Introduction to Big Data: Species, Big Data Characteristics and Big Data Benefits

Big data is also big data. Big Data is a term used to describe a huge set of data but it grows exponentially over time.

big data

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In short, this data is so large and complex that none of the traditional data management tools can store or process it efficiently.

Examples of big data

  • Big data types
  • Big Data Properties
  • The advantages of big data processing
  • Introduction to Big Data: Species, Characteristics and Benefits

Here are some examples of big dataThe New York Stock Exchange produces about one terabyte of new trade data every day.

Introduction to Big Data: Species, Characteristics and Benefits

Social media

Statistics show that 1000+ terabytes of new data are absorbed into Facebook social media databases, every day. This data is created primarily in terms of image and video uploads, message exchange, comment mode, etc.

Introduction to Big Data: Species, Characteristics and Benefits
A single jet engine can generate more than 10 terabytes of data in 30 minutes of flight time. With several thousand flights a day, data generation reaches a large number of Petabytes.

Introduction to Big Data: Species, Characteristics and Benefits

Big data types
BigData ‘can be found in three forms:

  • structured
  • unstructured
  • Semi-structured

Structured

Any data that can be stored, accessed, and processed in the form of a static format called “structured” data. Over the period of time, talents in computer science have been more successful in developing techniques for working with this type of data (where coordination is known in advance) as well as extracting value from it. However, at present, we expect problems when the volume of such data increases considerably, with typical sizes in the midst of multiple zettabytes.

Do you know? 1021 bytes equal to 1 zettabyte or billion terabytes constitutes zettabyte.

When looking at these formats, one can easily understand why giving big data-name and imagine the challenges involved in storage and processing.

Examples of structured data

The Employee table in the database is an example of structured data

unstructured

Any data of unknown shape or structure is classified as disorganized. In addition to the large size, unstructured data poses multiple challenges in terms of processing them to extract value. A comon example of unstructured data is a different data source that contains a set of simple text files, images, videos, and so on. Now organizations today have a lot of data available with them but unfortunately, they do not know how to derive value from them since these data are in their raw form or unstructured format.

Examples of unstructured data

The output returned by Google Search

Introduction to Big Data: Species, Characteristics and Benefits

Semi-structured

Semi-structured data can contain both forms of data. We can see semi-structured data as structured, but in fact it is not defined, for example, as a table definition in relational databases. An example of semi-structured data is data represented in an XML file.

Examples of semi-structured data

Personal data stored in an XML file

<rec> <name> Prashant Rao </name> <sex> Male </sex> <age> 35 </age> </rec>
<rec> <name> Seema R. </name> <sex> Female </sex> <age> 41 </age> </rec>
<rec> <name> Satish Mane </name> <sex> Male </sex> <age> 29 </age> </rec>
<rec> <name> Subrato Roy </name> <sex> Male </sex> <age> 26 </age> </rec>
<rec> <name> Jeremiah J. </name> <sex> Male </sex> <age> 35 </age> </rec>
Data growth over the years

Introduction to Big Data

Please note that unstructured web application data consists of log files, transaction log files, etc. OLTP systems are designed to work with structured data where data is stored in relationships (tables).

Big Data Properties

(I) Size – The name of the big data itself is associated with a huge size. The size of the data plays a very important role in determining the value of the data. Also, whether or not certain data can actually be considered large is dependent on the size of the data. Thus, “size” is one of the characteristics to consider when dealing with big data.

(ii) Miscellaneous – The next aspect of big data is its diversity.

Diversity refers to heterogeneous sources and the nature of data, both structures

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