Big Data vs Data Science – Know What’s Trending in 2019?

Big data and data science, you must have often heard these terms together but today you will see their major differences that is Big Data vs Data Science. While both of these subjects deal with data, their actual usage and operations differ. Along with their differences, we will see how they both are similar. We will also observe how big data forms a part of the major data science ecosystem.

Big data vs data science

So, let’s start with the basic question – What is Data Science?

What is Data Science?

Data Science is the study of data. It is about finding patterns in data through an in-depth analysis. The process of Data Science involves the extraction, data transformation, data analysis and prediction to gain insights about the data. With Data Science, employees can assist in the decision-making process which will help the business to grow and enhance the quality of the product.

Data Science is the most sought after field today. Data is everywhere. It is being generated at an exponential rate and contains within insights that can shape the course of businesses. There are several machine learning and business intelligence tools that help to find the likelihood of the outcome of the event. Data Science is like a sea of data operations. It stems from multiple disciplines like statistics, math and computer science.

Steps in data science

Using Data Science, you can work on both unstructured and structured data. Data Science is heavily being used in industries like finance, banking, health, and manufacturing. Industries are leveraging data to find the hidden patterns that will help them to find appropriate solutions to problems.

Do you know Why Banks are Using Data Science?

What is Big Data?

Big Data is the extraction, analysis and management of processing a large volume of data. It revolves around the datatype – Big Data which is a collection of a colossal amount of data. Such amount of data, which could not be processed earlier due to limitations in the computational techniques can now be performed with highly advanced tools and methodologies.

Some of the tools for Big Data are – Apache Hadoop, Spark, Flink etc. Big Data contains a pool of data that can be both structured and unstructured. By structured data, we mean the data that mobile devices, services, and websites generate. The unstructured data is more of an organized data that is the users generate themselves. For example, emails, chats, telephone conversations, reviews etc.

The contemporary Big Data came into existence after Google published its technical paper on MapReduce. This brought about a revolution in the data community. MapReduce was developed into an open-source framework called Hadoop. Later on, Apache released Spark that mitigated the shortcomings of the MapReduce paradigms. Almost every industry in the world today makes use of Big Data. Industries like finance, healthcare, banking, manufacturing have to deal with surplus amounts of data. In order to manage data of the millions of customers, companies have adopted the Big Data approach.

Difference Between Big Data and Data Science

After understanding the terms Big Data and Data Science, now let’s check the most trending difference that is Big Data vs Data Science. While Big Data and Data Science both deal with data, their method of dealing with data is different.

  • Big Data deals with handling and managing huge amount of data. Prior to Big Data, industries did not possess the required tools and resources to manage such a large volume of data. However, the emergence of MapReduce and Hadoop made it easier for them to handle this form of data. Data Science, on the other hand, is the scientific analysis of data. It is more quantitative in nature and uses various statistical approaches to find insights within the data.
  • While Big Data is about storing data, Data Science is about analyzing it. However, it is to be kept in mind that Data Science is an ocean of data operations, one that also includes Big Data. A Data Scientist analyzes the data that is quite large and requires a big data platform. Therefore, an ideal data scientist must also possess the knowledge of big data tools.
  • Furthermore, Big Data is limited only to the storage and management of data. However, recently, more components like PIG and HIVE have been added to the Hadoop framework in order to facilitate the analysis of big data. Furthermore, newer frameworks like Spark have analytical features that are intrinsic to it.
  • The roles of Data Scientist and Big Data specialist also differ. A Data Scientist is required to analyze, draw insights from the data, visualize the data and communicate the results through robust storytelling. A Big Data Specialist, on the other hand, develops, maintains and administers Big Data clusters that hold the voluminous amount of data.

Similarities Between Big Data & Data Science

As mentioned above, Data Science is the ocean of data operations. These data operations also include Big Data. Data Science is like a bigger set that also contains Big Data as its sub-set along with other important data operations. Both of these fields deal with data. Furthermore, a data scientist is required to handle big data which is frequently unstructured in nature.

Big data and Data science

In order to handle such type of data, a data scientist must possess the skills. If you are skilled at Hadoop or any other Big Data technology, it will add a great bonus to your profile. Furthermore, it will also increase your value in the market and give you a competitive edge over others.

Perfect Time to Upgrade your Skills – Learn Big Data with Industry Veterans

Recently, the line between Big Data and Data Science has been becoming lesser. This is because recent Big Data platforms like Spark and Flink have data analytical engine as part of their framework. Even the older platform like Hadoop has released Mahout, which is the data analytical engine comprising of machine learning algorithms. This makes the Big Data platform comprehensive and inclusive of all the data science tools.

Summary

In the end of the article Big Data vs Data Science, we conclude that while Big Data and Data Science may share a common frontier of dealing with data, they are completely different. We learnt about these two terms and the tools that are used to perform respective operations. We also overviewed how Data Science is a bigger set that comprises of Big Data as its subpart. Furthermore, we learnt how newer Big Data platforms are utilizing analytical tools.

Still any doubt? Drop your query in the comment our expert will respond you soon.

3 Responses

  1. Ashok Kumar Singh says:

    This is extremely helpful in understanding big data.

  2. Shiva Panchal says:

    HI
    I am an ETL developer mainly working with Informatica having 5+ year of experience. I am looking for technical skills change as market is changing day by day. For an ETL developer which stream should I opt, Big data or Data Science? If possible please elaborate with tools as well.

    Thanks in Advance

    • DataFlair Team says:

      Hey Shiva,
      Your ETL background will definitely benefit you when transitioning to the field of Big Data or Data Science. Firstly, you must know that Data Science is an ocean of data operations which also involves Big Data. A Data Scientist has to deal with colossal amount of structured as well as unstructured data. This data is best handled with tools of Big Data like Hadoop, Spark, Flink etc. Moreover, big data tools like these provide data analysis and machine learning features, providing a comprehensive package for Data Science. In order to become a Big Data Engineer, you will require knowledge of a programming language like Java, Python or Scala and proficiency in any or multiple big data tools that were mentioned before. For becoming a data scientist, you will not only require knowledge of big data but also statistical knowledge and proficiency in languages like R and Python.
      If you want to make your career in Big data, then DataFlair has an excellent course on Big Data Technologies.

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