Tableau Tutorial: Defined, Explained, & Explored For Free

Last updated on 10th Jul 2020, Blog, Tutorials

What is Tableau?

Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data into the very easily understandable format.

Data analysis is very fast with Tableau and the visualizations created are in the form of dashboards and worksheets. The data that is created using Tableau can be understood by professional at any level in an organization. It even allows a non-technical user to create a customized dashboard.

The best feature Tableau are

• Data Blending
• Real time analysis
• Collaboration of data

The great thing about Tableau software is that it doesn’t require any technical or any kind of programming skills to operate. The tool has garnered interest among the people from all sectors such as business, researchers, different industries, etc.

Tableau Product Suite

The Tableau Product Suite consists of

• Tableau Desktop
• Tableau Public
• Tableau Online
• Tableau Server
• Tableau Reader

For clear understanding, data analytics in tableau can be classified into two section

1. Developer Tools: 

The Tableau tools that are used for development such as the creation of dashboards, charts, report generation, visualization fall into this category. The Tableau products, under this category, are the Tableau Desktop and the Tableau Public.

1. Sharing Tools: 

As the name suggests, the purpose of the tool is sharing the visualizations, reports, dashboards that were created using the developer tools. Products that fall into this category are Tableau Online, Server, and Reader.

Tableau Desktop

Tableau Desktop has a rich feature set and allows you to code and customize reports. Right from creating the charts, reports, to blending them all together to form a dashboard, all the necessary work is created in Tableau Desktop.

For live data analysis, Tableau Desktop provides connectivity to Data Warehouse, as well as other various types of files. The workbooks and the dashboards created here can be either shared locally or publicly.

Based on the connectivity to the data sources and publishing option, Tableau Desktop is classified into

• Tableau Desktop Personal: 

The development features are similar to Tableau Desktop. Personal version keeps the workbook private, and the access is limited. The workbooks cannot be published online. Therefore, it should be distributed either Offline or in Tableau Public.

• Tableau Desktop Professional: 

It is pretty much similar to Tableau Desktop. The difference is that the work created in the Tableau Desktop can be published online or in Tableau Server. Also, in Professional version, there is full access to all sorts of the datatype. It is best suitable for those who wish to publish their work in Tableau Server.

Tableau Public

It is Tableau version specially build for the cost-effective users. By the word “Public,” it means that the workbooks created cannot be saved locally, in turn, it should be saved to the Tableau’s public cloud which can be viewed and accessed by anyone.

There is no privacy to the files saved to the cloud since anyone can download and access the same. This version is the best for the individuals who want to learn Tableau and for the ones who want to share their data with the general public.

Tableau Server

The software is specifically used to share the workbooks, visualizations that are created in the Tableau Desktop application across the organization. To share dashboards in the Tableau Server, you must first publish your work in the Tableau Desktop. Once the work has been uploaded to the server, it will be accessible only to the licensed users.

However, It’s not necessary that the licensed users need to have the Tableau Server installed on their machine. They just require the login credentials with which they can check reports via a web browser. The security is high in Tableau server, and it is much suited for quick and effective sharing of data in an organization.

The admin of the organization will always have full control over the server. The hardware and the software are maintained by the organization.

Tableau Online

As the name suggests, it is an online sharing tool of Tableau. Its functionalities are similar to Tableau Server, but the data is stored on servers hosted in the cloud which are maintained by the Tableau group.

There is no storage limit on the data that can be published in the Tableau Online. Tableau Online creates a direct link to over 40 data sources that are hosted in the cloud such as the MySQL, Hive, Amazon Aurora, Spark SQL and many more.

To publish, both Tableau Online and Server require the workbooks created by Tableau Desktop. Data that is streamed from the web applications say Google Analytics, Salesforce.com are also supported by Tableau Server and Tableau Online.

Tableau Reader

Tableau Reader is a free tool which allows you to view the workbooks and visualizations created using Tableau Desktop or Tableau Public. The data can be filtered but editing and modifications are restricted. The security level is zero in Tableau Reader as anyone who gets the workbook can view it using Tableau Reader.

