Before we deep dive into the introduction to Big Data, first need to know something about data.
Data is a real time object or entity.
Data is a set of qualitative or quantitative variables – it is a set of information/value.
The main examples of data are weights, prices, costs, numbers of items sold, employee names, product names, addresses, tax codes, registration marks etc.
Now, let’s come to our main topic Big Data…
What is Big Data?
Big Data is a collection of data that is huge in volume, yet growing exponentially with time.
It is data with so large size and complexity that none of the traditional data management tools can store it or process it efficiently.
Big data is also data but it’s in huge size.
Examples Of Big Data
The New York Stock Exchange is an example of Big Data that generates about one terabyte of new trade data per day.
Statistic shows that 500+terabytes of new data uploaded into the databases of social media site Facebook, every day.
A single Jet engine can
generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, the generation of data reaches up to many Petabytes.
Types Of Big Data
1. Structured Data
Structured data is the easiest to work with. It is highly organized with dimensions defined by set parameters.
Examples of structured data is database.
2. Unstructured Data
Not all data is as neatly packed and sorted with instructions on how to use it as structured data is. The consensus is no more than 20% of all data is structured.
Example of unstructured data is images, audio, videos etc.
3. Semi-Structured Data
It is very difficult to categorize semi-structured data because sometimes they looks structured or sometimes unstructured.
Examples are XML, JSON documents, spread sheet etc.
(i) Volume – The name Big Data itself is related to a size that is enormous. The size of data plays a very crucial role in determining the value out of data. Also, whether a particular data can actually be considered as Big Data or not, is dependent upon the volume of data.
(ii) Variety – The next aspect of Big Data is its variety.
Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining, and analyzing data.
(iii) Velocity – The term ‘velocity’ refers to the speed of the generation of data.
Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous.
(iv) Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
- Big Data definition : Big Data meaning a data that is huge in size.
- The Big Data analytics examples includes stock exchanges, social media sites, jet engines, etc.
- Improved customer service, better operational efficiency, Better Decision Making are few advantages of Big Data