{"id":55114,"date":"2019-04-25T16:52:20","date_gmt":"2019-04-25T11:22:20","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=55114"},"modified":"2025-07-31T21:38:31","modified_gmt":"2025-07-31T16:08:31","slug":"big-data-vs-data-science","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/big-data-vs-data-science\/","title":{"rendered":"Big Data vs Data Science \u2013 Know What\u2019s Trending in 2025?"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1574,&quot;href&quot;:&quot;https:\\\/\\\/techvidvan.com\\\/courses\\\/ai-data-science-course-hindi&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250521215628\\\/https:\\\/\\\/techvidvan.com\\\/courses\\\/ai-data-science-course-hindi\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 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23:50:27&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-04-23 02:36:13&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-04-26 04:51:37&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-04-29 09:23:15&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-02 09:23:23&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-05 09:34:39&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-08 14:15:43&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-11 22:07:14&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-14 22:20:22&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-18 04:10:28&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-21 09:10:39&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-24 10:54:17&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-05-28 02:56:02&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-06-01 10:19:32&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-06-04 19:05:06&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-06-07 19:47:05&quot;,&quot;http_code&quot;:403},{&quot;date&quot;:&quot;2026-06-11 01:06:12&quot;,&quot;http_code&quot;:403}],&quot;broken&quot;:true,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-11 01:06:12&quot;,&quot;http_code&quot;:403},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p>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. <span style=\"font-weight: 400\">While both of these subjects deal with data, their actual usage and operations differ. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Along with their differences, we will see how they both are similar.\u00a0<\/span>We will also observe how big data forms a part of the major data science ecosystem.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-55155\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science.jpg\" alt=\"Big data vs data science\" width=\"803\" height=\"421\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science.jpg 803w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science-768x403.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/Big-data-vs-data-science-520x273.jpg 520w\" sizes=\"auto, (max-width: 803px) 100vw, 803px\" \/><\/a><\/p>\n<p>So, let&#8217;s start with the basic question &#8211; What is Data Science?<\/p>\n<h3>What is Data Science?<\/h3>\n<p><em>Data Science is the study of data. It is about finding patterns in data through an in-depth analysis.<\/em> The process of Data Science involves the extraction, data transformation, data analysis and prediction to gain insights about the data.<\/p>\n<p>With Data Science, employees can assist in the <strong>decision-making process<\/strong> which will help the business to grow and enhance the quality of the product.<\/p>\n<p>Data Science is the most sought-after field today. Data is everywhere. It is being generated at an exponential rate and contains insights that can shape the course of businesses.<\/p>\n<p>There are several machine learning and\u00a0<strong>business intelligence<\/strong> <strong>tools<\/strong> 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.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-54758\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps.jpg\" alt=\"Steps in data science\" width=\"802\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps.jpg 802w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/data-science-steps-520x272.jpg 520w\" sizes=\"auto, (max-width: 802px) 100vw, 802px\" \/><\/a><\/p>\n<p>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.<\/p>\n<p><strong>Some fascinating Data science statistics:<\/strong><\/p>\n<p>The Rise of Data Scientists: Jeff Hammerbacher, who oversaw Facebook&#8217;s data team at the time, invented the phrase &#8220;Data Scientist&#8221; in 2008. Since then, there has been an exponential increase in need for data scientists.<\/p>\n<p>Data volumes and velocity have increased dramatically as a result of the spread of digital technology. 2.5 quintillion bytes of data is produced every day, and this number is only rising.<\/p>\n<p>The Power of Predictive Analytics: Predictive analytics is made possible by data science, which helps organisations to estimate future trends, consumer behaviour, and market dynamics, which improves strategy and decision-making.<\/p>\n<p>Do you want to become one of the highest demanding data scientist in 2025? Do check the<a href=\"https:\/\/techvidvan.com\/courses\/ai-data-science-course-hindi\/\"> Best Data Science online course<\/a> being crafted by industry veterans to help you land your dream job as others have done.<\/p>\n<h3>What is Big Data?<\/h3>\n<p>Big Data is the extraction, analysis and management of processing a large volume of data. It revolves around the datatype &#8211; Big Data which is a collection of a colossal amount of data. 5 Vs that define big data are velocity, volume, value, variety and veracity.