Tableau vs Power BI: Comparing the Data Visualization Tools

In analytics, Tableau is the leading visualisation tool. Its rich analytical features and attention to data details are the reason behind its popularity. Power BI, on the other hand, is preferred by professionals who are more comfortable with Microsoft Office365. The users can connect Excel queries, data models and are able to report to the dashboard.
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While the usage of both these tools might depend on many factors, here is a quick comparison of the two popular tools on various functionalities.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

1. Performance
One of the crucial differences between Tableau and Power BI is that Tableau is an extensible platform which not only provides visualisations but also helps in gaining a better understanding of the data. Both the tools are excellent in visualisation but when it comes to the depth of data, Tableau helps an analyst to dive deeper into the data by performing “what-if” analysis on the data.

2. Flexibility
A user can deploy tableau on-premises, on the public cloud on Microsoft Azure, Amazon Web Services, or Google Cloud Platform, or on Tableau Online. While PowerBI, an upgradation of Microsoft Excel can be said as not so flexible as it serves only as a software-as-a-service model.
Power BI vs Tableau: A Data Analytics Duel - TechnologyAdvice

3. User Interface
Tableau is mainly designed keeping data analysts in mind. The richer analytical capabilities for visualisation helps an analyst gain insight into large datasets. It allows the user to create customised dashboards which can be considered as more of a pro-level. Microsoft PowerBI is simpler than Tableau and offers a better intuitive interface, especially for the beginners. This tool can be used by a coder as well as a non-coder.

4. Visualisation
Power BI focuses on data modelling and offers features of data manipulation and then provides data visualisation while tableau strictly focuses on data visualisation. Learn more from Power BI online course

5. AI-Powered
PowerBI tool with its Microsoft Flow and its AI builder tool can help in building apps with a layer of intelligence. With the advantages of Microsoft AI, the user can prepare data, build machine learning models and gain insights from both structured and unstructured data. On the hand, Tableau is working on natural language capabilities to simplify analytics and help the users who have no prior data analysis experience, known as Ask Data. Recently, Tableau has also announced the beta version of Explain Data, a new AI-powered feature to help users understand the “why” behind unexpected values in their data.

6. Price
PowerBI offers two subscription offerings, Power BI Pro and Power BI Premium. Power BI Pro is priced at .99 per user per month which is a self-service BI where user can collaborate, publish, share and perform ad-hoc analysis. Whereas Power BI Premium is priced at ,995 per month per dedicated cloud compute and storage resource. Here, the user can perform big data analytics, advanced administration and more.

Tableau, on the other hand, offers three subscriptions — Tableau Creator, Explorer, and Viewer. Tableau Creator is priced at user per month and it includes Tableau Desktop, Prep Builder and one creator license of Tableau Server. Tableau Explorer is priced at per user per month where they can explore the trusted data with self-service analytics and the Tableau Viewer is priced at user per month and here the user can view and interact with dashboards and visualisations in a secured way. These pricings are for teams and organisations where multiple users/viewing is required.
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Tableau vs Power BI: Comparing the Data Visualization Tools

Tableau vs Power BI: Comparing the Data Visualization Tools

In analytics, Tableau is the leading visualisation tool. Its rich analytical features and attention to data details are the reason behind its popularity. Power BI, on the other hand, is preferred by professionals who are more comfortable with Microsoft Office365. The users can connect Excel queries, data models and are able to report to the dashboard.
This is image title

While the usage of both these tools might depend on many factors, here is a quick comparison of the two popular tools on various functionalities.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

1. Performance
One of the crucial differences between Tableau and Power BI is that Tableau is an extensible platform which not only provides visualisations but also helps in gaining a better understanding of the data. Both the tools are excellent in visualisation but when it comes to the depth of data, Tableau helps an analyst to dive deeper into the data by performing “what-if” analysis on the data.

