Big data is the large volume of data both structured and unstructured that inundates a business on a day-to-day basis and used to describe a collection of data that is huge size and growing exponentially with time. Big Data is so large and complex that none of the traditional data management tools are able to store it and process it efficiently. Big data can be analyzed for insights that lead to be better decisions and strategic business moves. The intention of it is too large or complex to be dealt with traditional data-processing application software.

Big Data challenges include the data storage, data analysis, capturing data, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was initially associated with three key concepts: volume, variety and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional usual software to process within an acceptable time and value.

Big Data also handles a wide variety of data types, including structured data in SQL databases and data warehouses, unstructured data, such as text and document files. For example, big data analytics project may attempt to gauge a product’s success and future sales by correlating past sales data, return data and online buyer review data for that product.

Big data is often characterized by the 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. More recently, several other Vs have been added to descriptions of big data, including veracity, value and variability. Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three V’s. They are following below:

Volume: The amount of data matters. It can be data of unknown value, such as Twitter data feeds, click streams on a webpage or a mobile app or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

Velocity: The amount of data matters. It can be data of unknown value, such as Twitter data feeds, click streams on a webpage or a mobile app or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.

Variety: Variety refers to the many types of data are available. Traditional data types structured and fit neatly in a relational database. Unstructured and semi structured data types, audio and video, such as text, require additional preprocessing to derive meaning and support metadata.

Data sets grow rapidly, in part because they are increasingly gathered by cheap and numerous information-sensing Internet of Things devices such as mobile devices , cameras, aerial (remote sensing), microphones, software logs radio-frequency identification (RFID) readers and wireless sensor networks.

Current usage of the big data tends to refer the use of predictive analytics, user behavior analytics or certain other advanced data analytics methods that extract value from data. The quantity of data now available is indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, practitioners of medicine, business executives, advertising and governments alike regularly meet difficulties.

It’s what organizations do with the data that matters. These data sets are so large that traditional data processing software just can’t manage them. These massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.