Tech stacks explained

How to choose the needed tech stack for web application development

Let’s outline the essential aspects every developer needs to know about a technology stack and how its selection can affect the entire process of software development. Further, we’ll stress the top things you should pay attention to when choosing the tech stack for your project.

We do hope that this article will help you get a full picture of the tech stack’s purpose and understand how to make a smart choice of the one that fits your project at best.

Let’s start!

What is a tech stack in software development?

Even if you are already well-familiar with the concept, let’s recap its definition.

A tech stack (also known as a solutions stack) is a bundle of tools, namely programming languages, libraries, frameworks, and databases that come together for the development of a particular web, mobile, or hybrid application.

Such a name is given to this concept not without a reason – each subsequent layer of the stack builds on top of the features of one underneath it. All of them are brought together around one common goal – to assist in building, testing and running your application.

Though this concept can be found in many areas, the term itself emerged in the software development community. It meant to simplify communication between developers and help to indicate the application’s strong and weak points.

Structure of a tech stack

Every tech stack is different, yet, they all share a common structure. It’s comprised of two major components – front end and back end. The front end is responsible for the client-side part of your app while the back end is in charge of its server-side functionality.

What is a back end stack?

A back end is the backbone of your application. It refers to everything that is not visible to users but ensures the proper functioning of the app. Behind the scenes, it takes care of things like security, content management and the structure of your app.

Structurally, the back end consists of the following components:

Programming languages (JavaScript, Node.js, Ruby, C#, Perl, PHP, Python, Java, Go, Scala, Elixir, etc)
Server-side web frameworks (e.g., .NET, Django, Flask, Mojolicious, Laravel, CakePHP, Ruby on Rails, Phoenix, Express JS, AdonisJS, Spring Boot, etc)
Databases (MySQL, Oracle, MongoDB, GraphQL, etc)
Web servers (Nginx, Express, Apache, Apache Tomcat, IIS, Google Web Server, etc)

Let’s break down the purposes of all the back end’s building blocks. A back-end developer has to work with programming languages that databases understand to ensure the communication between web services, database systems and the application itself. A framework simplifies and speeds up development. A database is used for storing your application’s data. In addition to a database, a caching system is required to be set up to reduce the load on your database (e.g., Memcached, Redis, etc). A web server handles requests that come from the client-side, i.e., your app’s users.

What is a front end stack?

And now let’s look at the front-end more closely.

A front end is responsible for a visual part of an application. It defines what your users see and how they can interact with your app. The core purpose of front-end web development is to create a seamless user experience and maximize its quality by making the interface accessible to any user.

As a rule, a front end is a part of the project where the development starts since it’s the most important thing to show to your customers in the first stages.

What are the cornerstones of the client-side?

HTML
CSS
JavaScript

This is a classical trio of tools for implementing designs on the web. HTML defines the structure and layout of the application’s content. CSS tells the browser how this content has to be styled with colors, fonts, etc. JavaScript is a scripting language that is responsible for making web pages interactive (i.e. defining the rules of how, when and where the content should be modified, hidden or removed). Often, front-end development becomes heavily JavaScript-oriented, especially if a web application is highly interactive.

A modern front-end developer needs to be familiar with the principles of responsive design, test-driven development, front-end frameworks and libraries (React, AngularJS, Angular, Vue, Ember, Bootstrap, Foundation), and basics of server-side rendering. Also, knowledge of the principles of working with SASS and LESS adds a competitive advantage since these pre-processors for CSS provide more flexibility and modularity when it comes to your app’s styling. Front-end developers should know how to use graphic design tools that allow building the application’s prototypes, experiment with the user interface and deliver the final variant. The most popular software is Balsamiq Mockups, Illustrator, Figma, Photoshop, Sketch. Try at least several of them to find your perfect match.

Why tech stack matters

The technology stack is a foundation of your application. The underlying technologies make a significant impact on how your app is functioning and how it will perform in the future. It also can affect the scalability of your app. That’s why developers should take the task of selection responsibly and with exceptional care.

Once you’ve chosen the tech stack, try to understand its strengths and weaknesses. This will help you to know how to benefit from the former and alleviate the latter.

How to choose your tech stack

If you struggle to make the right choice on the tech stack, there are some general guidelines for you to follow.

Take into account the size of your project – it can be small, mid-range or large. Small projects are often built on top of Node.js – React and Python-Django stacks. If you need only a simple landing page, consider using WordPress. Mid-size projects (e.g., online stores, etc) need more complex stacks such as MEAN, LAMP, or MERN that prove to perform well. Large projects require more scalability, speed, and maintainability. So, you need to find the stack that leverages well both performance and customer satisfaction.
Scalability. Depending on the technologies you choose, your app may have different scalability capabilities. This factor should be taken into consideration as early as possible. If you are building a service that will be accessed by thousands and millions of users, think about programming languages that support high-read operations and databases that are scalable. In this case, you can try MEAN stack.
Consider your app’s type. Will it be a mobile or desktop app? If a mobile, then for which platform – Android or iOS? If a desktop, which browser will it use? It’s always better to launch the MVP of your app only on a single platform – web or mobile. After you made enough process in developing and distributing your product, you can proceed to build the second app’s interface.
Stick to the mobile-first design paradigm and build a responsive web application.
Make sure you have enough engineers with the required skills.
Try to predict whether your tech stack will scale. However, the lion’s share of performance issues is related to application design rather than a choice of technologies. So, be ready to tune and optimize your app’s design.
Evaluate the cost of development. After studying carefully the tech stacks that correspond to the needs of your app. Before choosing the paid tools for development, compare the cost of a resulting stack and usability of the features it provides. Rather than investing in expensive tools or building custom ones from the ground up, try researching their free alternatives. You can reap a lot of benefits from the open-source products.

Vue.js is a progressive JavaScript framework that enables web developers to create UIs in an elegant and efficient manner. Lately, it started to attract more and more attention from wider circles of the web development community.

And not without a reason – among the most prominent features of Vue are a smooth learning curve, concise programming style and high performance due to the automatic tracking of the component’s dependencies during its rendering. Besides, its core concepts are well-documented.

As the Vue.js community grows in size, so does its ecosystem of development tools. Let’s explore its best representatives in more detail.

Vue CLI

Vue CLI is a standard utility for rapid Vue.JS development. It provides your Vue app with access to the build tools and ensures they work smoothly together, allowing you to fully concentrate on bringing your ideas with code rather than on configuring the app.

The flexible plugin system of Vue CLI supports Babel, TypeScript, ESLint, PostCSS, PWA, Jest, Mocha, Cypress, and Nightwatch. Plus, thanks to this system, developers can share their plugins with the community.

To my mind, the greatest thing is that you can make use of various configuration and customization options to meet your project’s specific needs.

Another thing you’ll love is a mighty GUI of Vue CLI that makes creating, configuring and managing projects a lot easier. Besides, Vue CLI has out-of-the-box support of unit & end-to-end testing.

Storybook

Storybook is an open-source environment for developing, managing and testing UI components. Seamless configuring components through a flexible API is what makes it a must-have tool for building UIs efficiently.

With Storybook, you can create awesome components and demonstrate them in an isolated environment. Since it runs outside your app, you don’t need to worry about your app’s dependencies and specific requirements.

