Difference Between XML And JSON

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XML vs JSON

Overview:
XML:

Extensible Markup Language(XML) is a markup language that is human-readable and machine-readable. It is a hardware and software independent tool for storing and transporting data. Technically, an XML document does nothing as it is just information wrapped up in tags. A piece of software is needed that can send, receive, store and display the document. There are no predefined tags in XML. The tags are invented by the author of the XML document.

JSON:

“JavaScript Object Notation (JSON) is a lightweight data-interchange format.”[1] This is easy for humans to read and write it, It is also easy for machines to parse and generate. JSON is an easier to use alternative to XML. JSON is language independent, it uses JavaScript …show more content…

They are both hierarchical so you can have values within values. Both languages can be parsed to be used by lots of programming languages. Both can be passed around through httpWebRequest using AJAX.

How they are unalike
XML uses angle brackets, with the tag name at the start and end of the element. Where as, JSON uses the curly braces and has the tag name only at the start of the element. JSON is less long-winded meaning it is quicker for humans to write and to also read. JSON includes arrays where each element doesn’t have a name of its own. In XML you can use any name that you want for an element but in JSON you can’t use reserved words from JavaScript.

Technically there would be no clear winner as it would depend on the developers goals. JSON is a good choice for mobile development where as XML is a good choice for large data.

Web Services

“Web services are web application components. These can be published, found and used on the web.” [2] The are different types of web services such as WSDL, SOAP, RDF and …show more content…

Web services use XML to code and decode data and uses SOAP to transport it with open protocols. Application components that web services offer are currency converter and weather reports.

Big Data

Big data is a broad term for datasets that are so large or complex that data processing applications are inadequate. The challenges that come with Big Data would be the analysis capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The term can refer to the use of predictive analytics or other advanced methods that are used to extract the value from the data.

Relational database management systems and desktop statistics and visualization packages often can’t handle Big Data. The work can require anything from tens to thousands of servers. Big data varies depending on the capabilities of the users and their tools.
Since Big Data is so big in volume the data doesn’t sample, it just observes and tracks everything that happens. The Big Data can often be available in real-time. There is a big variety in big data, it can be drawn from text, images, audio and it completes missing pieces through data fusion. Big data doesn’t ask why, it just detects patterns. Big data can often be a cost-free byproduct of digital

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