If you want to share the dashboards that you have created, the receiver should have Tableau Reader to view the document.

How does Tableau work?

Tableau connects and extracts the data stored in various places. It can pull data from any platform imaginable. A simple database such as an excel, pdf, to a complex database like Oracle, a database in the cloud such as Amazon webs services, Microsoft Azure SQL database, Google Cloud SQL and various other data sources can be extracted by Tableau.

When Tableau is launched, ready data connectors are available which allows you to connect to any database. Depending on the version of Tableau that you have purchased the number of data connectors supported by Tableau will vary.

The pulled data can be either connected live or extracted to the Tableau’s data engine, Tableau Desktop. This is where the Data analyst, data engineer work with the data that was pulled up and develop visualizations. The created dashboards are shared with the users as a static file. The users who receive the dashboards views the file using Tableau Reader.

The data from the Tableau Desktop can be published to the Tableau server. This is an enterprise platform where collaboration, distribution, governance, security model, automation features are supported. With the Tableau server, the end users have a better experience in accessing the files from all locations be it a desktop, mobile or email.

Tableau Architecture & Server Components

Tableau Server is designed in a way to connect many data tiers. It can connect clients from desktop, mobile, and web. Tableau Desktop is a robust data visualization tool. It is highly available and secure.

It can run on both virtual and physical machines. It is a multi-user, multi-process and multi-threaded system.

Providing such powerful features requires a robust architecture. Let’s study the Tableau Server Architecture in this tutorial.

In this tutorial, you will learn

• Tableau Server Architecture
• Data Server
• Data Connectors
• Components of Tableau Server
• Gateway
• Clients

Tableau Server Architecture

The various layers used in the Tableau server are given in the following architecture diagram

Tableau Architecture Diagram

Let’s study the different components of Tableau Architecture

1. Data Server

The primary component of Tableau Architecture is the Data sources it can connect to it.

Tableau can connect to multiple data sources. These data sources can be on-premise or remotely located. It can connect to a database, excel file, and a web application all at the same time. Tableau can connect data from heterogeneous environments. It can blend the data from multiple data sources. It can also make the relationship between various types of data sources.

1. Data Connectors

The Data Connectors provide an interface to connect external data sources to Tableau Data Server.

Tableau has in-built ODBC/SQL connector. This ODBC Connector can connect to any databases without using their native connector. Tableau has an option to select both live and extract data. Based on the usage, one can be easily switched between extracted and live data.

• Live Connection or Real time data:

Tableau can connect to real time data by linking to the external database directly. It uses the infrastructure of existing database system by sending dynamic MDX (Multidimensional Expressions) and SQL statements. This feature can link to the live data with Tableau rather than importing the data. It makes good the investment done by an organization on a fast and optimized database system. In many enterprises, the size of the database is huge and is updated periodically. In those cases, Tableau works as a front-end visualization tool by connecting to the live data.

• Extracted or In-memory data: 

Tableau has an option to extract the data from external data sources. We can make a local copy in the form of tableau extract file. It can extract millions of records in Tableau data engine with a single click. Tableau’s data engine uses storage such as RAM, ROM and cache memory to store and process data. Using filters, Tableau can extract few records from a huge dataset. This improves the performance, especially while working on massive datasets. Extracted or in-memory data allows the users to visualize the data offline, without connecting to the data source.

1. Components of Tableau Server

The different components present in a Tableau server are:

• Application Server
• VizQL Server
• Data Server

A) Application Server:

The application server is used to provide the authentications and authorizations. It handles the administration and permission for web and mobile interfaces. It assures security by recording each session id on Tableau Server. The administrator can configure the default timeout of the session in the server.

B) VizQL Server:

VizQL server is used to convert the queries from the data source into visualizations. Once the client request is forwarded to VizQL process, it sends the query directly to data source and retrieves information in the form of images. This image or visualization is presented to the user. Tableau server creates a cache of visualization to reduce the load time. The cache can be shared across many users who have the permission to view the visualization.