If you want to implement extra features for Storybook, you can try addons functionality.

Vuetify

Vuetify is a component framework that offers a broad range of stunning UI components. All the components adhere to the principles of Material Design. You can use this framework for building mobile, server-side rendered, single page, and progressive web apps as well as for regular HTML pages.

To get familiar with everything Vuetify has to offer, browse the components, pick those you like the most and get hands-on experience with them in your app. Try one of the pre-made layouts, design your own theme or style your components with colors, spacings, typography and more.

Another aspect I’d like to highlight is its rich ecosystem which includes a theme generator, vuetify-loader, Cognito module, and other tools. Moreover, the components are well-documented and accompanied with nice examples.

To my mind, this framework can meet the needs of any project. Plus, you can select only specific components to be used within your app. This approach allows you to control the size of your app by making it more lightweight.

VuePress

VuePress is a static site generator tool. It offers blogging features and a plugin system. You can build custom themes and use ready-to-use community themes.

Let me break down the essence of its work. After you write the site’s content using Markdown syntax, VuePress transforms it into static HTML files. As soon as these files are loaded, your site runs as a single page application (SPA) backed by Vue, Vue Router and Webpack.

The main advantage of this approach is that you can include Vue code or components within Markdown files. That’s why the development comes out as flexible and you can create a website like a regular Vue app.

VueX

VueX is a state management system for Vue apps. You can use it as a centralized store for the application’s components and define rules that ensure that states are mutated merely in a predictable manner.

Let’s take a closer look at the structural components of VueX:

state – an object that contains the app’s state
getters – objects that provide methods used to access the state. They act like computed properties for stores.
mutations – objects that provide methods for affecting the state. Mutations may remind you of events since a mutation has a string type and a handler – a function in which we modify an actual state.
actions – objects that provide methods for triggering mutations and running async operations.

Also, it’s worth to mention modules as an inevitable part of VueX. Modules may come in handy when your application starts to scale. It’s a good practice to organize your code in modules to separate the store into individual pieces and hence simplify its maintenance. Each module has its own state, mutations, actions, and getters.

Other cool built-in features you may like are hot-reloading mutations, modules, actions, and getters during development.

To my mind, VueX perfectly copes with shared state management. But if your application is as simple as single page application, then you most likely don’t need Vuex. You should use it if you are building a medium- or large-scale single page application and facing the problem of handling the state outside of your Vue components.

Nuxt

Nuxt is another progressive Vue-based framework for building modern web applications. Under the hood of Nuxt, there are Vue official libraries (vue, vue-router, and vuex), and web dev tools (as webpack, PostCSS, and Babel) being used.

With Nuxt, you can build all kinds of Vue applications – from static landing pages to sophisticated enterprise-ready ones. It has a modular ecosystem that enables you to easily connect REST and GraphQL endpoints.

Other built-in features include server-side rendering, automatic code-splitting, ES2015+ transpilation, powerful routing system with async data and a lot more.

To my mind, the best thing about Nuxt is that it lets you enhance the development experience by providing the functionality for making the app structured and optimized.

To get a better idea of how Nuxt.js can be used, dive into examples and explore its API.

If you want to contribute to its development, you are welcome to do it on its GitHub.

Gridsome

Gridsome is a modern framework for building fast and secure websites that can be deployed to any static web host or CDN (Netflify, Amazon S3, etc). Though it’s relatively new (released in 2018), there are already a lot of adopters and followers among Vue developers. Its creators were inspired by Gatsby.js (a site generator for React). This inspiration resulted in building a similar framework but for Vue.js.

Let me explain how it works. Gridsome generates SEO-friendly HTML files based on the data source you provide within your app. The data source can be any – Markdown, CSV, Yaml, JSON, WordPress, Drupal (any other CMS), MongoDB, Airtable, etc. After loading the content, it’s turned into a single GraphQL Data Layer that serves for centralized data management. Now you can use extract this data and use it for building and deploying your Vue application.

The use of a universal GraphQL layer for all the data sources connected to the app is what makes Gridsome so unique. Due to it, data management is greatly simplified since the GraphQL layer acts as centralized storage and can be used by any page or component.

Another aspect I’d like to mention is a plugin ecosystem of Gridsome. You can take advantage of diverse plugins developed by the community of developers or even create your custom one.

Other out-of-the-box features are automatic code-splitting, static file generation, dynamic and file-based routing, hot-reloading, etc. I recommend taking the time exploring all other features of the framework.

Besides, not only it’s free but also open-source. Any contribution to the project is more than welcome since it’s a young project created and maintained by two Vue enthusiasts.

Learn more

If you are searching for ways to improve your VueJS coding skills and deepen knowledge in its specific concepts, we recommend you to explore GitConnected – there you can find plenty of tutorials on how to build apps with VueJS.

The best way to succeed in the field of data science or data analytics is to complete structured courses designed by industry experts.

And here is why.

The primary value of such courses is that you learn the subject in a logically organized way.

But courses shouldn’t stand alone. When coupled with real-life projects, they help you to get a deeper knowledge of how everything works.

Another advantage of such courses is that you can learn everything at your own pace and from anywhere in the world. Modern courses allow you to be truly flexible. There is no room for “no-time” excuses – you are free to make a learning schedule that suits you the best. No less important is interaction with the community of peers who are in the same boat as you are. By getting into thematic discussions on forums, you can move forward noticeably faster.

To simplify your way to developing an intuition behind algorithmic techniques, statistical and mathematical concepts, and master programming skills, we’ve picked for you the best-in-class free data analytics and data science online courses that deliver the most current knowledge and cover cutting edge models and techniques. During the completion, you’ll be given a chance to gain hands-on experience with coding assignments and solve real-world business problems.

Let’s dive in!

Data Science Courses

Data Science fundamentals by IBM
Probability and Statistics in Data Science using Python by UC San Diego
Essential Math for Machine Learning: Python Edi t ion by Microsoft
Statistical Thinking for Data Science and Analytics by the Columbia University
Data Science Math Skills by the Duke University

Data Science and Analysis Learning paths

Another way to start data science and analytics journeys is with tracks on DataQuest.io. There are a lot of awesome career paths available:

DataCamp also has to offer comprehensive career tracks in data science and analytics. Check them out:

During all these missions, you’ll face exciting challenges as well as get the intuition behind the basic concepts, apply statistical and ML models to real-world scenarios, and master programming in Python or R.

Data Science Specializations

Since many courses simply bombard you with the theory, we’ve selected only those that contain practical coding exercises and help to build a solid foundation of maths and statistics behind data science and analytics. All of them are created by professional data scientists who are eager to share their knowledge and simply teach you complex concepts.

We do hope you’ll find the best fit for you, get enough soft and hard skills and create a stunning portfolio to kickstart your career.

We hope you’ve had enough time to get acquainted with WebDataRocks Pivot Table and make sure its functionality is the best fit for your analytical app.

Here at WebDataRocks, we strive to provide you with the quintessential features for data visualization and analytical reporting:

Connecting to local or remote CSV and JSON data sources
17 aggregation functions for data summarization
Filtering by members, values
Report filters
Sorting
Customization options
Exporting and saving results in different formats
Extensive JavaScript API
Conditional & number formatting

All these features work together to help you compose interactive reports and understand your data better.

