What is Big Data?

Big Data is an algorithm that deals with data science sets that are excessively large or complex and not easily computed with the traditional data-processing application software Available. Data with many rows have higher statistical power, whereas the data with higher levels of attributes or columns may lead to a higher Complexity Rate in the available data set. Big data concept includes several functions like capturing data, storage, analysis, searching data, sharing, transferring, visualizing, querying, updating timely, information privacy, and data source. 

data science, big data

Big data was actually Initiated with three key concepts: 

  • Volume
  • Variety
  • Velocity 

When the user handles big data,  one cannot sample but they can simply observe and track the actions occurring in the processing mode. Thus, the big data concept is often used to include the data with sizes that exceed the capacity of ancient Software to process within the specific time and value.

Currently, the term big data is used to refer to the use of doing predictive and user behavior analytics, or certainly many other data analytics methods that extract value from data.

Relational database management systems also commonly known as RDBMS has software packages that are used to visualize data when it’s very difficult to handle big data.

Big data uses various sources such as mathematical analysis, optimization, statistics and concepts from non- linear system which helps to imply on various concepts like regression Analysis, nonlinear and linear relationships, and causal effects from large data sets with a very low information density to reveal relationships and dependencies and it also helps to perform predictions of outcomes and their respective behaviors.


Big data has specifically 3 following characteristics:


The term Volume specifies the quantity of generated and stored data. The size of the data determines the value and potential counts, and whether it can be counted as a term of big data or not.


This specifies the type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data is drawn from text, images, audio, video; also it completes missing pieces with help of data fusion.


The velocity specifies the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data concepts are produced more continually. There are specifically two kinds of velocity related to big data is the frequency of generation and the frequency of handling, recording, and publishing.


It is the external concept for big data, which refers to the data quality and the data value. The data quality of captured data may vary greatly, affecting accurate analysis.

data science, big data

Why Is Big Data Important?

Big data has it’s own importance which doesn’t revolve around the quantity of data you have, but what you do with it. You can collect data from any source and analyze it to find solutions which help in 

  • Cost reductions
  • Time reductions
  • New product development and optimized offerings 
  • Smart decision making. 

When the big data concept is combined with high-quality analytics, you can publish business-related tasks such as:

  • Determining major causes of failures, issues occurring and defects in near-real time
  • Generating coupons at the time of sale based on the customer’s buying habits
  • Calculating entire risk portfolios in minutes again and again
  • Detecting fraud/misleading behavior before it affects the organization

Mention few Use Cases of Big data

There are various use cases with the concept of Big Data but most commonly known and used ones are mentioned below:

  • Product Development

Companies like Netflix and (P&G) Procter & Gamble use big data to forecast customer demand. They build such predictive models for new products and services by classifying the key attributes of past and current products or services been used by customers and modeling the relationship between those attributes and the commercial success of the offerings. Also, P&G uses data and analytics from various groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.

  • Predictive Maintenance

Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost-effectively and maximize parts and equipment uptime.

  • Customer Experience

There is always a race between customers to grab the best deal available. A clearer view of customer experience is more possible now than ever before. Big data enables us to gather data from social media, web visits, call logs, and other sources to improve the experience and maximize the bundle of benefits being delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.

  • Fraud and Compliance

When it comes to security, it’s not just a few black hackers trying to steal the data from the website.

Security parameters and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and monitor large volumes of information to make regulatory reporting much faster.

  • Machine Learning

Machine learning is a rich topic right now. And specifically, big data is one of the reasons for it. We are now able to directly teach machines instead of program them of “How to perform a task”. The availability of big data to train AI learning models makes that possible.

Opt for the best machine learning online course to understand the topic and its working in detail.

  • Operational Efficiency

Operational efficiency may not always make the buzz, but it’s an area in which big data is having the most impact. With big data, you can analyze and examine the production, customer feedback and returns, and other factors to reduce extras and forecast future demands. Big data can also be used to improve decision-making in line with current market demand.

data science, big data

How does Big Data Works??

Big data gives the user a new outlook that presents them up with new opportunities and business models. 

Big Data involves three key actions:

  • Integrate

Big data recollects data from distinguishing sources and applications. Traditionally data integration mechanisms, such as extract, transform, and load generally aren’t up to the mark. It requires new mechanisms and technologies to analyze big data sets at terabytes, or even petabytes, scale.

During the integration process, you need to collect the data, assemble it, process it, and make sure it’s formatted and available in a form that your business analysts can use it as per the requirements.

  • Manage

Big data requires a lot of storage. Your storage solution can lead you to cloud storage or on manual, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently saved. The cloud is gradually gaining popularity because it supports your current manual requirements and enables you to access the resources whenever needed.

  •  Analyze

The investment made by the user in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

What Is Data Science?

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

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

data science, big data

What are the differences between Data Science and Big data ?? 

Data ScientistBig Data Professional 
Statistical & Analytical SkillsTechnologies like Hadoop, Spark, Hive, etc
Data Mining ActivitiesWorking with unstructured data
Co-relationGeneral Purpose Programming
Machine LearningSQL/Database coding
Deep Learning principlesFamiliarity with MATLAB
In depth knowledge of programmingCreativity
SQL/Database codingBusiness skills
SAS/R CodingData visualization

Data Science vs Big Data Application Areas

Application Areas of Big Data

  •  Communication Media

Telecommunication Organizations need big data to gather more and more new subscribers, eliminate the old ones, and spreading their business with existing customers. By combining and analyzing the continuously generated data by the users and systems (machine-generated), big data enables us to resolve the related issues in the Telecommunication sector.

  • Big Data for Retail

Understanding customers’ needs are the objective of any business, be it an online e-retailer or a Medicare store near the street. The capability of analyzing various sources of data that businesses handle on a daily basis is what big data justifies. Be it customer transaction data, weblogs, data from store-branded credit cards, loyalty program data, or social media, big data is responsible enough to take charge of all.

