This is a big data era, where every moment of life is calculated and is store in some or other manner. Scientists, who continuously work for the future predictions, advanced computing and other growing technologies continuously require a quick look over the data of past several years, and so these studies and requirements are giving a blast of data which is crucial to maintain immortally. The major challenge is to store and maintain this giant data with a flexibility to visualize the data in static or dynamic form. In previous decades people did had data and data management tools too, but now the volume of data has grown exponentially. Traditional data management tools are not really effective and even efficient to combat the current scenario.
Let’s first define the attributes of big data which evolves major challenges:
Volume:
Big data, by name itself it justifies the definition that means a huge sized data that is generated at a period of single day. Now there is nothing like important data and casual one, each data generated in per day time is been stored and processed and this is one of the major challenges faced by big data management.
Velocity:
There are certain channels for data transmission and the velocity and frequency with which s being transferred through the channels generates a heavy congestion in the channels making it challenging to maintain.
Variety:
During past era, there was certainty for the formats of data to be stored, as data were well defined following the data schema. With the technological expansion it also gave an expanded world for data with a huge variety of data to be generated and hence also increased the requirement of data maintenance, processing and transmission.
Value:
The value of data keeps varying with every data generation. It is the biggest challenge for the big data management to evaluate every single data before processing and transmitting it. This is crucial so as to segregate the data as per its value which makes the data mining process easier and faster too.