What if you need more data to load?

You may have noticed that WebDataRocks perfectly works with data sets that do not exceed 1 MB. This quality satisfies most of the pivot’s users.

But what if your business starts rapidly scaling?

That particular moment can be hard to predict but it’s important to be prepared for any sudden changes in advance. Especially if your data also starts growing in size and the need to find scalable solutions for data analysis arises.

That’s exactly the case for which we’ve decided to prepare you. To handle either rapid or expected growth of your business successfully, we suggest migrating from WebDataRocks to Flexmonster – a more sophisticated and flexible reporting tool.

If you are ready to discover more exciting features of Flexmonster Pivot which your project will certainly reap the benefits of, move ahead!

1. Beyond CSV and JSON: working with other data sources

Surely, CSV and JSON are the most common data sources when it comes to storing the data. And WebDataRocks perfectly copes with its processing.

But if you prefer to load the data directly from the database, you can try connecting to relational or NoSQL databases from Flexmonster Pivot.

Or if your data comes from an OLAP cube, you can connect Microsoft Analysis Services and start pivoting your data in minutes.

Lastly, if you prefer Elasticsearch to any other data storages, that’s not a problem at all likewise. You can easily connect to the index from the pivot table just as fast.

As you can see, Flexmonster provides a lot more data source options to which you can connect.

What else deserves special attention is the special algorithm under the hood of Flexmonster which ensures the data from the cube or index is loaded piece-by-piece, meaning the component loads only the data that is specified by the current slice.

This approach guarantees that all the data calculations are performed on the server-side and less data is loaded into the browser thereby improving the overall performance of your app.

2. More flexibility of integration

The developers can experience more flexibility in terms of integration with the most popular tech stacks. Among the solutions stacks in which Flexmonster proved to work smoothly are LAMP and MEAN.

As for front-end frameworks, libraries, module loaders, bundlers, and languages, you can integrate the reporting tool with AngularJS, Angular, React, webpack, RequireJS, TypeScript, ASP.NET, PhoneGap, and a lot more.

Check out the full list of client-side technologies for which Flexmonster provides ready-to-use wrappers and integration instructions.

3. Extended filters

You may especially like more sophisticated filtering options of Flexmonster Pivot.

As in WebDataRocks, you can easily manage how and when filters are applied to your data but using more versatile queries.

Take advantage of filters by date – we are sure that your end-users may find these extremely powerful for their reports.

4. Total alignment with Material Design

What is distinctive about Flexmonster is its UI design inspired by the principles of Material Design. It makes the component totally suitable for any app, no matter where it’s published – on Android, iOS or web.

5. More customization options

As well as in WebDataRocks, you can customize the Toolbar, apply predefined themes, localize UI labels, customize cells, and configure exporting options of Flexmonster.

However, you can feel even more freedom of personalization and change the component in line with the needs of your project. For example, you can completely change the context menu to add custom scenarios for the right-click action, enable search in the Field List, and a lot more. There are also more date and time formatting options available.

If you want to dive into other visual customization options, refer to the Customizing appearance section.

6. Pivot Charts

This is the feature exclusive to Flexmonster. In addition to integrations with charting libraries, you can benefit from Pivot Charts that are always at hand.

To understand patterns hiding in your data visually, simply send summarized numbers to Pivot Charts in a few clicks:

You can even try the split view to look at both charts and the grid at the same time.

7. Extended API

The JavaScript API of Flexmonster offers many more API calls. For example, you can partially update data in the runtime, define custom sorting for hierarchy members, and more.

8. Advanced customer support

If you choose Flexmonster, you’ll also get new minor features and updates every two weeks. Besides, the Flexmonster Support team is always ready to help you to manage a case of any difficulty on the Forum.

Discover Flexmonster Pivot Table & Charts

Feeling you need more data to analyze? Then you should give Flexmonster a try. Learn more in our documentation.

You have 30 days to test its functionality without any commitment and decide whether to move to a new solution or not. Oh, besides, it’s really easy. We’ve prepared a special guide for you to make the transitioning completely seamless.

You may also be interested in:

Today we’re going to talk about analytics. To be more precise – HR analytics.

We’ll clarify why it is so important for small-, medium- and large-sized businesses and how you can implement a custom solution for HR data analysis simply using charts and a pivot table without investing a lot of developer resources?

You might be wondering: why does your company need to focus on it at all? – Fortunately, we have all the answers for you here.

First of all, it helps to evaluate the performance of the company’s departments and answer crucial questions regarding the work of employees.

Though often being underestimated, HR analytics also gives the power to transform how the HR department performs by testing the effectiveness of the implemented strategies.

In other words, provided that you use analytics to the fullest extent, you can improve employee performance, get a bird’s eye view of your business and make data-driven decisions relying on extracted insights rather than simple intuition.

Before the start, we suppose you’ve already managed to outline your business goals. The goals may vary from company to company but the foremost ones may include:

recognizing trends in the current workforce.
increasing employee retention rates, engagement, and performance.
predicting voluntary and involuntary turnover.

Then, you need to identify metrics to track.

We took care of it and have prepared a kind of memo for you.

It’s fundamental metrics we’re going to analyze in this tutorial and corresponding types of visualization. (!But bear in mind that you can always calculate your own metrics with the help of tools we’ll mention below).

Employee productivity (efficiency). This is the ratio of the employee output’s amount to time spent on a particular task or project.
Turnover rate. Measures the number of employees leaving a company over time. With the multi-line chart, we can track a turnover rate in each department. This metric is often the center of the HR manager’s attention. The goal is to minimize this number.
Revenue per employee. Measures the revenue per employee.
Retention rate. This is the opposite of a turnover rate. Measures the percentage of employees remained at the end of a period. We can also measure it by the department.
Employee engagement. Measures the level of the employee’s commitment. It is mostly affected by satisfaction and stress levels. We can use the line chart to track engagement for each person over months. Or within each department.
Training costs per employee. Column chart.
Overtime hours by departments and age groups. Best to analyze with a column chart.
The number of part-time employees in the company.
Hires to the goal. Total hires required to achieve the specified recruitment goal within a fixed time.
Absenteeism (absence rate). Measures the percentage of total working time when employees are absent from work. You want it to be as low as possible.
Ranges of salaries by departments.

And finally, demographics-related metrics:

Employees’ countries of origin. We can display the top countries in a pie chart.
Gender ratio. The ratio between the number of females and males in the company. Pie chart works here best since there are only two categories to depict.
Age groups. Column chart.

Now, check all your data that you’ve already collected from various sources and start building your HR analytics solution.

We suggest the most popular way to communicate all the data through an interactive dashboard. You can easily implement this approach by combining WebDataRocks Pivot with Highcharts.

Both libraries work together like a charm and soon you’ll make sure of it. Let’s go-ahead to the dashboard’s creating!

Step 1: Add Highcharts dependencies

We’ll be adding all scripts into the <head> section of the web page to ensure they are run before the rest of the page is loaded.

