Open Data can be Big Data, and Big Data can be Open Data -- but they are not synonymous. What is the major difference between the two?
They are not the same at all. Datasets are Open if they are available under a free license to everyone. Datasets are Big if... well, they are. Typically big beyond where common software can handle them in real time.
For example Facebook and Google work with Big Data that is not Open.
Most Open Data sets are actually an example of Small Data: The datasets themselves are not huge, but there is a large number of them that can be correlated to increase their value.
They aren't synonymous, but big data can be inherited by open data. "Big" is just a quantifiable attribute given to data; meaning there is a large amount of it. "Big" data can also be misrepresented in terms of scale. Your big data could be small to someone else and vice versa. "Open" data is an idea that data should be readily available to the public.
I think the point has been missed about "Big Data". It doesn't need to be large in volume.
There are historically (from 2001) three tenets of big data
- high volume (e.g. Facebook, Google index)
- high velocity (e.g. mouse click data being analysed every second for user behaviour triggers)
- high variety (e.g. data that has a diversity of data types stored within it, e.g. documents, images, structured information, relationship information, etc.)
If data has one or more of these, then it is considered "big".
This very good article on open data that shows what it is.
An open data set could be small, slowly changing or non-changing, and contain a well structured simple format - this would not be a "big data" set. If we consider data.gov.uk's 'Trees in Camden' data set, it is a 2.8 Mb csv file of council owned trees which is updated periodically (maybe monthly).