Big Data, Data Science, and Data Analytics are three distinct concepts of Computer Science. If someone is not from the IT industry then it becomes quite difficult for them to understand it. This is the reason why these terms are used interchangeably by numerous people around the world. After studying it in detail, one gets to know that these three terms are completely different concepts, performing diverse but significant jobs. With the progressive increase in unstructured data, paths have opened up in the big data industry which includes data science and data analytics.
Big Data is a booming concept in the technological world and it is currently visible everywhere. With this introduction of big data, the other terms data science and data analytics come into the picture. The average salary of these sectors are:
Data Scientist: $125,000, Big Data Professional: $97,000, Data Analyst: $66,000.
With some research, here is a simplistic representation of these terms so that you can understand the gist of the data industry. Before going into detail, let us see the basics of big data as the concept of data science and data analytics revolves around it only.
The Basic Concept of Big Data, and The Importance of It
We do know that data is the collection of facts and bits of information. Big data is also a relatable data term but with a huge size, growing exponentially with time. The analysis of large data sets reveals patterns, trends, and associations, complex to be stored or processed using traditional data processing tools. The big chunks of data visible in real-time explain the concept of big data. Some of the examples of it include:
- New York Stock Exchange: Generates one terabyte of new trade data per day.
- Facebook: 500+ terabytes of new data gets ingested with the use of photos, videos, comments, and messages.
- Jet Engine: 30 minutes of flight time of a single jet engine can generate 10+ terabytes of data. The data reaches up to many Petabytes with the involvement of thousands of flights per day.
Even though it is the gist of big data, clarity is achievable after delving into its avenues. Here is a brief descriptive approach to understanding the nuances of big data, data science, and data analytics.
Big Data vs Data Science vs Data Analytics
|What is it?||What does it do?||Where do we use it?||What are the required skills?|
It refers to humongous volumes of data, which include:
|It is used for analyzing the system bottlenecks, building large-scale data processing systems, and for the architecture of a highly scalable distributed system.||Financial Services, Communications, Retail, Operational Analysis||
Programming languages like JAVA, Scala.
NoSQL databases like MongoDB Cassandra DB
Frameworks like Apache Hadoop
Excellent grasp of distributed systems
|Data Science||Identifies patterns by mixing a large amount of structured and unstructured data which includes a combination of programming, statistical skills, machine learning, and algorithms.||It does the future prediction based on past patterns by exploring and examining data from multiple disconnected sources. It helps in developing new analytical methods and machine learning models.||Search Engines, Financial Services, E-Commerce, Web Development, Internet Search||
Programming skills like SAS, R, Python
Statistical and mathematical skills
Storytelling and data visualization
|Data Analytics||It is basically the processing and performing of statistical analysis of the available data. Data Analytics is used for discovering how data can be used to draw conclusions and solve problems.||In this, the data is acquired, processed and summarized. The data is packaged for insights, and the reports are designed and created using various reporting tools.||Healthcare, Travel, IT Industry, Traveling and transportation||
Programming skills like SAS, R, and Python
Statistical and mathematical skills
Data wrangling skills
Data visualization skills
That is all you need to know to understand the concept of big data, data science, data analytics and the difference between the three. After reading this article, we hope none of you use the terms interchangeably.