To access Highcharts, include these scripts:

<script src="https://code.highcharts.com/highcharts.js"></script>
<script src="https://code.highcharts.com/highcharts-more.js"></script>

Let’s also add the export module to allow end-users to download charts as PDF, JPG, PNG, or SVG images:

<script src="http://code.highcharts.com/modules/exporting.js"></script>

If you want to enable exporting without an internet connection, you can add this script instead:

<script src="http://code.highcharts.com/modules/offline-exporting.js"></script>

Add containers where the charts will be rendered:

<div id="piechart-container"></div>
...
<div id="barchart-container"></div>

Create as many containers as there will be charts on the dashboard.

Step 2: Add WebDataRocks Pivot

It’s time to configure a reporting tool in your project.

Include WebDataRocks scripts and styles:

<link href="https://cdn.webdatarocks.com/latest/webdatarocks.min.css" rel="stylesheet">
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.toolbar.min.js"></script>
<script src="https://cdn.webdatarocks.com/latest/webdatarocks.js"></script>

To apply the dark theme, add the corresponding styles:

<link rel="stylesheet" type="text/css" href="https://cdn.webdatarocks.com/latest/theme/dark/webdatarocks.min.css" />

Create the container for the pivot table:

<div id="pivot-container"></div>

Step 3: Initialize the pivot table on the page

var pivot = new WebDataRocks({
    container: "#pivot-container",
    toolbar: true,
    report: {
        dataSource: {
            data: getData()
        }
    }
});

To load data into the component, define and set a function that returns the JSON data:

function getData() {
    return [
        {
            "id": 1,
            "Full Name": "Sallyanne Tryhorn",
            "Job Title": "Software Consultant",
            "Country": "Sweden",
            "Gender": "F",
            "Employee Engagement": 0.8,
            "Part-time": "No",
            "Revenue per employee": 889,
            "Hours Spent for Project": 66,
            "University": "Kalmar University College",
            "Currency": "SEK",
            "Department": "Business Development",
            "LinkedIn Skill": "Brand Management",
            "Month Salary": 1229,
            "Overtime Hours": 12,
            "Employment start date": "9/26/2015",
            "Age Group": "<21"
        }
    ]
}

Don’t forget to specify data types for JSON!

Step 4: Create a report

Now that your pivot contains the data, it's time to analyze it.

To provide your end-users with a meaningful report, set a slice that describes which hierarchies have to be placed to the rows, columns, and measures.

As well as a report, this object has a JSON structure:

"slice": {
    "rows": [{
        "uniqueName": "Department"
    }],
    "columns": [{
        "uniqueName": "Measures"
    }],
    "measures": [{
        "uniqueName": "Total Overtime Hours",
        "formula": "sum(\"Overtime Hours\") ",
        "caption": "Total Overtime Hours"
    }],
    "sorting": {
        "column": {
            "type": "desc",
            "tuple": [],
            "measure": "Total Overtime Hours"
        }
    }
}

This slice helps to get an understanding of the overtime hours by departments.

Try exploring the data interactively - drag and drop the hierarchies on the grid and save the resulting report.

Step 5: Pass the data to the charts

Before creating the charts and filling them with the data, we need to process the data to the appropriate formats.

Each chart requires a different data structure. To save your time, add the WebDataRocks connector for Highcharts to get access to the ready-to-use methods that handle data processing:

<script src="https://cdn.webdatarocks.com/latest/webdatarocks.highcharts.js"></script>

Next, we need to write code that performs the following scenario for each chart type:

Gets the data from the pivot table component. On this step, you can specify which subset of the data you want to visualize.
Processes this data with the help of the connector's methods.
Creates the chart and sends the data to it.
Each time the report on the grid is updated, the renewed data has to be resent to the chart.

To know whether the pivot table is ready to provide the data, we should use an event handler for reportcomplete.

Here's a simple example of the handler you can attach to the event:

reportcomplete: function() {
    pivot.off("reportcomplete");
    createPieChart();
    createBarChart();
}

To keep the blog post concise, I prefer not to include all the code here - you can find all the details of charts creating in the CodePen demo. You can also see there how to apply a dark theme for Highcharts.

Step 6: Enjoy the resulting dashboard

See how the dashboard looks like as a result:

This dashboard is completely ready to be an efficient instrument for HR analytics. Your end-users can interact both with a pivot table and charts, change reports on the fly and look at the data from multiple angles.

Coupled together, the pivot table and charts complement each other and make your data speak.

Hopefully, the presented approach will help you to get insights from the data, showcase your data story to the colleagues and improve the business performance in the long run.

People often ask: how data analytics and data science differ from each other? For many, it may be hard to differentiate these two disciplines. And today we’ll try shedding the light upon their principal distinctions.

To my mind, it’s not quite right to oppose these disciplines with each other. Instead, it’s more beneficial to consider them as parts of a single whole.

Data Science

Data science is an umbrella term that embraces data mining, data analytics, machine learning, and other disciplines. Its primary goal is to extract insights from massive collections of raw unstructured, semi-structured and structured data sets.

A significant difference between Data Science and Data Analytics lies in their goals and tactics. For example, a data scientist is more concerned with finding the right questions to ask while a data analyst intends to obtain the right answers based on the existing questions to the data.

Also, these disciplines are different in scopes. While data science produces broader insights on the subject and identifies inherent trends in the data, data analytics aims at focusing on more concrete / narrower goals.

Skills to become a proficient data scientist

Hard skills

A skilled data scientist has to know how to collect data sets from diverse sources, preprocess it by cleaning and transforming disordered data, extract required features and implement machine learning techniques to gain insights into the data.

Equally important is to know how to perform predictive analytics, sentiment analysis and integrate analytics with enterprise-scale systems.

Tools and languages

Along with ML techniques, a data scientist has to know Python, R, Scala, or/and SAS/STAT well, have a strong background in maths, probability, and statistics.

Since the data that data scientists work with is most often stored in relational databases, SQL expertise is a critical skill that can’t be underestimated. You need to know how to write basic and complex, nested queries to get the precise data you need.

Understanding how to work with unstructured data is especially valuable – in most cases, you’ll work with messy data. The ability to work with NoSQL databases (MongoDB, etc) is also highly appreciated.

Soft skills

A qualified data scientist should possess not only exceptional technical skills but also outstanding communication, problem-solving, and prioritization skills. Another key skill is a clear understanding of the data from a business point of view.

Given the above, data scientists are proficient data analysts with solid programming skills.

To be a good data scientist, you must be an excellent data analyst. But the opposite is not always true.

Skills to become a proficient data analyst

A data analyst has to be skilled at descriptive statistics, visualizing data, and reporting insights gained from the data.

Like data scientists, data analysts have to be proficient at statistics, working with databases, and looking at the data from multiple angles.

Hard skills

Must-have skills for data analysts are:

Knowledge of R and Python
Experience with Tableau, Power BI
Ability to prepare the data for analysis
Solid maths and statistics foundation
The ability to create visualizations with Python/JavaScript libraries (e.g., pivot tables, charting libraries) is a plus.

Soft skills

A skilled data analyst has to expertly communicate their findings to a wide audience of decision-makers by using various data visualization and reporting methods. Data analysts have to constantly cooperate with other team members, be good at problem solving and research.

No less important is to be attentive to the smallest details and be able to present complex concepts in a simple language.

