Leveraging D3 and Angular to Visualize Big Data

Leveraging D3 and Angular to Visualize Big Data

Leveraging D3 and Angular to Visualize Big Data - Visualizing big data signifies the visual representation of available information in the quantitative form like charts, graphs, and many more.

Being able to visualize the data in its most authentic way is not only useful for the development platforms, but also the people. D3 and Angular have lots of fundamental processes through which you can add or make changes in the document. 

Data visualization significance

Data visualization in big data can be assumed as the virtual representation of data with the help of graphs, plots, and informational graphics. It is a statistical representation of the data in the most precise manner. There are many disciplines to view data visualization equivalent to advanced and modern visual communication. Also, data visualization includes the creation and artistic representation of data. Its primary advantage is that it enables the user to actively see the connections which are occurring in between the operational conditions and the organization's performance. Using the advancement of Angular, it provides the extension of templates where the component gets attached accurately. Using the instances of existing elements in an array provides better visuals.

Understanding D3

D3 is the most preferred and extensive JavaScript library, which is incredible for data visualization. It is used to manipulate the files or documents which are based upon the data and then bring them to a different level with the help of HTML and other platforms. It mainly focuses on the web standard to provide the capabilities of the Advanced Browser along with a proprietary framework. D3 JavaScript library combines the data visualization components and an approach with the document object model (DOM). Later D3 applies the same data-driven transformation for the corresponding document.

D3 is an open-source JavaScript library that helps users by providing robust manipulation of the document object model (Dom) which is data-driven. It also gives access to the rules which can create the desired visualization, and transformation gets more realistic by providing shape and creation to the data. Multiple layouts are equipped to transform the visualizing data tools into various other representational forms. It allows the data to pass through several transitional stages to ensure a visual flair once the data change. It is a stable JavaScript library for producing dynamic and interactive visuals in the Browser. Along with it, D3 also provides a large number of graphic options which is incredible for the developer. Visualization is estimated to be the core dimension of D3. 

Selection ()

D3 allows for the selection of elements just like JQuery. In this method, the data gets associated with the initially selected elements. If the code is run and its nodes are inspected in the DOM models, then the result will appear where each node has improvised data. The select () method selects the element from the same document. Also, it undertakes the argument for the corresponding name which you want to add, and then returns the HTML node in the sequence having similarity with the name. D3 utilizes the association of data for the subsequent process. Similar to JQuery, D3 also allows for the changing of calls as it can efficiently manipulate the DOM model elements.

Append ()

This method takes the argument from the element which you want to add in the data and then, it is depended over the HTML node for the selected item and Returns a new direction to that node.

Enter ()

It is instrumental when the data is a lot more than the corresponding DOM elements. Enter is also used when there is a need to add items in the DOM model. This method enables the user to set the selected text of note or start with the current document. To set the value of the string is passed as an argument. D3 follows three basic types of selection-

  • Existing DOM elements
  • Enter selection
  • Exit selection

D3 is based upon the web credentials which allow the user to transform data and give it a new platform with the help of HTML, SVG, and CSS. It is easy to start with D3, and to proceed with the visualization, we would require an SVG element.

Say like creating a multilevel chart to display the data. To visualize the data, we initially need to transform the shape and carry out the necessary customization. 


Basic updation

D3 follows a pattern for how you deal with the corresponding data; this pattern could be 

Enter, update, join, and exit. This process is supported by the joining of data with new as well as existing data, which is followed by the sequence of entering, updating, and the removal of data. It gets more simplified as a pattern is using data (), append (), transition (), and exit () with D3. By combining all the utilities with the visualizing data, D3 performs all the necessary activities to create the chart.

Scales in D3

  • Existing DOM elements
  • Enter selection
  • Exit selection

This scale will map all of the discrete values into an array of distinct values. If the amount is shorter than the domain, then it will repeat itself.

  • Existing DOM elements
  • Enter selection
  • Exit selection

They are similar to the ordinal scales, but the difference is they use a continuous range. Also, it splits the whole field into multiple numbers of values in the domain array. This scale divides the entire range into the segments scaling their costs as per the accordance.

  • Existing DOM elements
  • Enter selection
  • Exit selection

Linear scales encompass the numbers or dates which are of a continuous domain. It is the y-axis in a graph and also one of the most preferably used scales in D3.

Key features of D3

It is evident that interactive visualization can add a more impressive form to the data sets. Significant characteristics of the data set are that its use as an application is enhanced. This allows the user to make a selection with the specific data sets to visualize the way he wants. This is efficiently supported by D3 framework, and the features of D3 are as follows-

  • Existing DOM elements
  • Enter selection
  • Exit selection

D3 utilizes web standards mostly of HTML, CSS, and SVG. This feature allows for the quick implementation of the script on the cross the platforms without the actual need of any specific Technology or any other plugin from Browser. Along with it, D3 also allows the data visualization to be performed uniformly indulging with other scripts and frameworks. Addition can be put into place with the website without disturbing the coding, and one of the best parts of D3 is that it is incredibly lightweight. D3 can work with any of the web standards regardless of the specification; hence, it is fast.

