What is Big Data?
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. The term “big data” refers to data that is so large, fast, or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s:
The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not.
The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video, plus it completes missing pieces through data fusion.
The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data are produced more continually.
Types of Big Data
Any data that can be stored, accessed and processed in the form of fixed format is termed as a 'structured' data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms.
Any data with the unknown form of the structure is classified as unstructured data. Or it can also be said as Unstructured data refers to the data that lacks any specific form or structure whatsoever. In addition to the size being huge, unstructured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos, etc.
Semi-structured is the third type of big data. Semi-structured data can contain both forms of data. We can see semi-structured data as a structured in form but it is actually not defined with e.g. a table definition in relational DBMS.it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data.
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