To summarize, data science and data analytics have a lot in common, yet, they have differences that can be nonobvious at first sight. Today we’ve covered the major aspects that differentiate these two powerful disciplines and outlined must-have skills for data scientists and data analysts.

Have you read?

Best Public Dataset Sources for Data Analysis and Machine Learning

What is React

React is a front-end library created and maintained by Facebook.

The central idea behind React is that everything can be built with reusable components which have own lifecycle.

Among companies that choose React as a front-end technology, you can find Netflix, Dropbox, Coursera, IMDb, Khan Academy, New York Times and a lot of other notable organizations.

If you asked us what are the main features that make React great for the development of user interfaces, we’d outline these:

Quick process of writing components with JSX.
Virtual DOM & the diffing algorithm that ensure effective updates of the DOM tree, leading to higher app’s performance. This aspect is critical for building large-scale applications with real-time data.
Stability of code due to the downward data flow.
SEO–friendliness.
Ability to build mobile apps for iOS and Android platforms with React Native.
Shorter learning curve comparing to Angular. As soon as you are comfortable with the concepts of components and their lifecycle, you can proceed to create user interfaces.

Another advantage of React is its rich ecosystem that’s backed by the strong community of developers across the globe. Thanks to them, all possible tools are available to you: IDEs, extensions for code editors, plugins for web browser, boilerplates, libraries, etc. And these are what we’re going to dedicate this blog post to.

Regardless of your level of expertise, you can greatly benefit from tools that speed up web development. To simplify your searching, we’ve picked the most useful tools that will help you to build a successful project.

Let’s take a closer look at them.

CodeSandBox

CodeSandBox is on the top of our list. And not by chance – it’s one of the most progressive online IDEs we know. The companies that use CodeSandBox are GitLab, Atlassian, Shopify.

With the available toolkit, you can create any type of web project that uses React, Angular, Vue, Vanilla JS, Preact, Reason, Svelte, CxJS, Unibit, Dojo as client templates and Next.js, Nuxt.js, Gatsby, Ember, Apollo, Nest, Node.js as server templates. If you don’t want to be limited to provided templates, you can create your custom ones.

The finished sandboxes can be deployed to Zeit or Netlify.

The IDE smoothly integrates with GitHub: you can import your repository as a sandbox which will stay in sync with the original repo.

You can even create commits and open pull requests from a forked sandbox. It’s also possible to create a new GitHub repository based on the existing sandbox.

Another cool feature is that React DevTools are integrated directly inside CodeSandBox.

The feature we’d like to highlight is the capability to embed the project anywhere, even on Medium. This is what makes it perfect for sharing projects.

Lastly, the IDE is almost entirely open-source – you can contribute to its development.

Rekit Studio

You shouldn’t miss this stand-alone IDE as well. Rekit Studio offers a lot of features which make building scalable web applications with React, Redux, and React Router easier: code generation, dependency diagraming, building, unit tests, navigating code in a meaningful manner. It also facilitates refactoring, which is especially important for large-scale apps.

With the project explorer, you can quickly navigate between project elements.

To understand your code better, you can always review your project visually – Rekit generates diagrams that display dependencies between features, modules, and actions. This functionality increases the visibility of your work and simplifies refactoring.

Read about it in more details on freeCodeCamp.

React Studio

Whether you prefer using a design- or code-first approach for building projects, you are going to like React Studio.

It provides the functionality that makes easier to control all the top-level aspects of your application’s design, assemble the components into an application and keep your code clean. As soon as you’re satisfied with your project, you can publish it right to the web. All these features bolster rapid development.

It should be noted that the IDE is especially suitable for designers. Completely from scratch, they can create a web application via the drag-and-drop gesture and test it even before the real-world data is available.

Other features for designers include built-in drawing tools for Illustrator, Photoshop, and Sketch.

Discover more about new features of React Studio on Hackernoon.

React boilerplate

React boilerplate is a real find for developers who want to enhance their performance. It automates everything – creating of components, routers, containers, selectors right from the CLI.

The built React applications are offline-first, meaning that they remain accessible to the users even without a network connection.

When editing code, you can see changes to the CSS and JS in an instant, without the need to update the page.

Other features for development involve state management, support of the most up-to-date JavaScript features, composable CSS.

You are free to contribute to this evolving project on GitHub.

Create React App

Create React App is an official start-kit for React created by Facebook. It considerably reduces time spent on configuring projects. With one simple command, you can create a project with its live development server, compiled React, JSX, ES6, CSS files. For testing, ESLint is used.

Keeping the library up-to-date is effortless as well – once a new version of Create React App is rolled out, you can update it with a single command.

React DevTools for Chrome

React DevTools for Chrome is a handy extension for making the development process more manageable. As well as ‘Create React App’, it’s created and maintained by Facebook.

With the Component tab, you can thoroughly inspect the components, edit their properties and states.

With the Profiler feature, you can track performance information.

By the way, you can select one of the 18 predefined themes to change its Look & Feel. Or even create your custom styling.

React DevTools for Firefox

React DevTools for Firefox is another add-on that offers alike functionality for inspecting a React tree right in Firefox.

Closing words

All the listed React developer tools are meant to make your development process more efficient and fast. We truly hope they will.

More to learn

If you are getting started with React, you’ll find useful this amazing overview.

If you prefer a visual approach, check out the React for designers guide.

Importance of analytics

Employees often spend hours trying to compose meaningful reports with inconvenient tools, creating a big drain on efficiency in the team.

To prevent this from happening, you can provide them with ready-to-use data dashboard libraries equipped with interactive features and intelligible visualizations.

As a developer, you can build such a dashboard right in your app with the help JavaScript libraries.

The advantage of this approach is that it makes development flexible – you can control all the aspects of a dashboard (both visual and functional) and customize it whenever required.

Where to start

Firstly, understand your problem and objectives.

Since the core purpose of any dashboard library is to communicate actionable information at a glance, you need to figure out the far-reaching goals of analysis and define metrics or KPIs to measure in a specific department.

Secondly, define the dashboard’s structure. The data dashboard can consist of many elements of varying complexity: charts, tables, maps, graphs, networks, diagrams, word clouds, timelines, widgets, etc.

Key characteristics of a full-featured dashboard library are the ease of customization and displaying trends in data.

Keeping this in mind, we’ve decided to provide you with an overview of popular JavaScript data visualization libraries which charts can be used as a dashboard’s components. We’ll also focus on “a pivot table + charts” combination and show how to build a dashboard with WebDataRocks Pivot and any of the listed charting libraries.

But firstly, a few words about WebDataRocks.

What is WebDataRocks

WebDataRocks is a lightweight JavaScript pivot table component that runs on any browser and integrates with any front-end and back-end technology. Its functionality is aimed primarily at creating tabular reports in a fast-paced and efficient manner.

The coolest thing is that you can integrate this component as a part of an embedded business intelligence solution.

Its core features are:

Aggregation functions.
Slicing & dicing data via the drag-and-drop.
Calculated values feature for tracking custom metrics.
Filtering based on members and values.
Report filters that can be applied to the entire report.
Highlighting cells via conditional formatting.
Number formatting.
Built-in exporting of reports to PDF, HTML, Excel.
The drill-through feature that helps to know which records stand behind the aggregated values.

And a lot more!

All these reporting features are at hand via the UI and make WebDataRocks Pivot so essential for data analysis.