C) Data Server:

Data server is used to manage and store the data from external data sources. It is a central data management system. It provides metadata management, data security, data storage, data connection and driver requirements. It stores the relevant details of data set such as metadata, calculated fields, sets, groups, and parameters. The data source could extract data as well make live connections to external data sources.

1. Gateway

The gateway channelizes the requests from users to Tableau components. When the client makes a request, it is forwarded to external load balancer for processing. The gateway works as a distributor of processes to various components. In case of absence of external load balancer, gateway also works as a load balancer. For single server configuration, one primary server or gateway manages all the processes. For multiple server configurations, one physical system works as primary server while others are used as worker servers. Only one machine can be used as a primary server in Tableau Server environment.

1. Clients

The dashboards and visualizations in Tableau server can be viewed and edited using different clients. The Clients are Tableau Desktop, web browser and mobile applications.

Work Flow

As Tableau helps in analyzing lots of data over diverse time periods, dimensions, and measures, it needs a very meticulous planning to create a good dashboard or story. Hence, it is important to know the approach to design a good dashboard. Like any other field of human endeavor, there are many best practices to be followed to create good worksheets and dashboards.

Though the final outcome expected from a Tableau project is ideally a dashboard with story, there are many intermediate steps which needs to be completed to reach this goal. Following is a flow diagram of design steps that should be ideally followed to create effective dashboards.

Connect to Data Source

Tableau connects to all popular data sources. It has inbuilt connectors which take care of establishing the connection, once the connection parameters are supplied. Be it simple text files, relational sources, SQL sources or cloud data bases, Tableau connects to nearly every data source.

Build Data Views

After connecting to a data source, you get all the column and data available in the Tableau environment. You classify them as dimensions and measures, and create any hierarchy required. Using these you build views, which are traditionally known as Reports. Tableau provides easy drag and drop feature to build views.

Enhance the Views

The views created above needs to be enhanced further by the use of filters, aggregations, labeling of axes, formatting of colors and borders, etc.

Create Worksheets

Create different worksheets to create different views on the same or different data.

Create and Organize Dashboards

Dashboards contain multiple worksheets which are linked. Hence, the action in any of the worksheet can change the result in the dashboard accordingly.

Create a Story

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.

Benefits

1. Quickly create interactive plots

Volume, variety, and velocity, right? Today the 3V’s not only define Big Data, but also accurately summarize the projects being thrown at data scientists.

There are lots of them; every business problem is unique and they are coming at you at incredible speeds. I’ve had situations where 2-3 stakeholders came to me with multiple project requests on a daily basis!

So how do you deal with this onslaught of work and produce a great deliverable every time? One way is to get very good at ggplot2, shiny, htmlwidgets, dygraphs, googleVis & co. and hope that your pre-built templates fit the next project that comes your way so you can save some precious development time.

Another way is to use Tableau’s drag-n-drop interface to build many (beautiful) visuals in minutes. The interface can handle endless variations and helps tackle just about any project thrown your way with ease.

When I first start a project what I really want is to SEE my data. Ever since I started using Tableau, the first thing I do on a new project is throw all my data into this magic box. Drag-drop and I can see the trends, drag-drop and there are the anomalies, drag-drop and hmmm, that’s interesting, let me drill into that further… You get the point.

2. Build interactive dashboards using a GUI

With Tableau you can build interactive dashboards to empower your clients. It’s so easy that this has become my default option.

Now when somebody comes to me after project delivery and asks, “Can you do a very quick adjustment for me? Pretty pleeeeeease.”  I point them to their dashboard and say “You wanted this? Here you go. It’s interactive, so make as many changes as you like!” (like building a Shiny app using a GUI interface)

The best part is that dashboards can be deployed at enterprise level (Tableau Server) and can be viewed and interrogated on a laptop, tablet, and even mobile. Managing executives of your company are going to be your new best friends! It’s self-Service Analytics at its finest.

3. Connects to R

You can perform basic calculations and even run some simple stats in Tableau itself. But if that’s not enough and heavy artillery analytics is required, simply run your models in R, import results into Tableau and visualize away! Need to leverage R computations in real-time? Not a problem! Tableau has in-built support for R via Rserve.

