Royce  Reinger

Royce Reinger


Kaminari: A Scope & Engine Based, Clean, Powerful, Customizable


A Scope & Engine based, clean, powerful, customizable and sophisticated paginator for modern web app frameworks and ORMs



Does not globally pollute Array, Hash, Object or AR::Base.

Easy to Use

Just bundle the gem, then your models are ready to be paginated. No configuration required. Don't have to define anything in your models or helpers.

Simple Scope-based API

Everything is method chainable with less "Hasheritis". You know, that's the modern Rails way. No special collection class or anything for the paginated values, instead using a general AR::Relation instance. So, of course you can chain any other conditions before or after the paginator scope.

Customizable Engine-based I18n-aware Helpers

As the whole pagination helper is basically just a collection of links and non-links, Kaminari renders each of them through its own partial template inside the Engine. So, you can easily modify their behaviour, style or whatever by overriding partial templates.

ORM & Template Engine Agnostic

Kaminari supports multiple ORMs (ActiveRecord, DataMapper, Mongoid, MongoMapper) multiple web frameworks (Rails, Sinatra, Grape), and multiple template engines (ERB, Haml, Slim).


The pagination helper outputs the HTML5 <nav> tag by default. Plus, the helper supports Rails unobtrusive Ajax.

Supported Versions

Ruby 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 3.0, 3.1, 3.2

Rails 4.1, 4.2, 5.0, 5.1, 5.2, 6.0, 6.1, 7.0, 7.1

Sinatra 1.4, 2.0

Haml 3+

Mongoid 3+

MongoMapper 0.9+

DataMapper 1.1.0+


To install kaminari on the default Rails stack, just put this line in your Gemfile:

gem 'kaminari'

Then bundle:

% bundle

If you're building non-Rails of non-ActiveRecord app and want the pagination feature on it, please take a look at Other Framework/Library Support section.

Query Basics

The page Scope

To fetch the 7th page of users (default per_page is 25)

Note: pagination starts at page 1, not at page 0 (page(0) will return the same results as page(1)).

Kaminari does not add an order to queries. To avoid surprises, you should generally include an order in paginated queries. For example:


You can get page numbers or page conditions by using below methods.

User.count                     #=> 1000       #=> 20       #=> 50      #=> 1         #=> 2         #=> 1       #=> true       #=> true   #=> true

The per Scope

To show a lot more users per each page (change the per value)


Note that the per scope is not directly defined on the models but is just a method defined on the page scope. This is absolutely reasonable because you will never actually use per without specifying the page number.

Keep in mind that per internally utilizes limit and so it will override any limit that was set previously. And if you want to get the size for all request records you can use total_count method:

User.count                     #=> 1000
a = User.limit(5); a.count     #=> 5         #=> 20  #=> 1000

The padding Scope

Occasionally you need to pad a number of records that is not a multiple of the page size.


Note that the padding scope also is not directly defined on the models.


If for some reason you need to unscope page and per methods you can call except(:limit, :offset)

users = User.order(:name).page(7).per(50)
unpaged_users = users.except(:limit, :offset) # unpaged_users will not use the kaminari scopes

Configuring Kaminari

General Configuration Options

You can configure the following default values by overriding these values using Kaminari.configure method.

default_per_page      # 25 by default
max_per_page          # nil by default
max_pages             # nil by default
window                # 4 by default
outer_window          # 0 by default
left                  # 0 by default
right                 # 0 by default
page_method_name      # :page by default
param_name            # :page by default
params_on_first_page  # false by default

There's a handy generator that generates the default configuration file into config/initializers directory. Run the following generator command, then edit the generated file.

% rails g kaminari:config

Changing page_method_name

You can change the method name page to bonzo or plant or whatever you like, in order to play nice with existing page method or association or scope or any other plugin that defines page method on your models.

Configuring Default per_page Value for Each Model by paginates_per

You can specify default per_page value per each model using the following declarative DSL.

class User < ActiveRecord::Base
  paginates_per 50

Configuring Max per_page Value for Each Model by max_paginates_per

You can specify max per_page value per each model using the following declarative DSL. If the variable that specified via per scope is more than this variable, max_paginates_per is used instead of it. Default value is nil, which means you are not imposing any max per_page value.

class User < ActiveRecord::Base
  max_paginates_per 100

Configuring max_pages Value for Each Model by max_pages

You can specify max_pages value per each model using the following declarative DSL. This value restricts the total number of pages that can be returned. Useful for setting limits on large collections.

class User < ActiveRecord::Base
  max_pages 100

Configuring params_on_first_page when using ransack_memory

If you are using the ransack_memory gem and experience problems navigating back to the previous or first page, set the params_on_first_page setting to true.