The pivot table has a smooth learning curve. To explore more features, please refer to our UI Guide. If you prefer the code-first approach, you are welcome to dive deeper into the API reference.

Integrations

Available tutorials on integrations will walk you through using a pivot in React, Angular, AngularJS, and jQuery projects.

Another shiny feature is that you can use a pivot control with any charting library of your choice.

Charts

We are excited to present our top of charting libraries that you can use with WebDataRocks. ?

With over 8K stars on GitHub, the Highcharts library holds the leading positions among free and commercial charting solutions on the market. It’s widely used across diverse industries: from computer software and IT to higher education and financial services. It’s also favored by such top companies as Facebook, Microsoft, StackOverflow, MasterCard, and others.

You can run it either on top of jQuery or on top of its framework.

It’s easy to get started – simply install packages via CDN, npm, Bower or locally.

Data

You can load data from a CSV file, HTML tables or Google Sheets. You can even visualize the real-time data from a server.

Chart types

The Highcharts library provides a whole family of charts: Highstock, Highmaps, Highcharts Gantt.

Among the basic charts there are gauges, area, areaspline, bar, column, error bar, box plot, funnel, heat maps, line, pie, polar range, waterfall, scatter, spline charts, treemaps, and more.

Integration options

Supports TypeScript, React, Django integrations.

Documentation

The documentation is divided into topics that contain logically linked tutorials. You can quickly get used to all primary concepts and move to create more sophisticated charts.

A particular point of interest is the Use cases section. Here you can check real examples of industry-specific usage.

Customization

There are a lot of aspects you can configure through options:

Customize the design of charts by changing colors, gradients, axes, titles, predefined styles and even create your theme.
Add the drill-down, zooming features, tooltips, bubble legends, scrollbars.
Make charts responsive.

It’s also possible to extend Highcharts functionality by writing custom plugins.

General impression

Highcharts stand out from the rest thanks to a wide variety of responsive charts for all purposes and the ease of usage – the code is incredibly developer-friendly.

If you want to communicate your data effectively, this library is a perfect choice.

Dashboards with Highcharts and WebDataRocks:

FusionCharts is one of the most widely used charting libraries written in JavaScript.

It offers three types of installation: via CDN, npm or locally.

Chart types

Once you start working with FusionCharts, a broad spectrum of charts is at your disposal: maps, columns, bar, line, area, pie, donut, stacked, bubble, scatter charts, heat maps, Gantt, Pareto, box and whisker, bullet charts, gauges, and more.

All charts are categorized by purposes and accompanied by the code you can play with.

Most of the charts are available in 3D versions. There are also their real-time charts which means you can stream real-time data through charts. Another benefit is that there are a lot of charts for time-series analysis.

Integration options

It integrates easily with such front-end technologies as React, Angular, jQuery, Vue, Ember, AngularJS, and React Native. Plus, it works well with apps that are written in Java, PHP, Ruby on Rails, Django, ASP.NET.

If you want to integrate FusionCharts with Highcharts – it’s not a problem as well.

Customization options

Personalize charts by changing colors, titles, labels, add tooltips, animations, and exporting options, apply themes. Using API events, you can build custom scenarios around the component.

You can even add a special extension to make charts more accessible to any end-user and great looking on any screen and device.

Documentation

The documentation is user-friendly – it walks you through the basics to advanced custom charts creating. What is more, you can take a look at how to build each chart using various frameworks.

If you want to develop your storytelling skills and learn how to communicate information, we strongly recommend delving into the Data stories section where you can find many industry-oriented demos.

General impression

Regardless of your level of expertise, it takes a brief time to figure out how charts work and configure them. This is all thanks to a rich gallery of examples.

Dashboards with FusionCharts and WebDataRocks:

Google Charts is one of the most popular services for creating web-based data visualization. It’s created and maintained by Google.

You can install it by simply including JS scripts and loaders to your web page.

Data

The simplest way to use Google Charts is by setting JSON data in the chart’s configuration. Under the hood, it will be represented by DataTable and DataView classes.

Other options include connecting data from your database, importing data from Salesforce, Google Fusion Tables, or Google Sheets. As an advanced option, you can implement your data source protocol to fill the charts with data from custom data sources.

Also, you can embed charts right in spreadsheets.

Chart types

It offers pre-built visualizations that serve for diverse purposes: bar, column, combo, area line, geo, pie, donut, bubble, scatter charts, histograms, gauges, timelines, candlestick charts, word trees, and a lot more.

Integration options

There are no official integration guides with frameworks but you can find wrappers for Angular, React, Vue, and TypeScript definitions contributed by passionate developers.

Customization
You can customize the look and feel of every chart effortlessly by setting their options in the chart’s draw() method.
Change axes, formatters, lines, crosshairs, points, overlays, tooltips, animations, add toolbars – it’s all possible.

If you want to run certain scenarios upon the user’s actions or describe complex interactions between charts and widgets placed on the same web page, you can do it with events.

Chosen charts are available in material design which makes charts look modern in any app.

Do you want to be the first to test the grid with your data?

Download

Documentation
The library’s functionality is well-documented and exemplary. The structure of the documentation is intuitive and consists of many tutorials. It guides you through all the important aspects of visualizing data: from installing the library to creating advanced web dashboards.

General impression
Charts are easy-to-use for both beginners and skilled developers.

Check out how to create a dashboard with WebDataRocks Pivot and Google Charts:

AmCharts is a charting library that can brighten up any project with truly eye-catching visualizations. Among customers, there are Amazon, eBay, Microsoft, CISCO, Apple, PayPal and a lot of other outstanding companies.
The library runs on any platform and browser and can be used with mobile platforms.
You can include it via CDN, npm or install it right on your server. Note your browser must support SVG technology (which is true for all modern browsers).
To make it work perfectly on mobile devices, you can activate touch-specific UX features.

Chart types

A collection of charts contains pie, line, column, pyramid, polar, dumbbell, bubble, stacked column, Gantt charts, histograms and many more. It also comes with plenty of charts for visualizing time-series data. You can also visualize geographical data with maps.

With minimum lines of code and using an object-based approach, you can build any chart.

Integration options
You can use it with vanilla JavaScript, React, Angular2+ projects. It also integrates with Ember, RequireJS, Cordova / PhoneGap, webpack. You can use it as a plugin for WordPress.
The latest version is written in TypeScript, therefore, there are no problems in integrating with this language.

Customization

You can tailor colors, gradients, patterns, themes, zoom and localize charts. Besides, it’s possible to enable responsive settings of charts to make them look accurate on any device’s screen. The adapters can be used to override the default functionality.
Also, charts support right-to-left (RTL) languages such as Arabic, Aramaic, Hebrew, Farsi, and others.
To complement the existing functionality with new features, you can load extra plugins (e.g., Regression trend lines, Slice Grouper)

Documentation

We’d like to note that the product is well-documented and the documentation is nicely organized by logical sections. What is more, each demo contains the source code that helps to understand how to use charts with frameworks. You can simply copy-paste and create a chart.

General impression

All in all, this library is definitely worth your attention. Feel free to use it in your data visualization projects.

