Self-service Business Intelligence Tools For Linux Lovers – This is the second post in a blog series about BI tools. The first article is about the evolution of business intelligence in the 21st century. This time we delve into one of the leading tools on the market. We’ll describe how Tableau differs from its key competitors, the components of the platform, licensing options, and more. We try to be comprehensive, but it’s impossible to consider or even mention every feature. Thoroughly describing a BI tool in a blog post is challenging. If you need a more detailed evaluation or would like to see Tableau in action with real-life content, please contact us.
Read our blog posts for new features introduced at Tableau Conference 2021, as well as an overview of Tableau’s product roadmap based on TC22 and TC21, and Tableau’s Minority Report at TC23 – Towards Augmented Reality, Generative AI, and Wireless Head to BI Direction Development.
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This is what Tableau says about their mission: to help people see and understand their content. Tableau’s goal is to be easy to use, so that everyone can use it and gain useful insights from it. Tableau was originally built based on Stanford University’s research on visualization; how to best support people’s natural ability to think visually and intuitively understand certain graphical presentations.
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Tableau Desktop does a great job of being the enterprise BI dinosaur, making analysis easier and even more fun (read the previous blog post for a reference to the dinosaur). The success and market penetration of Tableau Desktop meant that the platform needed to expand. Since then, Tableau Server, Online, Public, Mobile, and Prep have been released. Today, Tableau’s product is a comprehensive analytics platform that differentiates itself from its competitors.
The Transformation of Tableau Gain Insights Quickly and Easily In general, valuable insights can be obtained very quickly at the source using Tableau. Analyzing and creating visuals and dashboards is mostly very simple and fluid. There are out-of-the-box time hierarchies, drag-and-drop analysis templates available, and a host of easy-to-create calculations (running totals, moving averages, total shares, rankings, etc.). Preparation and modeling are also easy to use. Both of these can be done without deep technical knowledge and coding skills. Perhaps what I’m most grateful for in this space is how new features are released and old ones deprecated: in a way it just works. For example, when new in-memory storage replaced the old technology in 2018, it had very little user impact and maintenance. The same thing happened in 2020, when a new semantic model layer was introduced, and again, there was no laborious migration from old to new, everything just worked. Extraordinary creativity Tableau began as a visualization and visual analysis tool, so it is still very powerful. Tableau uniquely empowers users to be creative and original when analyzing and developing content. What does it mean? In other tools, you typically start by selecting the desired result you are looking for (visualization type, e.g. line, area, bar, pie, etc.), and then assign fields to the roles supported by the visualization type (e.g. value, legend, axis , tooltip, etc.). If the visualization doesn’t support what you need (such as size or small multiples), then there’s not much you can do.
Tableau works very differently: you can drag and drop fields onto the canvas and Tableau will visualize them in a suitable way. Some properties of fields can change dynamically: dimensions can be changed to measures, discrete fields can be converted to continuous fields, and vice versa. Almost any domain can be assigned to any role, and different types of visualizations can be combined. This approach is more flexible than any other tool I’ve used. However, this may seem complicated at first glance. Fortunately, Tableau has a Demo menu to help you create different visualizations and understand how the tool works. Once you get the hang of it, you can perform powerful visual analysis like never before.
Maps and Spatial Capabilities As mentioned earlier, the different types of visualizations in Tableau are very diverse and flexible, but maps and spatial analysis in particular are top-notch. Here’s a short list of why Tableau spaces are so powerful:
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A detailed city center map with a street map in the background, a building layer with dark gray polygons at the bottom, and a point layer at the top showing building area (size) and heating fuel (color).
Interaction Between Users and Visualizations A third Tableau strength is the ability for users to interact with visualizations and for developers to define precisely where and how those interactions occur. For example, interactions can be used to filter, highlight, show and hide layout objects, show tooltips, define parameter values and set objects, drill up and down, drill to another dashboard or to an external url. Interaction enables non-technical business users consuming pre-made content to gain more information and insights from a single dashboard without creating multiple dashboards or going into full self-service mode. Flexibility in Infrastructure and Governance Tableau is the exact same tool no matter how and where you choose to deploy (on-premises, public cloud, or SaaS). You can use Windows or Linux servers (or containers), as well as Windows and Mac computers as desktops. You can use different authentication options, user directories, and origins without being forced to depend on any cloud provider.
You have the same flexibility when creating content. Models can be created in exactly the same way and with the same functionality whether in extract or live mode. You can also combine ingest and live mode content on the same dashboard. Use the same scripting language when preparing and building visualizations. It is a powerful yet straightforward language to use. The flexibility remains when publishing content to the server/online. You can structure content into folders exactly how you like and apply security policies at the level of detail you need.
Active and enthusiastic user community The Tableau user community is more active and enthusiastic than other enterprise tool user communities. For example, Tableau Public has more than 3.7 million visualizations published by more than 1.5 million users. Anyone can browse and use these visualizations to learn about Tableau and how to use it. The community supports and helps with issues related to the tool, but I personally appreciate the work they do to spread understanding and share best visualization practices and examples. Main functions and workflow
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Tableau is everything a modern analytics platform should be. There are no major flaws, but there are clearly some areas for improvement, especially related to new features. Tableau can be used to master the entire visual analytics pipeline across multiple channels, from preparation to consumption. This is how the Tableau workflow usually works.
Prep If you need prep functionality, Tableau provides it in Tableau Prep. The tool can be used as a desktop client or directly in Tableau Server or Online. Tableau Prep was built with the same philosophy as other components in the platform for ease of use. Creating steps and the overall workflow is so intuitive, the process is easy to understand and it’s easy to see what’s happening along the way. Tableau Prep provides standard collating functions for joins, unions, pivots, cleansing, and aggregations. You can also add new rows and use custom R or Python scripts to calculate new insights. Result sets can be pushed to a file, database, or as a Tableau extract. Prepared workflows can be shared and reused, and planning and execution can be monitored through the Prep Conductor add-on. Modeling The most common modeling is done using the Tableau Desktop client. The exception is if you use Tableau Prep or some external tool with the Tableau API to create and refresh extracts. Using Tableau Desktop, you can connect to sources, select the desired objects and define joins and relationships between objects. Tableau models today consist of two layers: a physical layer and a logical (semantic) layer. The separation of the two makes it possible to reuse the same Tableau model for different purposes. The logic layer functionality was released with version 2020.2, which is a major update to the model.
When modeling, you choose whether to use a live connection or pull to Tableau’s columnar in-memory storage. No matter what you choose, you’ll have access to the exact same features and functionality, and you’ll also be able to change the connection type later. One possibility is to use incremental refresh so that only new rows are inserted into the extract. Best practice is to validate and define all field types, default formats
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