2. Flexibility
A user can deploy tableau on-premises, on the public cloud on Microsoft Azure, Amazon Web Services, or Google Cloud Platform, or on Tableau Online. While PowerBI, an upgradation of Microsoft Excel can be said as not so flexible as it serves only as a software-as-a-service model.
Power BI vs Tableau: A Data Analytics Duel - TechnologyAdvice

3. User Interface
Tableau is mainly designed keeping data analysts in mind. The richer analytical capabilities for visualisation helps an analyst gain insight into large datasets. It allows the user to create customised dashboards which can be considered as more of a pro-level. Microsoft PowerBI is simpler than Tableau and offers a better intuitive interface, especially for the beginners. This tool can be used by a coder as well as a non-coder.

4. Visualisation
Power BI focuses on data modelling and offers features of data manipulation and then provides data visualisation while tableau strictly focuses on data visualisation. Learn more from Power BI online course

5. AI-Powered
PowerBI tool with its Microsoft Flow and its AI builder tool can help in building apps with a layer of intelligence. With the advantages of Microsoft AI, the user can prepare data, build machine learning models and gain insights from both structured and unstructured data. On the hand, Tableau is working on natural language capabilities to simplify analytics and help the users who have no prior data analysis experience, known as Ask Data. Recently, Tableau has also announced the beta version of Explain Data, a new AI-powered feature to help users understand the “why” behind unexpected values in their data.

6. Price
PowerBI offers two subscription offerings, Power BI Pro and Power BI Premium. Power BI Pro is priced at .99 per user per month which is a self-service BI where user can collaborate, publish, share and perform ad-hoc analysis. Whereas Power BI Premium is priced at ,995 per month per dedicated cloud compute and storage resource. Here, the user can perform big data analytics, advanced administration and more.

Tableau, on the other hand, offers three subscriptions — Tableau Creator, Explorer, and Viewer. Tableau Creator is priced at user per month and it includes Tableau Desktop, Prep Builder and one creator license of Tableau Server. Tableau Explorer is priced at per user per month where they can explore the trusted data with self-service analytics and the Tableau Viewer is priced at user per month and here the user can view and interact with dashboards and visualisations in a secured way. These pricings are for teams and organisations where multiple users/viewing is required.
Take your career to new heights of success with Power BI online training Hyderabad

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Comparing Power BI with other tools

the Business Intelligence (BI) world has been moving towards self-service BI. As expected, several vendors created tools empowering regular users to gain insights from their data. Among the many, there is Power BI. Nowadays, users want to understand the differences between Product X and Power BI.

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One of the most common questions in conferences and user group sessions is likely, “can you provide a comparison between this product and Power BI?”.

The answer is almost always, “No, I cannot compare them, because they are too different”. First, one needs to understand the deep difference between Power BI and most other reporting tools on the market. Only later does a comparison make any sense. As a matter of fact, I think Power BI can be compared to only a few products on the market today. I would like to add my point of view to the discussion.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Indeed, Power BI is a tremendously powerful data modeling tool that happens to come with a pretty face; most other products are beautifully crafted reporting tools with a pretty face. The only thing they have in common is the pretty face. If you stop at what they have in common, you are only comparing a small fraction of the whole product, and that would be unfair.

To go further, a deeper understanding of basic BI concepts is needed.

Beware: this article is biased. I love Power BI and I make my living out of it. Nevertheless, I am a BI professional; I started working with Business Intelligence many years ago and I have gathered experience that I can share. I will try to be as fair as I can in this post, as my goal is not to provide a comparison with any tool. The goal of this post is to help you understand what you really need to evaluate when making (or reading) any comparison between different BI products.

At the top level, any Business Intelligence solution is composed of three layers:

Raw data: these are the data sources that one wants to analyze. Raw data comes as is.
Semantic model: this is where data is re-arranged to optimize it for analysis. Here you also define the calculations required by the reports.
Reports: these are the nice dashboards you can build with the tool.

