Big Data Vs Data Science- What Is The Difference?
Tags: bigdata, datascience
Published: 2020-10-10 11:54:39

Data is everywhere. The amount of digital data that exists is rapidly increasing, doubling every two years, and changing the manner in which we live. Information is all over the place. Till the year 2020,about1,7 megabytes of new information will be generated every second for every human being. 

Here we will differentiate, big data and data science with various parameters. Before we start Big data vs Data science, let us see each one in detail.

#What Is Big Data?
Big data is a humongous volume of data which cannot be effectively processed with the traditional apps that exist. The processing of Big Data starts with the raw data that isn’t aggregated and is generally difficult to store in the memory of a single computer. A popular expression that is utilized to describe massive volumes of data, both unstructured and structured, Big Data inundated a business on an everyday premise. Big Data is something that can be used to examine insights that can prompt better decisions and strategic business moves. By the definition- Big data is high-volume and high velocity or  high variety information asset that demand cost-effective, innovative forms of information processing that allow enhanced insights, decision making and process automation.

#What Is Data Science?
Managing unstructured and structured data, Data Science is a field that involves all that is related to data cleansing, preparation, and analysis. Data science is a combination of programming, problem-solving, statistics, mathematics, the ability to look at things in a different way, getting data in ingenious ways, data cleaning, aligning and preparing. In simple words, it is an umbrella of techniques that use to extract insights and information from data.

#Big Data Vs Data Science- What Is The Difference?
**1. Perception-**
Generally, big data is generated from multiple data sources and so it can be called a collective dataset. As the data set is made with data from multiple sources, each data type and data format is possible to add in big data. Big data can be Structured or unstructured or semi-structured datasets. Basically, a company or organization creates real time that insures the current status of an event and encourages them to work in a way to achieve the goal.

Data science includes multiple tools and techniques to analyse the dataset. Main goal of data science is to simplify the complexity of big data. Basically it is a concept made to reduce the difficulties in taking decisions for an organization. Considering big data vs data science, big data are unstructured and need to be simplified, whereas data science is a quick solution to it.

**2. Platforms-**
Big data is produced from each conceivable history that can be made in an event. The operation of producing data is started on platforms like DOMO, Hortonworks, Cloudera, Microsoft Machine Learning Server, Vertica, Kofax insight, AgileOne and so on. 

Data science works for the improvement of an organization through data analysis, process, preparation, and so on. Knowing the use and importance of data science, scientists started to work on it for the creation of detailed and accurate data science platform. After some attempts, many platforms are created and those are MATLAB, TIBCO statistica, Anaconda, H2O, R-Studio, Databricks Unified Analytics platforms and so on.

**3. Tools-**

Know more at- [https://solaceinfotech.com/blog/big-data-vs-data-science-what-is-the-difference/]