  • Existing DOM elements
  • Enter selection
  • Exit selection

It is placed over data; hence, it is driven by the same by providing the input for statistics and fetching the data for the remote servers. It can be done across multiple formats and arrays to JSON, CSV, and XML. With the help of these, various charts can be created and the heavy use of its data-driven Technology powers up the data. D3 allows for the dynamic generation of different elements and tables irrespective of the form.

  • Existing DOM elements
  • Enter selection
  • Exit selection

D3 is flexible as well as dynamic, hence it is a handy tool for providing the active property in the functions. Data that is input into the script can quickly transform the styling and attributes which are required for the data visualization of any corresponding data set. 

  • Existing DOM elements
  • Enter selection
  • Exit selection

It is essential to understand that D3 does not introduce any form of visual representation, and unlike other platforms, its vocabulary of the graphics is derived directly from the web standards. Say, users can create SVG elements with the help of D3 and style them with the available style sheets. Even the composite effects of strokes can be used to transform. If any of the latest features are introduced, you can use it immediately as no Toolkit update is required. Other than 883 is quite simple to Deepak with the browsers inbuilt inspector encodes through which you can manipulate D3 in the same standards.

  • Existing DOM elements
  • Enter selection
  • Exit selection

Rather than representation, D3 focuses on the transformation of data sets, extending them directly from natural to the animations. Over time D3 transitions simultaneously interpellate the available styles and attributes, and Tweening can be regulated with natural functions. The enter politics of D3 efficiently supports the primitive numbers and number systems that are embedded in the strings along with the compound values. Even it can be extended to the registry to support complicated properties of data structures.

Source: [https://d3js.org//caption]

This is an example to fade the background of the page in black. Just by transforming the attributes of the code D3 minimize the overhead and also enables the graphical assembly and complexity at the higher frame. The Sequencing of the complicated transition also gets allowance with D3 while not replacing the toolbox of Browser. It instead uses an explosive way to make it easier.


Angular is a powerful platform to build and develop mobile as well as desktop applications. We can also call it an open-ended hierarchy of the Framework, which is designed to make the applications providing all the necessary elements all at once. It is a combination of multiple things that serve as the building blocks of the UI and corresponding code. Angular has a fitted high learning curve, and moreover when it comes to reacting. Angular allows the developer to build the dynamic client-side applications which single all multi-page association. 

Along with that, there are several other advantages of it-

  • Existing DOM elements
  • Enter selection
  • Exit selection

All of these advantages together provide a feature-rich development platform to build web or desktop applications efficiently.

  • Existing DOM elements
  • Enter selection
  • Exit selection

The combination of data visualization is justified in Angular, and the user does not need to aggregate the data anyway. 

  • Existing DOM elements
  • Enter selection
  • Exit selection

For the data aggregation, a visualization dashboard is always a better choice. Most of the analysts prefer panel, as they provide better metrics for tracking the business and also help in making the data-driven choices. As a developer, it requires lots of effort to configure it to follow the requests.

Google Charts

It is one of the best charting services which do not require any relation of the elements with D3. Instead, it uses the Google Charts to provide transformation of the data and then pass it on in the drawing function.

How to load the data in charts

To load the data in corresponding tables, a function is used, which is known as a data source. This service supports the chart tools Datasource protocol and also allows the user to send the SQL Query into it for retrieving the table filled with data. There is another web service which is known as a flex monster pivot table which is used as a provider from the client-side to aggregate the data across multiple data sources. These components also offer the JavaScript connectors to eliminate the requirement of writing code for data processing. This also includes the processing through different chart types, but for the implementation, you need some custom logic.

  • Existing DOM elements
  • Enter selection
  • Exit selection

Google Charts is compatible with Angular as one of the charts and tables are in the opposition with their corresponding self. Contrary to it, when they get combined, it gets productive. The dashboard has the efficiency to promote and enhance the communication of the analysis outcomes.

  • Existing DOM elements
  • Enter selection
  • Exit selection

Data set is the core for any developmental project and manipulation, or aggregation of the sets is time-consuming. Things get worse when the data changes because you have to go through the same process all over again. Creating the visualizations for angular application is not hectic and complicated instead, it can be achieved by following a few simple steps. First of all, you have to install the UI in the angular application (it can be done by using npm or yarn). Later to it follows-

  • Existing DOM elements
  • Enter selection
  • Exit selection

It is important to note here that this course can also be used in an individual HTML file if the properties of components are not set accurately.

Features of angular
  • Existing DOM elements
  • Enter selection
  • Exit selection

Angular is suitable and fit for various platforms, and all of them can use the codes. It even enables the use of systems and abilities to develop applications in angularjs for target deployment. It has equal and distributive efficiencies for web, mobile as well as native desktop requirements.