Power BI manages all three layers: you start from raw data, you can build a semantic layer, and finally you prepare reports. Most other reporting tools are focused on the last layer and are limited in the previous two. In other words, they are missing the capability to build a real semantic layer. It is important to clarify what a semantic layer is, to understand what you would miss by choosing a different product.

In the old ages of BI, there was a clear separation between users and developers. A BI developer would build a project to help users extract insights from their data, and build reports. Users did not need to understand tables, relationships, or calculations. The developer oversaw shaping the tables, providing predefined calculations and giving sensible names to entities. Leveraging the semantic model, users did not have to know DAX, MDX or SQL.

A semantic model lets users interact with business entities like customers, sales, and products. Users would place those entities in reports made with Excel or with other reporting tools. Regular users were happy with just Excel and a Pivot Table. More advanced users wanted more powerful tools, and this led to the creation of several reporting tools with their ad-hoc programming language to create more advanced formulas. Regardless, the important thing is that no matter how powerful those tools are, they were still reporting tools based on the existence of a previously crafted semantic model. No semantic model, no reporting.

Picture this: a BI tool lets a developer build a semantic model. A reporting tool lets a user build a report on top of an existing semantic model. You need both to create a BI solution.Learn more from Power BI online course

Unfortunately, building a BI project takes time. Users were hungry for reports. This led to the start of the Self-Service BI era. Self-service BI is the idea of users building reports themselves, to reduce development time and to build a democratic knowledge about data. Sounds cool and terrifying at the same time.

Anyway, this is where we are today. Obviously, driven by the market several vendors started to build self-service BI tools. A few new products appeared on the market. Rather, existing tools evolved into new ones, targeting self-service BI. Keep in mind: any self-service BI tool requires the functionalities to build both the semantic model and the report in the same tool. Thus, depending on where you start, you have two options to have an existing product evolve into a self-service product:

If you already have a semantic model tool, you need to add reporting capabilities. You need to make it easier to use, because the target is no longer a BI professional but a regular user instead.
If you already have a reporting tool, you need to add the capability to build a semantic model because your users need to massage the data and build calculations on top of the resulting model.

In both cases, in the end you obtain a tool that mixes the capabilities to create a semantic model and to build reports. After this first step, you can add tons of different features like sharing with other users, building wizards to automatically connect to other services, improving the formula language and so on. But the core is always the same: a semantic model and a reporting tool, bound together in a nice package.

Even though we consider Power BI to be a new product, it is actually the evolution of Power Pivot and Analysis Services Tabular (semantic model), Power Query (querying tool), and Power View (the first version of the reporting tool released with Excel and SharePoint). Other vendors took similar steps, with different starting points. It is fair to say that several vendors started from a reporting tool, adding the semantic model to it.

Now, if you need to compare two BI tools, you need to compare at least these two features: the semantic model and the tools to build a report.

Say you want to compare Product X with Power BI; you show me how easy it is to build a gorgeous report on top of an SQL view, much easier and much more powerful than Power BI. Cool, but you are only comparing a fraction of both products. Reporting-wise, sure, Product X is better than Power BI. But there are other considerations: can you load multiple tables in Product X? Can you build relationships between them? Can you use a programming language to author complex calculations that involve scanning different tables? All these operations belong to the semantic model. A fair comparison needs to apply to all the features.

This is what Power BI offers you:

Power Query – a data transformation tool which is easy to use and yet incredibly powerful. It can load virtually anything and join data from different sources.
A modeling environment where you can build different kinds of relationships between tables and build powerful models. It does not hurt that it runs on top of one of the fastest databases I have ever seen.
DAX – a programming language which is not easy, but lets you author nearly any query and calculation. Yes, on this I am biased for sure!
Power BI – a reporting engine which is very good in building dashboards and reports. It can also be extended with custom visuals and third-party products.

