joining tables and blending data sources are two different ways to link related data together in Tableau. Joins are performed to link tables of data together on a row-by-row basis. Blends are performed to link together multiple data sources at an aggregate level.
- Different filters in Tableau and different use cases in which these filters are more relevant than others
In Tableau, filters are used to restrict the data from database. 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 detail. Tableau offers multiple ways to filter data.
If you want to limit the scope of 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. These filters are applied before any other filters.
- 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.
- Dual axis in Tableau
Dual Axis is an excellent phenomenon supported by Tableau that helps users view two scales of two measures in the same graph. Many websites like Indeed.com and other make use of dual axis to show the comparison between two measures and their growth rate in a septic set of years. Dual axis let you compare multiple measures at once, having two independent axis layered on top of one another.
- Key components of a Tableau Dashboard
- Horizontal – Horizontal layout containers allow the designer to group worksheets and dashboard components left to right across your page and edit the height of all elements at once.
- Vertical – Vertical containers allow the user to group worksheets and dashboard components top to bottom down your page and edit the width of all elements at once.
- Text – All textual fields.
- Image Extract – A Tableau workbook is in XML format. In order to extract images, Tableau applies some codes to extract an image which can be stored in XML.
- Web [URL ACTION] – A URL action is a hyperlink that points to a Web page, file, or other web-based resource outside of Tableau. You can use URL actions to link to more information about your data that may be hosted outside of your data source. To make the link relevant to your data, you can substitute field values of a selection into the URL as parameters.
If you want to learn how to design dashboards in Tableau, this book Learning Tableau 2019, will give you a step by step process for designing dashboards.
- Why automate reports in Tableau
Once you have automated reporting, you’ll have time to spend on innovative projects. What can be done manually could be performed by automation, delivering the same results in a fraction of the time. Reducing such a time-consuming and repetitive task will make you more productive, and more efficient.
- What is story in Tableau? Why would create a story and what are they used for?
A story is a sheet that contains a sequence of worksheets or dashboards that work together to convey information. You can create stories to show how facts are connected, provide context, demonstrate how decisions relate to outcomes, or simply make a compelling case. Each individual sheet in a story is called a story point.
The primary objective of creating stories in Tableau is to communicate data to a certain audience with an intended result.
- How can you create stories in Tableau?
There is a feature in Tableau named as Stories that allows you to tell a story using interactive snapshots of dashboards and views. The snapshots become points in a story. This allows you to construct guided narrative or even an entire presentation.
Read this chapter, ‘Telling a Data Story with Dashboards’ from this book, Learning Tableau 2019, to create insightful dashboards in Tableau.
- How to embed views into Webpages?
You can embed interactive Tableau views and dashboards into web pages, blogs, wiki pages, web applications, and intranet portals. Embedded views update as the underlying data changes, or as their workbooks are updated on Tableau Server. Embedded views follow the same licensing and permission restrictions used on Tableau Server. That is, to see a Tableau view that’s embedded in a web page, the person accessing the view must also have an account on Tableau Server.
Alternatively, if your organization uses a core-based license on Tableau Server, a Guest account is available. This allows people in your organization to view and interact with Tableau views embedded in web pages without having to sign in to the server. Contact your server or site administrator to find out if the Guest user is enabled for the site you publish to.
- What is Tableau Prep? Can we clean messy data with Tableau?
Tableau Prep extends the Tableau platform with robust options for cleaning and structuring data for analysis in Tableau. In the same way that Tableau Desktop provides a hands-on, visual experience for visualizing and analyzing data, Tableau Prep provides a hands-on, visual experience for cleaning and shaping data.
If you wish to know more about Tableau Prep or how to clean messy data to create powerful data visualizations and unlock intelligent business insights, read this book Learning Tableau 2019, written by Joshua N. Milligan.
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