The Page Parameter Is in params[:page]

Typically, your controller code will look like this:

@users = User.order(:name).page params[:page]


The Same Old Helper Method

Just call the paginate helper:

<%= paginate @users %>

This will render several ?page=N pagination links surrounded by an HTML5 <nav> tag.


The paginate Helper Method

<%= paginate @users %>

This would output several pagination links such as « First ‹ Prev ... 2 3 4 5 6 7 8 9 10 ... Next › Last »

Specifying the "inner window" Size (4 by default)

<%= paginate @users, window: 2 %>

This would output something like ... 5 6 7 8 9 ... when 7 is the current page.

Specifying the "outer window" Size (0 by default)

<%= paginate @users, outer_window: 3 %>

This would output something like 1 2 3 ...(snip)... 18 19 20 while having 20 pages in total.

Outer Window Can Be Separately Specified by left, right (0 by default)

<%= paginate @users, left: 1, right: 3 %>

This would output something like 1 ...(snip)... 18 19 20 while having 20 pages in total.

Changing the Parameter Name (:param_name) for the Links

<%= paginate @users, param_name: :pagina %>

This would modify the query parameter name on each links.

Extra Parameters (:params) for the Links

<%= paginate @users, params: {controller: 'foo', action: 'bar', format: :turbo_stream} %>

This would modify each link's url_option. :controller and :action might be the keys in common.

Ajax Links (crazy simple, but works perfectly!)

<%= paginate @users, remote: true %>

This would add data-remote="true" to all the links inside.

Specifying an Alternative Views Directory (default is kaminari/)

<%= paginate @users, views_prefix: 'templates' %>

This would search for partials in app/views/templates/kaminari. This option makes it easier to do things like A/B testing pagination templates/themes, using new/old templates at the same time as well as better integration with other gems such as cells.

The link_to_next_page and link_to_previous_page (aliased to link_to_prev_page) Helper Methods

<%= link_to_next_page @items, 'Next Page' %>

This simply renders a link to the next page. This would be helpful for creating a Twitter-like pagination feature.

The helper methods support a params option to further specify the link. If format needs to be set, include it in the params hash.

<%= link_to_next_page @items, 'Next Page', params: {controller: 'foo', action: 'bar', format: :turbo_stream} %>

The page_entries_info Helper Method

<%= page_entries_info @posts %>

This renders a helpful message with numbers of displayed vs. total entries.

By default, the message will use the humanized class name of objects in collection: for instance, "project types" for ProjectType models. The namespace will be cut out and only the last name will be used. Override this with the :entry_name parameter:

<%= page_entries_info @posts, entry_name: 'item' %>
#=> Displaying items 6 - 10 of 26 in total

The rel_next_prev_link_tags Helper Method

<%= rel_next_prev_link_tags @users %>

This renders the rel next and prev link tags for the head.

The path_to_next_page Helper Method

<%= path_to_next_page @users %>

This returns the server relative path to the next page.

The path_to_prev_page Helper Method

<%= path_to_prev_page @users %>

This returns the server relative path to the previous page.

I18n and Labels

The default labels for 'first', 'last', 'previous', '...' and 'next' are stored in the I18n yaml inside the engine, and rendered through I18n API. You can switch the label value per I18n.locale for your internationalized application. Keys and the default values are the following. You can override them by adding to a YAML file in your Rails.root/config/locales directory.

      first: "&laquo; First"
      last: "Last &raquo;"
      previous: "&lsaquo; Prev"
      next: "Next &rsaquo;"
      truncate: "&hellip;"
          zero: "No %{entry_name} found"
          one: "Displaying <b>1</b> %{entry_name}"
          other: "Displaying <b>all %{count}</b> %{entry_name}"
        display_entries: "Displaying %{entry_name} <b>%{first}&nbsp;-&nbsp;%{last}</b> of <b>%{total}</b> in total"

If you use non-English localization see i18n rules for changing one_page:display_entries block.

Customizing the Pagination Helper

Kaminari includes a handy template generator.

To Edit Your Paginator

Run the generator first,

% rails g kaminari:views default

then edit the partials in your app's app/views/kaminari/ directory.

For Haml/Slim Users

You can use the html2haml gem or the html2slim gem to convert erb templates. The kaminari gem will automatically pick up haml/slim templates if you place them in app/views/kaminari/.

Multiple Templates

In case you need different templates for your paginator (for example public and admin), you can pass --views-prefix directory like this:

% rails g kaminari:views default --views-prefix admin

that will generate partials in app/views/admin/kaminari/ directory.