Then, there is web-based reporting and sharing, a mobile experience, the ability to load from nearly any data source in the cloud or on premises and many other useful features. Yet, the core is composed of the four features above. If you want to compare apples to apples, you need to compare at least these four parts. Be mindful: you need all of them. A tool that requires you to build a single table because it does not let you relate two tables is nothing but a nice reporting tool. Comparing it to Power BI does not make much sense to me.

Moreover, it does not come by chance that to learn Power BI, one needs to learn new programming languages. Each feature has its own language, and this is just the right thing to have.

Finally, reporting. Reporting is only the last part, even though it is the most visible one. You might find other products are better than Power BI when it comes to reporting. This is fine, if you are aware that you are only comparing a fraction of Power BI with the whole of Product X.

I love Power BI, and I would really love to see a fair comparison between Power BI and any other product. We could learn a lot from the topic. But for it to be fair, it cannot just be based on how easy it is to build a pie chart (just kidding! You are not using a pie chart, are you?). One needs to evaluate everything both products have to offer.

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Power BI vs Tableau

In your search for a Business Intelligence (BI) or data visualization tool, you have probably come across the two front-runners in the category: Power BI and Tableau. They are very similar products, and you have to look quite closely to figure out which product might work the best for you. I work for Encore Business Solutions; a systems partner that specializes in both Power BI and Tableau. We’ve seen more than a few scenarios in which Tableau was being used when the company really should have gone with Power BI, and vice-versa. That was part of the inspiration for this side-by-side comparison.

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Unfortunately, the internet is full of auto-generated and biased pages regarding which product trumps the other. The truth is, the best product depends more on you, your organization, your budget, and your intended use case than the tools themselves. It is easy to nit-pick at features like the coding language that supports advanced analysis, or the type of maps supported — but these have a minimal impact for most businesses. I’m going to do my best to stay away from these types of comparisons.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

In writing this comparison, I did a lot of research. The result was more than just this article: I also created a tool that can generate a recommendation for you based on your response to a short questionnaire. It will generate a score for both Power BI and Tableau, plus provide a few other things to think about.

Tableau Software
Founded in 2003, Tableau has been the gold-standard in data visualization for a long time. They went public in 2013, and they still probably have the edge on functionality over Power BI, thanks to their 10-year head start. There are a few factors that will heavily tip the scales in favour of Tableau, which I’ll cover in the next few paragraphs.

Tableau: Key Strengths
Let’s make one thing clear from the start: if you want the cream of the crop, all other factors aside, Tableau is the choice for you. Their organization has been dedicated to data visualization for over a decade and the results show in several areas: particularly product usability, Tableau’s community, product support, and flexible deployment options. The range of visualizations, user interface layout, visualization sharing, and intuitive data exploration capabilities also have an edge on Power BI. Tableau offers much more flexibility when it comes to designing your dashboards. From my own experience, Tableau’s functionality from an end-user perspective is much farther ahead of Power BI than the Gartner Magic Quadrant (below) would have you believe.

Tableau built their product on the philosophy of “seeing and exploring” data. This means that Tableau is engineered to create interactive visuals. Tableau’s product capabilities have been implemented in such a way that the user should be able to ask a question of their data, and receive an answer almost immediately by manipulating the tools available to them. I have heard of cases in which Tableau actually declined to pursue the business of a customer in the scenario that the customer didn’t have the right vision for how their software would be used. If you just want something to generate reports, Tableau is overkill.

Tableau is also much more flexible in its deployment than Power BI. You can install the Tableau server in any Window box without installing the SQL server. Power BI is less flexible which I will discuss in Power BI Weaknesses.

Tableau can be purchased on a subscription license and then installed either in the cloud or an on-premise server.

Finally, Tableau is all-in on data visualization, and they have their fingers firmly on the pulse of the data visualization community’s most pressing desires. You can expect significant future improvements in terms of performance when loading large datasets, new visualization options, and added ETL functions.

