Monday was the official launch of DZone's 2014 Guide to Big Data and to kick off this event, we gathered a panel of six Big Data specialists to participate in DZone's first Twitter Q&A, which appeared under the #DZBigData hashtag. The Q&A went great, and I am personally grateful to all the panelists that helped lend their expert voices to Big Data problems, and I'm glad that we had a turnout for the event. Below you can browse through the actual feed for the hashtag:
Many of these questions and more are discussed in the Big Data guide, so make sure you download it and give it a read. The following is a recap of only some of the questions and answers we saw from the Big Data Q&A. You can see the rest on Twitter at the #DZBigData hashtag.
In terms of the Three Vs, do you think there are any exact or ballpark thresholds that make a use case "Big Data"?
.@mpron When your velocity or volume doesn't fit on one reasonably priced machine, you have a big data problem. Variety does not make big.— Jonathan Ellis (@spyced) September 22, 2014
@mpron depends on the data analysis you're interested in. But I'd say anything which doesn't fit on one server.— mikiobraun (@mikiobraun) September 22, 2014
Are current cloud infrastructures able to handle the growth of Big Data?
@bendzone well, they can handle current state of big data but you mentioned "growth of"... The future is yet tbd— Lillian Pierson, PE (@BigDataGal) September 22, 2014
Any opinions about the Wolfram Language and how it compares to other solutions for visualization of large data sets?
What do you think about IBM releasing Watson Analytics? Will it be worth the $1bil investment?
What is "Dark data"? Is it really a thing and what's its value?
How do you tell the difference between a “lots of data” problem and a “Big Data” problem?
.@DZoneAlec the question is also always how much time for analysis you can endure. For real-time, hundreds of MB might already be too much.— mikiobraun (@mikiobraun) September 22, 2014
Any interesting Big Data ‘fail’ stories?
What's the biggest thing currently holding us back from getting to Big Knowledge from Big Data?
Are Google BigQuery and Cloud Dataflow, and services like them going to be like the AWS (in popularity) for Big Data?
@mpron Lots of potential with these services, and all the data living in the Google Cloud— MapR Technologies (@mapr) September 22, 2014