Big Data vs Data Science: Key Differences and Similarities

Big Data vs Data Science - In this article we will discuss the comparison between Big data and Data Science with a comparison table respectively.

Big Data vs Data Science
Big Data vs Data Science


Data is everywhere, and it is part of our lives more than we realize. The quantity of digital information we build is growing exponentially. According to the statistics, in 2021, there will be 74 zettabytes of generated data, and it is expected to double in 2024. So it is crucial to understand the basics of big data vs data science

As there is not so much difference, big data vs big science has always instigated many people's minds and put them into a dilemma. Today, we will present to you the difference between the two terms in a descriptive way that will help you understand the basic concepts behind them and how they are different from each other. 

Table of contents: 

  1. What is Data Science?
  2. What is Big Data?
  3. Application of Big Data
  4. Applications of Data Science
  5. Comparison between Data Science and Big Data
  6. Similarities between Big Data and Data Science
  7. Conclusion

So, let's start with the fundamentals of these both terms.

Data Science:

Data Science is the study of data, and it is about discovering patterns in data by deep analysis. The procedure of data science includes withdrawal, data transformation, data management, and research to achieve insights about the idea. With data science, the employees can help in the decision-making process which will help the business promote its growth and increase its quality.

Data Science is the most sought-after field in recent times. Data is everywhere. Information is being produced at an increased rate and includes valuable insights that can shape the business.

There are many machine learning and business intelligence tools that support the detection likelihood of the event's outcome. Data Science is a sea of data operations as it develops from multiple sources like math, computer science, statistics, etc. 

By using Data Science, you can work on both unstructured and structured data. Data Science is primarily used in industries like banks, health, financial, and manufacturing. The enterprises are using the data to find the hidden trends that will help them detect suitable solutions to problems. 

Big Data:

Big data is the development, analysis, and management of processing a massive quantity of data. It rotates around the data type – Big Data, which collects an enormous amount of data.

Due to limitations, the amount of data that cannot be processed in the computation technique can now be performed with highly advanced tools and methodologies. 

A few tools for Bi Data are – Spark, Flink, and Apache Hadoop. Big data includes:

  • Structured data – OLTP, transaction information, RDBMS, and other structured data formats.
  • Unstructured data – emails, blogs, digital images, audio, video, social networks, online data sources, sensor data, web pages, and more.
  • Semi-Structured – Text file, XML file, and system log files, etc.

However, all information besides its types and formats can be understood as big data. Extensive data procedure starts with gathering the data from multiple sources. The current big data comes into existence after Google has published its technical paper on MapReduce. It came as a revolution in the data industry. Map Reduce was created into an open-source structure called Hadoop.

Almost every business globally uses big data. The sectors like finance, banking, manufacturing, and healthcare industries have to manage the massive amount of data. 

Applications of Big Data:

  1. Gaming:

Online sources are the huge developers of data; mainly, the gaming industry is a massive creator of big data; a single structure of an online game needs 100MB of data to render. Guess how much data is developed in the gaming industry every day, and it is just uncountable.  

  1. Communications:

Achieve new subscribers, retaining old customers, and expanding the current subscriber base are the top priorities in the telecommunication sector. The solution to the challenge lies in the potential to gather and analyze the vast customer-generated data and machine-produced data, that is created every day. 

  1. Financial Service:

Financial services like credit card companies, insurance firms, venture capitalists, institutional investment banks, retail banks, and private wealth management counselors collect massive quantities of data every single day. To make the data valuable, they use big data to solve everyday problems. Unfortunately, the data are multi-formatted data situated in multiple disparate systems that can be managed only by big data.  

  1. Healthcare:

Big data in the healthcare sector captures more attention, and executives working in the industry find the technology to accelerate the medical processes. Hospitals and medical services save a massive quantity of data from analyzing and performing tasks like tracking and optimizing patient influx, detecting the uses of equipment and medicines, streamlining all the patient information, etc.

Application of Data Science: 

  1. Advertising:

Digital ads have the facility to provide click-through rates that differentiates them from traditional advertisements. So presenting the right ad at the right time and right place is essential in online advertisement campaigns. Digital marketers use the data science algorithm to show banners and digital billboards where it gets maximum viewership. 

  1. Recommendations:

Recommendation systems are increasingly standard in the modern world. We come across many recommendation systems every single day, and it is excellent. Even if we look for more content, an online recommendation system suggests what we like. It is used as a marketing approach to advertise products to users.  

  1. Internet Search:

Since the internet is the prophet of the virtual society, we search for everything online. Fortunately, we get applicable content maximum of the time. Data technology is being implemented in online search engines to make us get the results we expect. It goes through our previous browsing records and filters the results according to our ordinary search.

Comparison between Data Science and Big Data:


Data Science

Big Data


  • A data-focused scientific method.
  • Methods to process big data
  • Similar to data mining.
  • Tackle the ability of big data for enterprise decisions.

  • Large quantities of data that can't be managed using traditional database programming.
  • Identifies by variety, volume and velocity.

Application Areas

  • Search recommenders
  • Web development
  • Image/ Speech praise
  • Other miscellaneous areas
  • Internet Search
  • Digital advertisements
  • Fraud/ Risk detection.

  • Performance Optimization
  • Financial Services 
  • Streamline business process
  • Health and Sports
  • Research & Development
  • Improving Commerce
  • Telecommunications
  • Streamline business process.


  • An expertise area that involves scientific programming models, tools and methods to process big data.
  • Help businesses in the decision-making process.
  • Provides the methods to extract data and information from big datasets.

  • Include all kinds and structures of data.
  • Various data is produced from multiple sources.


  • Internet users/ traffic, data generated forms system logs and live feeds. 
  • Data filtration, preparation and analysis.


  • State–of–the–art techniques for information mining.
  • Data prediction and visualization.
  • Add major use of math, statics and other tools. 
  • Programming Skills (SQL, NoSQL) Hadoop portals.
  • Data acquisition, processing, publishing and preparation.

  • Achieve competitiveness
  • Understand market conditions & gain new customers.
  • Develop new business agility.
  • Strengthen database for business advantage.
  • Gain sustainability 
  • Set achievable metrics and ROI.

Similarities between Big data & Data Science:

As stated earlier, Data Science is the ocean of data operations. These data operations include Big Data. Data Science is like a vast set that also includes Big Data as a sub-set along with other crucial data operations. Both are managed with data.

Furthermore, Data Science is required to manage big data, which is frequently unstructured. 

To manage data, data science must possess the skills. If you are an expert at Hadoop or any big data technology, it adds a great advantage to your profile. Further, it will also enhance your value in the market and provide a competitive edge over others.


At present, the line between big data and data science has become less. Because recently, Big Data portals like Spark and Flink have a data analytics engine as part of its structure.

Even the older platform like Hadoop has launched Mahout, the information-analytical engine comprising system learning algorithms. This makes the Big Data platform complete and inclusive of all the information science tools.


At the end of the object Big Data vs Data Science, we conclude that while Big Data and Data Science may also share a common frontier of dealing with data, they're different. We learned about those phrases and the tools which are used to carry out respective operations.

Author: I am Anita Basa, an enthusiastic Digital Marketer and content writer working at I wrote articles on trending IT-related topics such as Microsoft Dynamics CRM, Oracle, Salesforce, Cloud Technologies, Business Tools, and Software.
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