Check out how to create a dashboard with amCharts and WebDataRocks Pivot:

WebDataRocks and amCharts: Pivot Table with Donut Chart

AnyChart is a JavaScript library that provides a collection of beautiful charts that run on any device and browser, including IE 6 and can be integrated with any app.

A lot of companies, startups, governmental and educational organizations all around the world choose AnyChart. Among the largest ones, there are Microsoft, Ford, Samsung, At&T, Nokia, Bosch, Oracle, McDonald’s, Citi, and others.

Chart types

There is a family of products that serve different purposes. You can use AnyStock to visualize financial data, AnyMap to display data in maps, AnyGantt to track performance inside the organization.

Other kinds of charts and maps you can try are area, bar, bar mekko, box, pie, Pareto, line, error, bubble, cherry, bullet, column charts, gauges, maps, candlestick and bullet charts, and many more. All of them are noteworthy.

Besides, the list of charts is constantly updated.

Integration options

Works well with scripting languages (PHP, ASP, ColdFusion, Perl).

Integrations with Elasticsearch and Backbone are planned.

Customization

You can customize everything manually or by using scripts.

Documentation

The documentation allows walking through all general concepts of using charts effortlessly. It’s divided into sections: the General User’s Guide is a great place to start with, the API Reference with all properties and methods, the Playground – a sandbox for running charts, and Chartopedia for discovering all kinds of charts.

General impression

This lightweight charting library offers brisk visualizations that deserve to be a part of your data visualization project.

Dashboards with WebDataRocks and AnyChart:

AnyChart Gauge and WebDataRocks Pivot

ChartJS is a free and open-source JavaScript charting library.

Getting started is easy: you can install it via CDN, Bower, jsDelivr, or npm and create your first chart.

Data

To fill the chart with information, you should make sure that your data fits the structure required for a certain type of visualization. In most cases, it’s an array of numbers or objects.

Chart types

There are eight kinds of charts you can add to your project: bar, line, pie, radar, area, bubble, scatter and a polar area.

All charts are rendered in canvas elements that are supported in all modern browsers.

Customization

You can configure fonts, colors, texts, tooltips, scales, chart’s size, gradients, as well as add scriptable properties and transitions.

What is especially cool is the ability to configure animations. There are a lot of easing functions that modify the motion of charts.

Also, try using plugins – they represent an extra way to change the charts.

Integration options

You can use ChartJS with plain JS and various bundlers. There are also React, Meteor, Vue, Ember, Angular wrappers available.

We recommend trying extensions for ChartJS that extend the capabilities of the original charting library. The extensions are contributed by developers – you can support this trend and suggest your ideas of making charts better.

Documentation

The most important information is at your fingertips in the documentation which is short yet comprehensive.

General impression

ChartJS is lightweight and simple. The visualizations it has to offer are enough for basic data exploration. But if you need more diverse charts (e.g., for statistical analysis or visualizing geospatial data), it’s better to stick to other solutions presented on this article’s list.

See how to create a data dashboard with WebDataRocks Pivot and ChartJS:

Sales dashboard with Polar Chart and Pivot Grid

Putting it all together

In this blog post, we’ve covered top JavaScript data visualization libraries and showed how these can be combined. We encourage you to take some time exploring the data in interactive dashboards. Hopefully, these will help to improve your data analysis and bring value to your business.

Extra: pivot table personalization

We believe customizing should be easy. Hence, we’ve prepared plenty of tutorials to make your pivot table component look and work uniquely:

How to become a better developer

Searching for ways to improve your programming skills? Try GitConnected – here you can find a rich collection of the most up-to-date coding tutorials.

Microsoft Excel is spreadsheet software that has been a standard on the market of reporting tools for many years. It’s designed to assist you in summarizing and analyzing data or keeping it in tabular format. Excel is also loved for its diversity of mathematical formulas for every situation. They come in handy when you need to solve a complex mathematical problem.

Excel is one of the most widely used reporting software in the business world. A lot of small and large companies use it for different data analysis purposes.

But before investing in licensed software, you can try other solutions that offer more flexibility and allow visualizing data right in your app.

Today we’ll research on the features of two tools for reporting – WebDataRocks and Excel and compare the ways they provide reporting experience.

We hope this article will help you to pick what is right for you.

Overview of features: Excel

Interface & learning curve

Excel strikes with the available functionality related to data visualization. But once you are taken to its workspace, it’s easy to get lost among the variety of tabs and bars. Figuring out the purposes of each one may be overwhelming for a newcomer. It often takes a lot of time before you learn how to navigate swiftly and efficiently. In general, the learning curve for adapting to Excel can be described as steep. You need to invest a lot of time to learn how to do reporting rather than extracting value from the data.

Scalability & performance

Performance is always a key consideration in any data analysis and reporting process.

Excel ensures fast data access and processing because of the way it manipulates memory. When you are working with spreadsheets, an entire file is loaded into RAM. On the one hand, this approach speeds up data access. On the other hand, the whole document has to be reloaded into RAM again once you make any small change to your current workbook.

Thus, working with spreadsheets that contain large amounts of data (~ 200k records) can be rather slow and not efficient.

Customizability

If you are willing to change some parts of the interface, you can personalize the Ribbon, Quick Access Toolbar, change the language, etc. In this sense, Excel is customizable enough.

Integrations

Excel works with almost every other product of Office 365. You can add spreadsheets to presentations or text reports.

But to move data between Excel and any third-party app, you need to use external services since there are no off-the-shelf integration options available.

Working online

Not only can you use Excel as a desktop app but also in the web browser. Pay attention to the fact that there are certain variations in the functionality of both versions. However, it’s not possible to embed Excel into a web application.

Teamwork

Shared workbooks had many limitations previously but now you can use Excel for collaborative work with the help of the `Co-authoring` feature. Provided that you have a cloud-based version of Excel (as part of an Office 365 subscription), your colleagues can have remote access to your spreadsheets and see updates nearly in real-time. Without this subscription, another way to send a report is via an e-mail or a portable storage media which leads to duplicating information and is a neither secure nor convenient way to share the data.

Excel as a data storage

Many companies use Excel as a database. Despite certain similarities between spreadsheets and databases, Excel lacks the capabilities of a database management system. Spreadsheets can’t meet the requirements on data integrity, consistency, security, ease of updating and retrieval of data. Moreover, as mentioned before, multiple users can’t access and update such a kind of database at the same time if they don’t have ‘Co-authoring’ feature enabled.

Considering all, it’s more convenient and reliable to keep your data in relational databases rather than store it in spreadsheets.

What about Pivot Tables?

Excel is primarily known for its pivot tables – the most indispensable tool when it comes to summarizing the data and getting insights from it.

But be aware that adding pivot tables may affect the performance – they double increase the workbook’s size because of the large caches they require.

Cost

Excel is not expensive for companies but can be costly for individual users.

Conclusion

Many people use Excel because it’s comfortable and they get used to it.

But why not break out of your comfort zone and move towards web-based solutions? Especially if you have exhausted the possibilities of Excel’s pivot tables.

It’s worth to note that using web pivot table components instead of Excel pivot tables implies going to a programmatic approach. The advantage of such an approach is that you can embed the component into any app, fully control the access to it and tailor pretty much every aspect of your reporting experience.

And this is where WebDataRocks comes into play.