Tableau Weaknesses
Unfortunately, Tableau comes at a cost. When it comes to the investment required to purchase and implement Tableau – 9 times out of 10 it will be more expensive than Power BI, by a fair margin. Often, Tableau projects are accompanied by data-warehouse-building endeavours, which compound the amount of money it takes to get going. The results from building a data warehouse and then hooking up Tableau are phenomenal, but you’ll need an implementation budget of at the very least $50k – plus the incremental cost of Tableau licenses. Learn more from Power bi online course

Of course, a data warehouse is not a requirement. Tableau connects to more systems out-of-the-box than Power BI. However, Tableau users report connecting to fewer data sources than most other competing tools. Overall, considering the investment required to implement a data warehouse is a worthy indicator of the commitment required to get the most out of Tableau.

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Power BI
Power BI is Microsoft’s data visualization option. It was debuted in 2013, and has since quickly gained ground on Tableau. When you look at Gartner’s most recent BI Magic Quadrant, you’ll notice that Microsoft is basically equal to Tableau in terms of functionality, but strongly outpaces Tableau when it comes to “completeness of vision”. Indeed, the biggest advantage of Power BI is that it is embedded within the greater Microsoft stack, which contributes to Microsoft’s strong position in the Quadrant.

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Power BI: Key Strengths
Though Tableau is still regarded by many in the industry as the gold standard, Power BI is nothing to scoff at. Power BI is basically comparable to all of Tableau’s bells and whistles; unless you care deeply about the manifestation and execution of small features, you’re likely to find that Power BI is fully adequate for your BI needs.

As I mentioned, one of the biggest selling points of Power BI is that it is deeply entrenched in the Microsoft stack – and quickly becoming more integrated. It’s included in Office 365, and Microsoft really encourages the use of Power BI for visualizing data from their other cloud services. Power BI is also very capable of connecting to your external sources.

Because Power BI was originally a mostly Excel-driven product; and because the first to adopt Microsoft products are often more technical users, My personal experience is that Power BI is especially suitable for creating and displaying basic dashboards and reports. My own executive team really likes being able to access KPIs from the Office portal, without having to put much time into the report’s creation, sharing, and interactivity.

Power BI’s biggest strength; however, is its rock-bottom cost and fantastic value. For a product that is totally comparable to the category leader, it’s free (included in Office 365) for basic use and $10/user/month for a “Pro” license. This increases adoption of the product as individuals can use Power BI risk-free. For companies that don’t have the budget for a large Business Intelligence project (including a data warehouse, dedicated analysts, and several months of implementation time), Power BI is extremely attractive. Companies that are preparing to “invest” in BI are more likely to add Tableau to their list of strongly considered options.

Power BI is available on a SaaS model and on-premise; on-premise is only supported by Power BI Premium licensing.

Microsoft is also investing heavily in Power BI, and they’re closing the small gaps in their functionality extremely fast. All of those little issues some users have with Power BI are going to disappear sooner rather than later.

Power BI Weaknesses
As I’ve mentioned, Tableau still has the slight edge on Power BI when it comes to the minutiae of product functionality; mostly due to their 10-year head start. But perhaps Power BI’s greatest weakness is its lack of deployment flexibility. For Power BI on-premise you need to install the Power BI Report Server as well as the SQL Server.

I also mentioned that Tableau works well for users with large amounts of data and for users that want on-premise systems. You should be aware that there are some new features being added to Power BI via Power BI Premium that help catch Microsoft up to Tableau in the areas of large datasets and on-premise capabilities – but Power BI Premium adds significant cost, and these features are relatively new. Tableau still reigns in these areas.

To get more knowledge of Power BI and its usage in the practical way one can opt for Power bi online training Hyderabad from various platforms. Getting this knowledge from industry experts like IT Guru may help to visualize the future graphically. It will enhance skills and pave the way for a great future.

