Alteryx vs tableau LEARNOVITA

Alteryx vs Tableau | Know Their Differences and Which Should You Learn?

Last updated on 03rd Nov 2022, Artciles, Blog

About author

Samual Agastin (Tableau Sr Developer Engineering )

Samual Agastin is a Tableau Sr Developer Engineering, his role can read API products and apps, as well as edit API proxies, shared flows, and KVMs. He has 5+ years of experience in SQL Server data, MuleSoft, ESB, IBM API, AWS API RESTful, and Apache Kafka.

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    • In this article you will get
    • 1.What is a Alteryx?
    • 2.What is Tableau?
    • 3.Benefits of using a Alteryx with Tableau
    • 4.Major differences between the Alteryx and Tableau: Tableau vs. Alteryx
    • 5.Learn using a Alteryx
    • 6.Conclusion

What is a Alteryx?

One can say it is a tool that aids in the gathering data from different sections of a company and present them in a forms of charts, graphs, etc. Data from the entire organization can be processed and analyzed to get idea of the progress of the organization by using Alteryx tool. Alteryx has features such as simple integration with a Tableau, stunning mobile-ready dashboards, automatic updates and a data notifications, server API, importing various types and sizes of data, sharing and embedding of a dashboard, etc.

Alteryx architecture

What is Tableau?

Data is the powerful and Tableau is tool that can help to harness its power for the progress of a business. And can collect and save all the data of a company on the single platform and analyze it in detail to get a meaningful business insights with help of a Tableau. Tableau has features like an encrypted storage, spatial analysis, and a mapping, export-import package, etc.

Benefits of using a Alteryx with Tableau

Tableau is an inarguably the most powerful tool to gather and analyse a data from a different sources. Tableau makes a understanding the visualizations seem like a piece of cake and helps in taking a right decision based on interpretation. However, as modern of data types emerge, it becomes complex to collect and analyse a data from newly emerged data sources like a social media and big data. The company will need a team of an IT specialists to do this task.

Here, an Alteryx comes in for a help. With Alteryx, will be able to collect and analys all the data need without any hassle as it permits to combine data from each source, despite its location. These data sources are then combined with a third-party demographic data based on its location, and single work-flow is created. By this method, Alteryx and tableau together give the best data set available without a seeking help from a data specialists.

Major differences between the Alteryx and Tableau: Tableau vs. Alteryx

Connect the data need to analyze:

There is the Tableau icon on the left side at a topmost corner on a start page itself.Click on it and a sidebar with a list of various data types and places will be displayed on a screen.

Drag and drop to get preview:

Just have to drag a necessary elements and drop them to create the analytics chart of data.

Highlight the results:

Tableau allows to use a separate colors and filters to display the findings in an analytic graph or a chart. By doing this, and can get more precise results. Understand a data from perspective of a geography: Just like charts, can also form a map to understand a data according to geography. Map views make it simple to compare.Sales, an effect of marketing strategies, and the other business parameters worldwide and can make an informed decisions after the analyzing these map views.

Understand nitty-gritty of a data:

As get a geographical view of a sales, can easily find out where a products or services are running at the low or negative profit rate. This will help to get a some meaningful insights into the sales.

Showcase your insights:

Creating analytics sheets is the various job and showcasing them properly is yet be another creative task. Can create a stunning dashboards in Tableau by which can present all the data sheets together.

Create representable stories:

A better way than indulging a colleagues in a bland presentation is to make the story for a better understanding. On Tableau, can easily create a story to make it simple for a team to understand.

Display results:

After everything is be done, can display the results to people living in various locations by a Tableau.

Tableau library:

Tableau’s comprehensive a Library of data can help to learn many other features that might help in data analytics projects.

Difference between Alteryx and Tableau

Learn using a Alteryx

Similar to the Tableau, first have to get registered at a Alteryx. Alteryx has a graphical user interface and it consists of a some components. Here’s the glimpse of those components and how to used them.

Similar to the Tableau, first have to get registered at a Alteryx. Alteryx has a graphical user interface and it consists of a some components. Here’s the glimpse of those components and how to used them.

Tool Palette: This component has a names and images of all tools. Just have to drag a tool to a workflow canvas to start working.

Workflow Canvas: Workflow canvas is be nothing but space where workflow is be created.

Properties Window:The function of component is to provide the options for configuring a specifications for tools used in a workflow. Even if not using the any tools, the properties of window adapts within a workflow.

Overview Window: This window provides a graphical representation of a workflow canvas.

Output Window: This window shows a status logs. Can see this window on the occasions such as a starting and end of workflow.


Alteryx and Tableau both are most used platforms for a performing data analytics related operations. Tableau is the tool more useful for a data collection and visualization and thereby helps to make an appropriate business decisions.However, an emergence of various datatypes has made it complex to collect and analyze a data. Alteryx plays the essential role here, and it helps in combining a modern datatypes in a way that they can be used with a Tableau.Both of these tools are be uniquely important in their own way but using together can get a best results in analyzing a difficult data.

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