Big Data vs Data Science

Big data and data science, you must have often heard these terms together but today you will see their major differences that are Big Data vs Data Science. While both of those subjects affect 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 section of the main data science ecosystem.

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

What is Data Science?

Data Science is the study of data. It is about locating patterns in data through an in-depth analysis. The process of data Science involves the extraction, data transformation, data analysis, and prediction to realize insights about the info. With Data Science, employees can assist within the decision-making process which can help the business to grow and enhance the standard of the merchandise.



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. 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.

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.

What is Big Data?

Big Data is the extraction, analysis, and management of processing a huge volume of data. It revolves around the datatype – Big Data which may be a collection of a huge 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 info that mobile devices, services, and websites generate. The unstructured data is more of an organized data that’s 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. To manage data of many 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 foremost trending difference that’s Big Data vs Data Science. While Big Data and Data Science both affect data, their method of handling data is different.

• Big Data deals with handling and managing large amounts of data. Before Big Data, industries didn’t possess the specified tools and resources to manage such an outsized volume of knowledge. 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 and uses various statistical approaches to find insights within the data.

• While Big Data is about storing data, Data Science is about evaluating 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, a perfect 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 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 varies. 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 sort of a bigger set that also contains Big Data as its sub-set alongside other important data operations. Both of these fields deal with data. Furthermore, a data scientist is required to handle big data which is usually unstructured in nature.

Big Data vs Data Science

To handle such sort of data, a data scientist must possess the talents. 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.

Recently, the road between Big Data and Data Science has been becoming lesser. This is because recent Big Data platforms like Spark and Flink have data analytical engines as a 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 info science tools.

Summary

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