WebDataRocks

WebDataRocks is a client-side pivot table component designed to provide data analysts with summarized data in the form of a tabular report. You can easily embed this tool in your web application and empower your end-users with reporting.

Let’s see what it has to offer.

Interface & learning curve

To make your learning curve less steep, we developed a highly intuitive interface that enables you to concentrate solely on the reporting purposes without being distracted by redundant functionality. All vital features are at hand, most of them are accessible via the Toolbar.

Customizability

You can customize WebDataRocks according to the style of a particular application, add or remove tabs to/from the Toolbar, and leave only the features which you need. You can easily switch between different layouts, apply predefined themes, add icons to the cells, and change everything through the UI or in code to make the component look natively in your app.

Additionally, you can bring to life your ideas about design and create a custom report theme.

Flexibility

The previously mentioned customization options are what make WebDataRocks a flexible reporting tool that ideally suits Agile practices. In particular, it adapts to such Agile principles as continual improvement and flexible response to changes.

Integrations

As a developer, you can experience the freedom of integration with popular frameworks such as React, Angular, AngularJS, Vue, and more.

To facilitate the integration process, you can make use of the available tutorials:

Besides, you can follow an API-first approach with a lot of API calls that make interacting with the tool easier and allow building your scenarios around the component.

Responsiveness

Works perfectly on all browsers and mobile platforms. Looks natively in any app.

Charts

In Excel, you are limited to use default charts and apply a set of defined styles to them. Of course, you can install some add-ons of 3rd-party charting libraries but it takes time to figure out how to use them in Excel’s workspace.

Here is how we solved this problem in WebDataRocks – you can integrate the pivot table component with any charting library you like. All you need to do is to pivot your data and pass it to any kind of chart. To make things easier, we prepared a series of tutorials on integration with the most popular charting libraries. If your favorite library is not on the list, you can make use of webdatarocks.getData() method to preprocess the data accordingly to the format the chart requires.

Ease of use and configuration

With little or no IT help, you can set up a powerful data visualization tool in no time. The Quick start guide will walk you through the basics of working with the component.

Teamwork

We know how teamwork matters: it provides the company with a diversity of thoughts and perspectives. After installing your instance of a pivot table tool, you can give role-based online access to analytics inside the application. Moreover, you can customize it to make a read-only mode of the pivot table.

Saving reports

We protect you from losing your working results.

Save the report in a JSON file and restore it anytime.

The exporting of reports is meant to be as fast as possible – WebDataRocks comes with exporting functionality bundled in which means there is no need to install custom plugins for exporting to PDF, HTML, and, of course, Excel. You can even send your report for printing. If you need to send the report to a server, it’s not a problem at all as well. To make that possible, you can customize exporting configurations with a few lines of code.

Data size

You can load up to the 1MB of data.

Cost

Free.

You can get WebDataRocks at no cost and save your money for other business investments.

Integration with Excel

To help you make the best of both Excel and WebDataRocks, we made a transition from Excel to WebDataRocks as smooth and simple as possible.

You can load your CSV data into the pivot table component and start pivoting your data in a new web-based interface.

Besides, if you feel you miss the spreadsheets and want to see the raw data, you can always switch to the flat mode and view non-aggregated data in a row-by-row manner.

Putting it all together

Today we reviewed the capabilities of a desktop reporting software (Excel) and a web-based pivot component (WebDataRocks). While the former is a great example of a standalone application, the latter can be successfully used as an integrated component of a larger Business Intelligence platform.

The choice is yours.

Live demos

How to become a better developer

Searching for ways to improve your programming skills? Try GitConnected – here you can find a rich collection of the most up-to-date coding tutorials.

In the final part of the data visualization project, we’ll discuss the charts that visualize the distribution of univariate and bivariate data.

Histogram

A histogram is the most commonly used plot type for visualizing distribution. It shows the frequency of values in data by grouping it into equal-sized intervals or classes (so-called bins). In such a way, it gives you an idea about the approximate probability distribution of your quantitative data.

Structure

The histogram is composed of vertical or horizontal bars. The height of each bar corresponds to the frequency of values that fall into this bin. By changing the bin width, you also change the number of bins – this will affect the shape of a distribution.

Purpose

To visually represent the distribution of univariate data. Additionally, with the histogram, you can figure out information about the center, spread, skewness of data as well as the extreme values, missing or non-typical values (outliers). In addition, you can check whether the data has multiple modes.

One should not confuse histograms with bar or column charts – though these graphs are alike, they play totally different roles in data visualization:

The histogram illustrates the frequency of continuous values that are grouped into ranges of a data series and represents distribution while the column chart compares values of a categorical data.
The most noticeable visual difference is in the existence of spaces between bars: there are no spaces between bars in the histogram but they can be in the column/bar chart.
It’s impossible to rearrange the bars in the histogram. With the column chart, it can be done without the loss of meaning.
Columns in the column chart have equal widths but columns in the histogram – don’t.

Example

The distribution of the country’s population:

Box and Whisker Plot

A box and whisker plot is one of the most popular charts when it comes to statistical analysis of data distribution.

Structure

A box contains three important numbers: the first quartile, median, and third quartile. The other two numbers are the minimum and maximum – these are represented by whiskers.

These five numbers divide the dataset into sections. Each section contains around 25% of the data.

Example

Conclusion

Today you’ve learned more about charts that can be used for visualizing data distribution. We encourage you to learn by doing and try creating such charts in your data analysis project.

What’s next?

Eager to learn about other chart types? You are welcome to read the previous blog posts of the data visualization project:

References

Move up

Tech stacks explained

What is a tech stack in software development?

Structure of a tech stack

What is a back end stack?

What is a front end stack?

Why tech stack matters

How to choose your tech stack

Vue CLI

Storybook

Vuetify

VuePress

VueX

Nuxt

Gridsome

Learn more

Data Science Courses

Data Science and Analysis Learning paths

Data Science Specializations

1. Beyond CSV and JSON: working with other data sources

2. More flexibility of integration

3. Extended filters

4. Total alignment with Material Design

5. More customization options

6. Pivot Charts

7. Extended API

8. Advanced customer support

Discover Flexmonster Pivot Table & Charts

Step 1: Add Highcharts dependencies

Step 2: Add WebDataRocks Pivot

Step 3: Initialize the pivot table on the page

Step 4: Create a report

Step 5: Pass the data to the charts

Step 6: Enjoy the resulting dashboard

Data Science

Skills to become a proficient data scientist

Skills to become a proficient data analyst

Have you read?

What is React

CodeSandBox

Rekit Studio

React Studio

React boilerplate

Create React App

React DevTools for Chrome

React DevTools for Firefox

Closing words

More to learn

Importance of analytics

Where to start

What is WebDataRocks

Charts

Putting it all together

Extra: pivot table personalization

How to become a better developer

Overview of features: Excel

Interface & learning curve

Scalability & performance

Customizability

Integrations

Working online

Teamwork

Excel as a data storage

What about Pivot Tables?

Cost

Conclusion

WebDataRocks

Interface & learning curve

Customizability

Flexibility

Integrations

Responsiveness

Charts

Ease of use and configuration

Teamwork

Saving reports

Sharing results

Data size

Cost

Integration with Excel

Putting it all together