<\/p>\n<p>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.<\/p>\n<p>Some of the <strong>tools for Big Data<\/strong> are &#8211; 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.<\/p>\n<p>The unstructured data is more organized data that is the users generate themselves. <strong>For example<\/strong>, emails, chats, telephone conversations, reviews, etc.<\/p>\n<p>The contemporary Big Data came into existence after Google published its technical paper on <strong>MapReduce<\/strong>. This brought about a revolution in the data community. MapReduce was developed into an open-source framework called Hadoop.<\/p>\n<p>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.<\/p>\n<p>In order to manage data of the millions of customers, companies have adopted the Big Data approach.<\/p>\n<p><strong>Some fascinating Big Data statistics:<\/strong><\/p>\n<p>An astounding 90% of the world&#8217;s data is thought to have been produced in the previous two years alone. The digital revolution, social media, Internet of Things (IoT) devices, and other factors have contributed to the exponential increase of data.<\/p>\n<p>Immense Data Production: Millions of emails are written every minute, hundreds of thousands of tweets are sent, and millions of Google searches are made, all of which add to the immense data production that is happening right now.<\/p>\n<p>Data Storage in Exabytes: It is anticipated that by 2027, there will be 163 zettabytes of digital data in existence. One zettabyte is equal to one billion terabytes, or one trillion gigabytes, to put this into context.<\/p>\n<h3>Difference Between Big Data and Data Science<\/h3>\n<p>After understanding the terms Big Data and Data Science, now let&#8217;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.<\/p>\n<p><em>1. Big Data deals with <strong>handling and managing huge amount of data<\/strong><\/em>. 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<strong> <em>scientific analysis of data<\/em><\/strong>. It is more quantitative in nature and uses various statistical approaches to find insights within the data.<\/p>\n<p>2. While Big Data is about <strong>storing data<\/strong>, Data Science is about <strong>analyzing it<\/strong>. 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 knowledge of big data tools.<\/p>\n<p>3. Furthermore, Big Data is limited only to the <strong>storage and management of data<\/strong>. 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 <strong>Spark<\/strong> have analytical features that are intrinsic to it.<\/p>\n<p>4. The roles of Data Scientists and Big Data specialists also differ. A Data Scientist is required to <strong>analyze, draw insights from the data<\/strong>, visualize the data and communicate the results through robust storytelling. A Big Data Specialist, on the other hand, <strong>develops, maintains, and administers Big Data clusters<\/strong> that hold a voluminous amount of data.<\/p>\n<h3>Similarities Between Big Data &amp; Data Science<\/h3>\n<p>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.<\/p>\n<p>Furthermore, a data scientist is required to handle big data which is frequently unstructured in nature.<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-55185\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science.png\" alt=\"Big data and Data science\" width=\"500\" height=\"500\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science.png 500w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science-150x150.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science-300x300.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science-160x160.png 160w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2019\/04\/similarities-in-big-data-and-data-science-320x320.png 320w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/a><\/p>\n<p>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.<\/p>\n<p>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.<\/p>\n<p>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.<\/p>\n<h3>Summary<\/h3>\n<p>Big Data means very large sets of data that are too big for normal tools to handle. It includes data from websites, mobile apps, sensors, and social media. Big Data is all about how to store, process, and manage this data. It uses tools like Hadoop, Spark, and NoSQL databases. The focus is on handling data volume, speed, and variety.<\/p>\n<p>Data Science uses this Big Data to find useful patterns and predictions. It is more focused on analysis, statistics, and machine learning. While Big Data provides the raw information, Data Science turns it into knowledge and decisions. For example, Big Data collects user clicks on a website, and Data Science predicts which product a user will buy next.<\/p>\n<p>So, Big Data is the fuel, and Data Science is the engine. Without Big Data, Data Science would have little to work on. And without Data Science, Big Data would just be piles of information with no meaning. Together, they create strong solutions for business, healthcare, finance, and more.<\/p>\n<p>So in this tutorial, we have seen how big data and data science differ from each other though they may have some similarities as well. Both are in huge demand and will last for lifetime now due to increasing size of data everyday.<\/p>\n<p>We also overviewed how Data Science is a bigger set that comprises Big Data as its subpart. Furthermore, we learned how newer Big Data platforms are utilizing analytical tools.<\/p>\n<p>Still any doubt? Drop your query in the comment our expert will respond to you soon.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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