What is Big Data?
Big data is also data but with a huge size. Big data is a term used to describe a collection of data that is vast in volume and is growing rapidly over time. In short this kind of data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently.
examples of big data
Social media :-
Statistics show that 500+ terabytes of new data are added to the social media site Facebook's database every day. This data is mainly generated in the context of photo and video uploads, message exchange, comments etc.
Jet Engine :-
A single jet engine can generate 10+ terabytes of data in 30 minutes of flight time. With several thousand flights per day, the generation of data reaches several petabytes.
New York Stock Exchange:-
The New York Stock Exchange generates one terabyte of new trading data per day.
Types of Big Data can be found in three forms
Structured
Unstructured
Semi-structured
Structured :-
Any data that can be stored, accessed and processed in a fixed format is called "structured" data. Over time, talent in computer science has achieved more and more success in developing techniques for working with this kind of data (where the format is already well known) and is gaining value outside of it. Is. However, nowadays, we are foreseeing issues when the size of such data grows enormously, with typical sizes being in the range of several zettabytes.
Unstructured
Any data with unknown form or structure is classified as unstructured data. Apart from being huge in size, unstructured data poses many challenges in terms of its processing, from which it derives its value. A typical example of unstructured data is a heterogeneous data source consisting of a combination of simple text files, images, videos, etc. Organizations now have a wealth of data available to them, but unfortunately, they don't know how to get the value out of it. This data is in its raw form or unstructured format.
Semi-structured
Semi-structured data can have both forms of data. We can see semi-structured data as a structured one but it is not really defined with examples. A table definition in relational DBMS. An example of semi-structured data is the data represented in an XML file.
Characteristics of Big Data
Volume:-
The name big data itself refers to a size that is very large. The size of the data plays a very important role in determining the value out of the data. Also, whether a particular data can really be considered as big data or not is dependent on the amount of data. Therefore, volume is a feature that needs to be considered while dealing with big data.
Variety:-
The next aspect of big data is its diversity. Diversity refers to the heterogeneous sources and nature of data, both structured and unstructured. During the earlier days, spreadsheets and databases were the only sources of data considered by most applications. Nowadays, data in the form of data, email, photo, video, monitoring device, PDF, audio etc. is also being considered in analysis applications. This diversity of unstructured data presents some issues for the storage, mining, and analysis of data.
Velocity:-
The term Velocity refers to the speed of data generation. How fast the data is generated and processed to meet the demands determines the actual potential in the data.
Big data velocity refers to the speed at which data flows through business processes, application logs, network and social media sites, sensors, mobile devices, etc. The flow of data is massive and continuous.
Variability:-
It refers to the inconsistency that can be shown by the data multiple times, thus hindering the process of being able to handle and manage the data effectively.
Benefits of Big Data Processing
The ability to process Big Data brings in many benefits, such as:-
Can use outside intelligence while making business decisions.
Access to social data from search engines and sites like Facebook, Twitter enables organizations to fine-tune their business strategies.
better customer service
Traditional customer feedback systems are being replaced by new systems designed with big data technologies. In these new systems, big data and natural language processing technologies are being used to read and evaluate consumer responses.
Early identification of risk, if any, for the product/services
better operating efficiency
Big data techniques can be used to create a staging area or landing zone for new data to determine how the data should be moved to a data warehouse. Moreover, such integration of big data technologies and data warehouses helps an organization to load frequently accessed data.
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