Here is an interview with Tammer Kamel, CEO and Co-Founder of Quandl.com. The Quandl API promises to make all numeric data on the internet easy to use. Quandl is a collaboratively-curated portal to over 3 million time-series datasets. Quandl’s long time goal is to make all the numerical data on the internet easy to find and easy to use.
PW-What was the reason for building Quandl?
Q- As a data scientist, I was spending *way* to much time trying to find, validate, reformat and merge the data I wanted to analyze. I felt that a site like Quandl was way overdue. We have one simple mission here: to make numerical data easy to find and easy to use.
PW- Describe the Quandl API- how do you intend to enthuse developers to use it?
Q-The Quandl API is a simple, consistent interface to over 3 million time series datasets. Our mission is twofold: To give developers one simple interface that they can use for all their numerical data needs, instead of having to talk to multiple different ones, secondly to create APIs where none previously existed; Quandl is already an API to these millions of datasets that are really just text documents or spread sheets sitting on a server somewhere. The dream is that any time series that lives on the internet, whether it has an API, or is just one some one's blog, will be accessible via Quandl's API. We're already working with a couple of groups to build apps on top of the Quandl API. We're excited about that because they help us refine and improve the API to meet their needs.
(One interesting thing: you could actually build www.quandl.com on top of the Quandl API if you wanted to.)
PW-The Open Datasets market has existing players like infochimps and datamarket.com . How does Quandl intend to distinguish and differentiate itself?
Q-Infochimps has shifted towards classic big data discovery. They've created a very robust infrastructure for dealing with massive big data questions for clients and are doing an excellent job, but there really is no overlap with our mission to make numerical data easily accessible and useable for those around the world.
In regards to Datamarket, we both share the desire to create structure where none previously existed, and they're doing a great job of that now with their new enterprise solution. I do think we're both providing data offerings that differ enough that we both can provide unique value to data users. If things continue to go well for us I feel there's more than enough room for the both of us in the market, and I think it is the big, old "closed data" companies that we can disrupt.
PW-Do you plan to add topic nodes tothe API. What are the other changes that are planned. How about creating libraries for Python /R/Ruby/JS?
Q-Topic nodes are on the long term agenda, yes. But libraries are on the short term agenda. We've done R already. Python, Ruby, Matlab, and Stata are next. As we connect with experts in each of these arenas, we're working with them to make sure we get the right "feel" for each language. (see Quandl's github here)