  • Existing DOM elements
  • Enter selection
  • Exit selection

Angular provides the maximum speed to the developers for a web application built up by web workers as well as server-side rendering. It also provides regulation with scalability as it meets the requirement by building data models on RXJS or immutable JS. It has the same utility for other models. 

  • Existing DOM elements
  • Enter selection
  • Exit selection

As it is feature-rich, the components get more declarative, and simple templates are used for the development. It also extends the language of the model with the predefined elements and utilizes the vast array of existing ones. It comes with lots of other features so that the developers can entirely refocus overbuilding the application rather than trying to arrange things or to manage the coding.

Angular has Global deployment as its productivity is inseparable. Also, it provides the most efficient and scalable hierarchy which supports most of the application and infrastructure.


At Cuelogic, we have worked extensively on D3 and Angular, and can vouch for the advantages of using the same. Now that we have discussed the concept of visualizing big data in the quantitative forms, we can chain multiple methods all at once along with the periods so that several actions can be performed in sequence. As a front end developer, things get most excited when it comes to visualizing the data. The libraries used for the solution are feature-rich, and they run on the client-side.

*Originally published ***by**shital agarwaat  cuelogic.com


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Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.

In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics

Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.

What Is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

[Source: gfycat.com]

It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.

What is Big Data?

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.

[Source: gfycat.com]

What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.

[Source: ibm.com]

What Does Data Scientist, Big Data Professional & Data Analyst Do?

What does a Data Scientist do?

Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.

Roles of Data Scientist

What do Big Data Professionals do?

The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.

Roles of Big Data Professiona

Big data professionals describe the structure and behavior of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, Kafka etc. based on requirements.

What does a Data Analyst do?

Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.

Roles of Data Analyst

Skill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst

What Is The Salary Prospect?

The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.

A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.

Now, let’s try to understand how can we garner benefits by combining all three of them together.

Let’s take an example of Netflix and see how they join forces in achieving the goal.

First, let’s understand the role of* Big Data Professional* in Netflix example.

Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.

Approach in Netflix

Traditional Data Processing

Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.

Big Data approach to process Netflix data

Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.

2. Improving the streaming experience

How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?

One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.

For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.

By determining all these factors one can improve the streaming experience.

3. Optimize content caching

A set of big data problems also exists on the content delivery side.

The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.

4. Improving content quality

Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.

In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.

By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.

So this is how Data Scientist optimizes the Netflix streaming experience.

Now let’s understand how Data Analytics is used to drive the Netflix success.

Role of Data Analyst in Netflix

The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.

So what does a Data Analyst do?

Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.

Big Data Tutorial - Big Data Cluster Administration

Big Data Tutorial - Big Data Cluster Administration

Big Data Tutorial - Big Data Cluster Administration: In SQL Server 2019 Big Data Clusters, we ensure that management services embedded with the platform provide fast scale and upgrade operations, automatic logs and metrics collection, enterprise grade secure access and high availability. In this video we will provide an overview of these administration experiences for Big Data Clusters.

In SQL Server 2019 Big Data Clusters, we ensure that management services embedded with the platform provide fast scale and upgrade operations, automatic logs and metrics collection, enterprise grade secure access and high availability. In this video we will provide an overview of these administration experiences for Big Data Clusters.

Big Data and Hadoop Framework Tutorial for Beginners

Big Data and Hadoop Framework Tutorial for Beginners

Big Data and Hadoop Framework Tutorial: Data analytics, Apache spark, hive, pig, Data Science, MapReduce, Machine learning, Aws EMR, Azure Machine learning

This course is focusing on Big data and Hadoop technologies, hands on demos,

Section 1 - Big data

1.1 Big data introduction

1.2 Big data history

1.3 Big data technologies

1.4 Big data characteristics

1.5 Big data Applications

1.6 Data Lake

1.7 Data Science and Data scientist

Section 2 - Hadoop

2.1 - Hadoop introduction

2.2 - HDFS-Overview

2.3 - Hadoop Architecture

2.3a - Hadoop Architecture - assumptions and goals

2.4 - Demo-Hadoop install - sw download verify integrity

2.5 - Demo-Hadoop install - Java ssh configure

2.6 - Demo hadoop access by browser

Section 3 - Machine Learning

3.1 Machine learning introduction

3.2 Machine learning algorithms

3.3 Machine learning softwares

Module 4 - AWS Machine Learning

4.1 AWS and Machine learning introduction

Below will be added soon.

4.2 Bigdata and aws

4.3 Hadoop on Amazon Elastic Map Reduce (emr)

4.4 What is EMR

4.5 EMR Architecutre

4.6 Demo - launch EMR cluster

2.6 - Hadoop single node cluster setup

2.7 - Hadoop single node - Pseudo-Distributed Operation

2.8 - Hadoop multi node cluster setup

2.9 - MapReduce

2.10 - Azure HDInsight

2.11 - HDFS-Operations

2.12 - Apache Spark and Big data analytics

2.15 - Hadoop, Hive and Pig

What you'll learn

  • Understand Big data technologies, Data analytics and Hadoop framework