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Big Data Write for Us, Guest Post, Contribute, Submit Post

Big Data Write for Us, Guest Post, Contribute, Submit Post

Big Data Write for Us


Big data refers to data sets that are too large or complex for traditional data-processing application software to handle. Data with a large number of fields (rows) has more statistical power, whereas data with more complexity (more attributes or columns) may have a higher false discovery rate. The challenges of big data analysis include data capture, data storage, data analysis, search, sharing, transfer, visualisation, querying, updating, information privacy, and data source. Initially, big data was associated with three key concepts: volume, variety, and velocity.

Characteristics Of Big Data

Big data can be described by the following characteristics:


The term “Big Data” refers to a massive amount of information. The size of the data is very important in determining the value of the data. Furthermore, whether a particular data set can be considered Big Data or not is determined by the volume of data. As a result, ‘Volume’ is one characteristic that must be considered when dealing with Big Data solutions.


Variety refers to a wide range of data sources and data types, both structured and unstructured. Previously, spreadsheets and databases were the only data sources considered by most applications. Data in the form of emails, photos, videos, monitoring devices, PDFs, audio, and so on are now considered in analysis applications. This variety of unstructured data raises concerns about data storage, mining, and analysis.


The term’velocity’ refers to the rate at which data is generated. The true potential of the data is determined by how quickly it is generated and processed to meet the demands.

Big Data Velocity is concerned with the rate at which data flows in from various sources such as business processes, application logs, networks, social media sites, sensors, mobile devices, and so on. The data flow is massive and continuous.


This refers to the inconsistency that data can exhibit at times, impeding the process of effectively handling and managing data.

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