  • Financial Services

Big data is consumed by organizations such as retail banks, credit card companies, insurance firms, private wealth management advisories, venture capitalists, as well as investment banks. Big data helps them resolve the issues with the high volume of multi-structured data collected in their systems and manage them efficiently. 

What are the functions of Big Data ?? 

The major functions of big data are –

  • Fraud Detection 
  • Customer Survey
  • Operational Parameters 
  • Compliance Services 
  •  Education 

As the concept of Big data has been highly adopted and bring in to action by various technologies by the industries and the executives, the education domain has not left untouched with the applications of big data. As the big data professionals are in demand these days, the big data expert trainers are also in the huge demand, how can they be left-back. It is the application area of Big Data where the individuals can make a bright career by collaborating or Hiring big data professionals for the businesses, companies, and industries.

This states that Big Data has a large number of applications in almost all industries, areas, and domains. Whether you are thinking to build a big data career as a fresher or have some knowledge in Big Data, there are a number of opportunities for you.

Update your big data knowledge with a certification. There are many big data certifications that can take your career to the new heights.

data science, big data

Application Areas of Data Science

  • Digital Advertisements

Data Science algorithms are highly beneficial for the digital marketing era, ranging from the display banners but not only limited and focussed to digital billboards. Data science drives the CTR rates of digital ads higher in comparison to earlier used conventional advertisements.

  • Internet Search

Data Science is the backline that determines the hidden backend algorithm behind search engine results. It motivates the search engine robots to spy through the diverse content available on the internet, as soon as you hit the search key on any search engine.

  • Recommender System

The recommender system of data science helps in effective user-experience and the easy way of finding/searching for a relevant product over the internet. Companies promote a huge range of products and give you suggestions, while you browse the internet or through ads popping in the apps downloaded, depending on the demand and relevance, which are influenced by your search history.

  • Image/Speech Recognition

Image and Speech recognition provides an effective and efficient user experience to individuals over the internet. It offers the barcode scanning tool in mobile, tagging your friend feature on Facebook, and to perform an image search on google by using a face recognition method. Similarly, speech recognition has made the life of people even easier, one can perform a  search action even when he is not in the mood of typing. It works on the model of speech to text conversion, Google Voice, and Siri is examples of speech recognition products.

Data Science vs Big Data: Skills Required

Let’s get to know the differences between – data science vs big data related career options and desired skills and decide what is best for you. So, it’s easy for an individual to choose the right career path in no time.

Skills Required to Become a Data Scientist

For becoming a data scientist, you need to be eligible in the following skills –

  • A great understanding of SQL database/programming (also execute complex calculations), even if Hadoop and SQL prefer the data science segment.
  • Hadoop platform knowledge,  Although, it’s not compulsory. 
  • Preference given to deep knowledge of R and SAS is required, specifically R.
  • Programming knowledge of Python is important along with C/C++, and Java.
  • Knowledge of maintaining unstructured data such as social media, audio, or videos as well.
  • Good academic background, preferably a technology-related degree Like Btech.

Skills Required to Become a Big Data Professional

If you are thinking to opt career as big data professional then you need to acquire the mentioned specific skillset –

  • Creativity to implement new ways of gathering, analyzing, and interpreting a strategy for data modeling.
  • Analytical skills to understand big data and choose the relevant ones to solve a given problem.
  • Understanding of algorithms and computing to process data and get better results into big data.
  • Business skills to understand the business goals and objectives along with the backend processes responsible for the growth and profit in business.
  • Statistical and mathematical skillsets for ‘number mushing’ and generating better solutions.
data science, big data

Data Science vs Big Data: Salary

When we talk about all sectors n domains of Both the roles and concepts then how can we forget to compare the salary part while having a comparison between Data Science vs Big Data?

 Being in an identical industry the professionals (data scientist, data analyst, and big data specialist) don’t have a fixed salary range. The pay packages may vary for data scientists and are higher than that of the big data professionals.

But the concept of Hadoop is the other big name of Big Data, and a report by AMR specifically suggests that the Hadoop market will reach $88.8 million by the end of 2021. This implies that there will be more and more opportunities arising soon for big data professionals in the upcoming years.

Let’s about the Big Data job trends, the demand for big data professionals is tremendously increasing in India. India alone contributes 15% of the worldwide big data market, and even currently approx 70,000 vacancies are available in India related to Big Data in various business sectors.

According to the comparison platforms such as Glassdoor and Indeed.com, the salary of a data scientist is 123,436 and $160,303 respectively per year.

As per Glassdoor, a Big Data specialist earns $49,965 annually whereas a data analyst annually earns $52,126.


In the conclusion part, I would like to conclude by saying that No matter which role you opt for Data Scientists or Big Data specialists the work is playing with a large amount of unstructured data collected from various sources! The process of reusing the collected data is where the work of these Big Terms explained starts. This is all about big data, data science, in the one or other form of a simple comparison i.e. Big Data vs Data Science. There are numerous common tools and languages used among both of them. Also, if you are a big data professional, data scientist, or data analyst. So, If you have relevant skills you can start with any of the particular roles of the common skills and then go to the specialization.

Also, in today’s era of competition, it is not enough to gain specialized skills. Besides, one needs to evaluate your skills if you want to imply them. Certification in the various role is something that helps the user to update the skills, many websites like Henry Harvin offers some certifications and courses that are helpful for your big data career. So, one can start learning anytime and get certified for a bright future!

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A Computer Science Engineer Munchkin Finding her paths Mingling in Marketing field of MBA. Having profound Interest in Writing Techie and Non -Techie stuff. Besides, Love to wander in all Valleys and Alleys too.

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