These programs complement each other well and this allows you to harness the power of each for a great end result.

4. Growth

For the third time, Tableau has been named a leader in the Magic Quadrant for Business Intelligence and Analytics Platforms report by Gartner:

This doesn’t come as a surprise. With incredible year-on-year growth and record adoption rates globally, Tableau is setting itself up for long-term success. It’s completely expected that the company delivers one of the best analytics and visualization platforms out there.

And when you join the Tableau community, you will set yourself up for long-term success too. Akin to R followers, Tableau fans are extremely passionate about the tool. They are there to help each other and their numbers are growing rapidly.

In fact, Tableau is becoming so popular that many organizations require Tableau on your resume to even apply for their data science positions. I won’t be surprised if in 5 years this will be the norm. Just check out the growth for Tableau search terms on Google Trends:

5. Short learning curve

Tableau is extremely easy to learn. It’s such an intuitive tool that you can pick it up on the fly. With the right type of training, in less than 7 hours you will be seamlessly creating fully interactive MIS dashboards like this:

6. Pricing

The Professional Version of Tableau is priced with enterprises in mind: $1,999 + maintenance fee per license. But here’s the good news: Tableau has a completely free version of their software called Tableau Public.

With Tableau Public you cannot connect to as many Data Sources as with Tableau Professional and all visualizations have to be saved on a public server. Apart from that, Tableau Public is capable of producing the same incredible visualizations and dashboards as Tableau Professional, making it a great solution for learning the software.

Advantages of Tableau

1. Data visualization: Tableau is the data visualization tool. It provides the facilities for complex computations, data blending. It plays a vital role in real-time decision-making.
2. Quickly create interactive visualization: Tableau has a drag and drop facility. The user can create complex and interactive visuals within a minute.
3. Ease of implementation: Tableau provides multiple ways to create visuals. It becomes very easy to the end user, to choose a comfortable way of creating visuals.
4. Tableau can handle massive data: Tableau can manage large data efficiently — different visualizations created for large data, without affecting the performance of dashboards.
5. Use of other scripting languages in tableau: For better performance and complex table calculations, tableau allows the usage of other languages like R, python in the tableau.
6. Mobile support and responsive dashboard: Tableau dashboard has striking reporting feature. That feature allows the user to customize the dashboard to individual devices such as mobile or laptop. Tableau dashboard identifies automatically, which device used by the user to view the reports.

Disadvantages of Tableau

1. Scheduling Reports: Tableau does not provide the facility of an automatic refresh. Automatic refresh can be achieved with the help of scheduling. Tableau does not have an option of scheduling. Need a manual work after every modification or updation.
2. No custom visual imports: Tableau does not allow importing the custom visuals, and user needs to create it instead.
3. Custom formatting in Tableau: Tableau has the limited formatting option. It displays only 16 column of data only at a time. If user want to create visuals of more than 16 column or user want to create the same formatting for multiple columns, then the user needs to do it manually.
4. Screen Resolution on tableau dashboards: If the tableau’s developers screen resolution is different from the screen resolution of clients, then the layout of the dashboard get distorted. Moreover, the dashboard will be no longer responsive. In that case, either the screen resolution of developer and end user should be same or developer has to create a dashboard to view it on mobile.
5. Limited data pre processing: a Tableau is a visualization tool, it provides only basic facilities such as blending data, joining data but it does not provide cleaning data facility. Data cleaning is a necessary step.

Conclusion

As we conclude our brief study on data visualization, it is clear that the field is rich in potential applications in diverse disciplines, at the same time we need to be aware of its practical and ethical complexities. In the previous chapters, we have presented some important theoretical and practical principles to keep in mind when designing a data visualization. We have also discussed and critiqued several examples of data visualizations, learning common pitfalls and helpful tricks along the way. As we have seen, developing an effective and ethical data visualization is a complex process. In this chapter we will touch upon the future of data visualization and additional resources for data visualizers.