Difference Between Data Science and Big Data

When we say Big Data, we are talking about the humungous volumes of non-totaled crude information with its size fluctuating up to petabytes. Big data refers to the huge volumes of data of various types, i.e., structured, semi-structured, and unstructured.

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Coming back to the current topic, data can be of various types, i.e., structured, semi-structured, and unstructured.

Unstructured data – social networks, emails, blogs, tweets, digital images, digital audio/video feeds, online data sources, mobile data, sensor data, web pages, and so on.
Semi-structured – XML files, system log files, text files, etc.
Structured data – RDBMS (databases), OLTP, transaction data, and other structured data formats.

When data sets get so big that they cannot be analysed by traditional data processing application tools, it becomes Big Data. With an ever increasing number of information getting created each millisecond from different sources, information does not come in standard shape yet has been delivered in assortments of frame. Truth be told, 80% of the information produced today are unstructured, and it is profoundly difficult to deal with them productively just by utilizing customary advances.

Earlier, the measure of information produced was not high and we continued chronicling them, and was playing out a just recorded investigation. Be that as it may, one vital thing to remember is that – Huge Data is something that is essential be to examined for with the goal that we can infer at helpful bits of knowledge to improve and vital business moves. That massive amount of data is useless if it is not analyzed and processed. The biggest the companies have currently is the amount of the data they hold, as it can turn their business into something they could not have done without that data.

The World’s driving worldwide research and warning organization, Gartner is characterizing Big Data as – High-volume, and high-speed or potentially high assortment data resources that require types of data preparing that are practical and inventive and can permit upgraded basic leadership, understanding and process robotization.

Data Science is a field including all which is related with organized and unstructured information, starting from planning, purging, breaking down and inferring valuable bits of knowledge. Data Science is a blend of science, measurements, catching information, programming, information scouring, planning, information arrangement.
Data Science is the mixture of a few systems and process to increase the proficient business bits of knowledge. By utilizing the techniques, calculations, procedures, and frameworks to adequately extricate data which can be utilized by business to settle on critical business choices.

Organizations need big data to improve efficiencies, understand new markets, and enhance competitiveness whereas data science provides the methods or mechanisms to understand and utilize the potential of big data in a timely manner. Organizations have no limit to the amount of valuable data that can be collected, but to use all this data to extract meaningful information for organizational decisions, data science comes to play. Big data relates more to technology (Hadoop, Java, Hive, etc.), distributed computing, and analytics tools and software. This is opposed to data science which focuses on strategies for business decisions, data dissemination using mathematics, statistics and data structures and methods mentioned earlier.