Death, Taxes and JSON

JSON is a language independent text based data interchange format. It is expected to become the data standard of storage and exchange for the foreseeable future. All tools and databases, Big Data or other, will support it.

JSON empowers developers in a world filled with Big Data hype about how unstructured data will drown out structured data.

Because it is so simple, it is not expected that the JSON grammar will ever change. This gives JSON, as a foundational notation, tremendous stability. JSON was first presented to the world at the JSON.org website in 2001. JSON stands for JavaScript Object Notation.

Screenshot 2014-10-31 19.09.19

Parsimonious and powerful:

  1. Agnostic about numbers (offers only the representation of numbers that
    humans use: a sequence of digits)
  2. Simple expression of name/value pairs (programming languages can map to – record, struct, dict, map, hash, or object)
  3. Supports ordered lists of values (map to array, vector, or list)

It will be built upon and extended, but is not expected to change. For instance, a recent extension that may be foundational to the development of web applications in the future is, JSON-LD (LD=Linked Data). It was announced on Jan 16, 2014 by the W3c (The consortium that is responsible for standards that define the internet, for example HMTL).

This specification defines JSON-LD, a JSON-based format to serialize Linked Data … It is primarily intended to be a way to use Linked Data in Web-based programming environments, to build interoperable Web services, and to store Linked Data in JSON-based storage engines.

A Big Data How To Guide – Overlooked advice?

Lots of thoughtful guidance for engineers, designers, scientists and businesses to focus on while doing the exciting interdisciplinary and real work that lies ahead.

The extremely thorough study made a few major recommendations, from my initial read:

  1.  Deeply interdisciplinary engineering approach influenced by design and system thinking
  2. “Middleware” to link high level analysis with distributed computational resources that maximizes reuse [Consider parallel computing reference by Asanovic and team]
  3. Students and the workforce need foundational statistical and computational training [Think ubiquitous driver permits after mass adoption of automobiles]

They allude to the possibility of an FFT like standard tool for massive data analysis (anyone who has done signal processing knows how ubiquitous the FFT is in getting almost anything done more efficiently). However, they claim to be pessimistic given the scope/variety of this domain.

Perhaps these principles can be uncovered once and for all, such that each
successive generation of researchers does not need to reconsider the massive data problem afresh.

Will we create a platform that stands for decades (think Shannon and Schockley) or take short cuts that lead to yet another disappointing bubble of frenetic activity?

Time will tell – Metonymy Labs seeks to participate productively.