The generator has the ability to fetch several sample template themes from the external repository ( in addition to the bundled "default" one, which will help you creating a nice looking paginator.

% rails g kaminari:views THEME

To see the full list of available themes, take a look at the themes repository, or just hit the generator without specifying THEME argument.

% rails g kaminari:views

Multiple Themes

To utilize multiple themes from within a single application, create a directory within the app/views/kaminari/ and move your custom template files into that directory.

% rails g kaminari:views default (skip if you have existing kaminari views)
% cd app/views/kaminari
% mkdir my_custom_theme
% cp _*.html.* my_custom_theme/

Next, reference that directory when calling the paginate method:

<%= paginate @users, theme: 'my_custom_theme' %>

Customize away!

Note: if the theme isn't present or none is specified, kaminari will default back to the views included within the gem.

Paginating Without Issuing SELECT COUNT Query

Generally the paginator needs to know the total number of records to display the links, but sometimes we don't need the total number of records and just need the "previous page" and "next page" links. For such use case, Kaminari provides without_count mode that creates a paginatable collection without counting the number of all records. This may be helpful when you're dealing with a very large dataset because counting on a big table tends to become slow on RDBMS.

Just add .without_count to your paginated object:

In your view file, you can only use simple helpers like the following instead of the full-featured paginate helper:

<%= link_to_prev_page @users, 'Previous Page' %>
<%= link_to_next_page @users, 'Next Page' %>

Paginating a Generic Array object

Kaminari provides an Array wrapper class that adapts a generic Array object to the paginate view helper. However, the paginate helper doesn't automatically handle your Array object (this is intentional and by design). Kaminari::paginate_array method converts your Array object into a paginatable Array that accepts page method.

@paginatable_array = Kaminari.paginate_array(my_array_object).page(params[:page]).per(10)

You can specify the total_count value through options Hash. This would be helpful when handling an Array-ish object that has a different count value from actual count such as RSolr search result or when you need to generate a custom pagination. For example:

@paginatable_array = Kaminari.paginate_array([], total_count: 145).page(params[:page]).per(10)

or, in the case of using an external API to source the page of data:

page_size = 10
one_page = get_page_of_data params[:page], page_size
@paginatable_array = Kaminari.paginate_array(, total_count: one_page.total_count).page(params[:page]).per(page_size)

Creating Friendly URLs and Caching

Because of the page parameter and Rails routing, you can easily generate SEO and user-friendly URLs. For any resource you'd like to paginate, just add the following to your routes.rb:

resources :my_resources do
  get 'page/:page', action: :index, on: :collection

If you are using Rails 4 or later, you can simplify route definitions by using concern:

concern :paginatable do
  get '(page/:page)', action: :index, on: :collection, as: ''

resources :my_resources, concerns: :paginatable

This will create URLs like /my_resources/page/33 instead of /my_resources?page=33. This is now a friendly URL, but it also has other added benefits...

Because the page parameter is now a URL segment, we can leverage on Rails page caching!

NOTE: In this example, I've pointed the route to my :index action. You may have defined a custom pagination action in your controller - you should point action: :your_custom_action instead.

Other Framework/Library Support

The kaminari gem

Technically, the kaminari gem consists of 3 individual components:

kaminari-core: the core pagination logic
kaminari-activerecord: Active Record adapter
kaminari-actionview: Action View adapter

So, bundling gem 'kaminari' is equivalent to the following 2 lines (kaminari-core is referenced from the adapters):

gem 'kaminari-activerecord'
gem 'kaminari-actionview'

For Other ORM Users

If you want to use other supported ORMs instead of ActiveRecord, for example Mongoid, bundle its adapter instead of kaminari-activerecord.

gem 'kaminari-mongoid'
gem 'kaminari-actionview'

Kaminari currently provides adapters for the following ORMs:

For Other Web Framework Users

If you want to use other web frameworks instead of Rails + Action View, for example Sinatra, bundle its adapter instead of kaminari-actionview.

gem 'kaminari-activerecord'
gem 'kaminari-sinatra'

Kaminari currently provides adapters for the following web frameworks:

For More Information

Check out Kaminari recipes on the GitHub Wiki for more advanced tips and techniques.

Questions, Feedback

Feel free to message me on Github (amatsuda) or Twitter (@a_matsuda) ☇☇☇ :)

Contributing to Kaminari

Fork, fix, then send a pull request.