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Which Is Better? Power BI Vs. Tableau

Power BI Vs. Tableau: A Useful Comparison
We can choose the best product depending upon the organisation’s budget and requirements. It is effortless for us to pick features such as maps support, coding language, etc. The Microsoft systems that are used by the Power BI are SQL, Excel, and Azure whereas Tableau is very much used to create beautiful visualisations.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Tableau can see also more focused on bigger budget environments. Power BI is suitable for the toolset, and Tableau is very flexible to use.

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Power BI
Power BI is nothing but a business analytics service provided by Microsoft. The aim of it to provide business intelligence and interactive visualisations. It is a cloud-based service with the desktop-based interface. It can offer some warehouse capabilities like data discovery, data preparations, and interactive dashboards.

Features Of Power BI

  • Content packs
  • Print Dashboard
  • Get data source
  • Custom visualisation
  • Natural language Q&A
  • Tableau
    Tableau is a data visualisation tool which is used in the business intelligence industry. It can change the raw data into an understandable format without the requirements of the coding and technical skills. The data analysation is very fast in the tableau. The visualisations can be done in worksheets and dashboards. It can be used to provide actionable insights and creates dashboards.Get more skills from Power BI training

Features Of Tableau

  • Data Blending
  • No need of technical knowledge
  • Real-time analysis
  • Data Collaboration
  • DAX Analysis function
  • Power BI Vs. Tableau
    In recent times, both the power BI and Tableau are key performers in the business intelligence tools. Power BI is the younger invention than Tableau, but it is a very close competitor for Tableau. Both of them have their features, strengths, and weaknesses. They are suitable according to the requirements.

Let’s see the complete comparisons both Power BI and Tableau under the following parameters:

  • Deployment
  • Functionality
  • User Interface
  • Cost
  • Data Visualisation
  • Bulk data handling capabilities
  • Product support
  • Programming tools support
  • Integration
  • Deployment

The word deployment means to bring resources into effective action. When compared to Power BI, Tableau has more flexible deployment options. Tableau has got both clouds compared options and on-premises whereas Power BI is available in the SaaS model. If there are no requirements for the SaaS, then Power BI is doesn’t work for you. So, in this deployment parameter Tableau is the leader.

Functionality
Here, the word functionality means how both the tools work. Tableau can able to answer the user’s queries, but Power BI is not capable of answering every question. The depth of Tableau is modernised than that of the Power BI.

User Interface
Tableau has an intelligent User Interface and can able to create the dashboards easily. Power BI Interface is very easy to learn whereas Tableau is a little difficult. Because of its simple usage, Power BI is more preferable for the users.

Cost
Tableau is costlier than Power BI. By building data warehousing in the tableau, we can get more out of it. Power BI is inexpensive, and the professional Power BI cost is $10. Tableau crost will be around $35 per user per month. If you want to start a business, initially it is best to go for Power BI and later on the move to Tableau.

Bulk Data Handling Capabilities
Tableau handles more data than the Power BI. Power BI faults handling bulk data very slowly which cannot use the import functionality. Tableau can manage huge data.

Product Support
There is no any particular differences under the product support category. Both can be used according to the requirements. But, Power BI is lagging in the few supporting systems because it is a younger invention compare to the Tableau.

Programming Tools Support
Both Tableau and Power BI connect very smoothly with programming languages. Tableau software is very much connected with the R language, whereas Power BI also combines with R language, but still connected with using revolution analytics.

Integration
Both the Power BI and Tableau integrates with the third party. Tableau has the capability in integrating with R language much better than the Power BI.

Data Visualisation
As we know that data visualisation means the representation of the particular data in the diagrams, charts, table form, graphs, etc. If the user is looking for the best data visualisation tool, then tableau is the option because Power BI concentrates more on reporting and predictive modelling. In the tableau, the information can be stored by using the Tableau server.

Conclusion
Both Power BI and Tableau doesn’t work on the same ideologies and principles. So, it is challenging for us to decide which is best. The user should understand which one to choose according to his requirements. The above parameters are beneficial for the user to decide what to choose.

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