Introduction

Data is all around us and growing exponentially without limit. Big Data refers to the heterogeneous mass of digital information produced by individuals or organizations whose characteristics require specific and increasingly advanced computer storage and analytics tools. This data can't be managed, processed, and studied using traditional methods. It has a primary characteristic called as “V's of Big Data”.

Volume

Volume refers to the size of data. It is necessary in determining whether a dataset can be called Big Data or not. A dataset, hereinafter mentioned as Big Data, is a data collection with quantity of more than 1 TB. These datasets contain a huge amount of data that are larger than traditional datasets, which requires more consideration of each stage of the processing and storage life cycle. It can be generated from diverse sources, such as social media, IoT devices, videos, customer logs, or financial transactions.

Variety

Variety refers to the type of data. The formats of data can vary significantly from different sources. Traditional data is limited in certain types, such as spreadsheets and databases. While Big Data systems are usually present in richer media like images, audio recordings, video files, texts, PDFs, and structured logs. This kind of data is crucial for its storage and analysis.

Value

Value refers to the importance of data. It simply represents the business value to be derived from Big Data. No matter how fast the speed and how large the size is, it has to be reliable and useful. Otherwise, the data is not valuable for processing and analysis.

Veracity

Veracity refers to the quality of data. It defines the degree of trustworthiness of the captured data for analysis. As most of the data is unstructured, it is not able to filter out unnecessary information and use the important data for processing needs.

5 Big Data Use Case