To run the test suite locally against all supported frameworks:

% bundle install
% rake test:all

To target the test suite against one framework:

% rake test:active_record_50

You can find a list of supported test tasks by running rake -T. You may also find it useful to run a specific test for a specific framework. To do so, you'll have to first make sure you have bundled everything for that configuration, then you can run the specific test:

% BUNDLE_GEMFILE='gemfiles/active_record_50.gemfile' bundle install
% BUNDLE_GEMFILE='gemfiles/active_record_50.gemfile' TEST=kaminari-core/test/requests/navigation_test.rb bundle exec rake test

Author: Kaminari
Source Code: 
License: MIT license

#ruby #rails #pagination 

What is GEEK

Buddha Community

Kaminari: A Scope & Engine Based, Clean, Powerful, Customizable

Jackson George


ECS: Residential & Commercial Cleaning Services in London

Specializing in commercial cleaning, office cleaning, hospital & GP surgery cleaning, residential cleaning, washroom cleaning, school cleaning, Covid cleaning and sanitization, ECS Commercial Cleaning Company London has built up a large, experienced team of highly-skilled team of professionals who ensures work is delivered to highest standards, on time and on budget.

At ECS Commercial Cleaning, we provide a safe, cost-effective and efficient service that covers all your commercial cleaning needs. From residential cleaning, washroom cleaning, school cleaning to office cleaning, hospital & GP surgery cleaning, we cater it all. We have years of experience with all kinds of projects and know the best approach to save you time and money. Our professional knowledge and skills has enabled us to provide high quality cleaning solutions throughout London.

We’ve been delivering commercial cleaning services throughout London with the help of trained and experienced professionals, using only the finest equipment and cleaning solutions. Our team starts cleaning project from initial consultation through to completion on budget and schedule.

ECS Commercial Cleaning strives to keep people first, investing in their professional training and safety. We work hard to create and sustain an environment that helps us to achieve clients’ expectations through consistently excellent service and minimal downtime.

Our Services

With 10 years of market experience, a resource of professional employees and coverage throughout the London, ECS Commercial Cleaning has established itself as one of the leading commercial cleaning company, offering valuable cleaning solutions including:

  • commercial cleaning
  • office cleaning
  • hospital & GP surgery cleaning
  • residential cleaning
  • washroom cleaning
  • school cleaning
  • covid cleaning and sanitization

Our clients are the London’s leading retail outlets, office buildings and office premises, schools, hospitals, production and industrial premises and others. Our cleaning solutions offers peace of mind to clients and most importantly ensure that workers are able to do their jobs comfortably and efficiently without compromising safety. Over the years of industry experience, we remain at the forefront of our industry due to our unparalleled customer dedication and unrivalled experience in providing safe, and cost-effective cleaning solutions.

Our Expert Team

ECS Commercial Cleaning provides you with an expert team that completes your cleaning project professionally and efficiently. No matter what cleaning service you require, our aim is to work closely with our clients in order to comprehend their needs and fulfil their specific requirements through tailored cleaning solutions.

With our eco-friendly cleaning products and a team of experienced professionals, we can provide timely cleaning solutions to our clients. Contact ECS Commercial Cleaning on 0161 5462235.

#cleaning #commercial cleaning #office cleaning #residential cleaning #washroom cleaning #covid cleaning and sanitization

sophia tondon

sophia tondon


Microsoft Power BI Consulting | Power BI Solutions in India

Hire top dedicated Mirosoft power BI consultants from ValueCoders who aim at leveraging their potential to address organizational challenges for large-scale data storage and seamless processing.

We have a team of dedicated power BI consultants who help start-ups, SMEs, and enterprises to analyse business data and get useful insights.

What are you waiting for? Contact us now!

No Freelancers, 100% Own Staff
Experienced Consultants
Continuous Monitoring
Lean Processes, Agile Mindset
Non-Disclosure Agreement
Up To 2X Less Time

##power bi service #power bi consultant #power bi consultants #power bi consulting #power bi developer #power bi development

sophia tondon

sophia tondon


Hire Power BI Developer | Microsoft Power BI consultants in India

Hire our expert Power BI consultants to make the most out of your business data. Our power bi developers have deep knowledge in Microsoft Power BI data modeling, structuring, and analysis. 16+ Yrs exp | 2500+ Clients| 450+ Team

Visit Website -

#power bi service #power bi consultant #power bi consultants #power bi consulting #power bi developer #power bi consulting services

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.

This is image title

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.

To get in-depth knowledge of this technology and to develop skills to make a great career in this regard one can opt for Power BI online training Hyderabad.

#power bi training #power bi course #learn power bi #microsoft power bi training #power bi online training #power bi online course

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.

This is image title

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.

This is image title

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.

This is image title

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.

#power bi training #power bi course #learn power bi #power bi online training #microsoft power bi training #power bi online course