Comment by nemesisj | Hacker News Reader

Comment by nemesisj | Hacker News Reader

Another conclusion to draw from this article (which I really enjoyed, by the way) is that Big Data has been turned into one of the most abstract buzzwords ever. You thought "cloud" was bad? "Big Data" is far worse in its specificity.

I can't count the number of times I'll be talking to some sales rep and they'll describe how they scan the data within whatever application they're demoing and "suggest" items using "big data techniques". In almost all cases they're talking about a few thousand or hundred thousand records, tops.

I've found that when non-hardcore techies talk about Big Data, what they really mean is "they have some data" vs before, when they had zero data.

From the article:

"Consultants urge the data-naive to wise up to the potential of big data. A recent report from the McKinsey Global Institute reckoned that the US healthcare system could save $300bn a year – $1,000 per American – through better integration and analysis of the data produced by everything from clinical trials to health insurance transactions to smart running shoes.

What these consultants mean is that by having just some data compared to the silo'd data that is the norm in US healthcare, they could save a lot, and they're right. My previous company had a large data set (20+ million patients) and we'd find millions of dollars of savings opportunities for every hospital we implemented in, but that's because we had the data, not because we were running some kind of non-causual correlation analysis like the article references. It was just because we could actually run queries on a data set.

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Off Topic - how annoying is it that when you copy & paste from the FT, they preface your copy with the following text?

High quality global journalism requires investment. Please share this article with others using the link below, do not cut & paste the article. See our Ts&Cs and Copyright Policy for more detail. Email ftsales.support@ft.com to buy additional rights. http://www.ft.com/cms/s/2/21a6e7d8-b479-11e3-a09a-00144feabd...

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Less than a month ago I was at an event about Big Data in marketing. One of the speakers spoke about how they used user data to improve their client's brand experience. If was very effective and I agree they did something extraordinary.
I then asked what tools they used. He responded with a well-known relational database. I then asked the total size of their dataset, with a good idea of what the upper bounds would be. He responded "around 100 million events" since the product started, 6 months ago.
It's really sad because they may end up under fire despite the effectiveness of their work.
Big Data is a lot like teen sex.

>Big Data is a lot like teen sex.
LOL.

But, like "cloud" or "web 2.0" or any similar buzzword there obviously is some substance to it unspecific, un-novel, abused, or not. It just break into unsatisfying mush when you look at it to closely.
Web 2.0 was some sort of a shift over web 1.0, the line between publisher and consumer melted. Cloud is etherealizing computing and data. There was a thread a few days ago about the film Her. "Where is Samantha" (the AI) is borderline nonsensical statement. It doesn't come up to a viewer. That's because people are used to cloud as an idea now It doesn't really matter that servers, replication, dumb clients, remote data or whatever were invented a long time ago.

I enjoyed your post, nemesisj. Within your field of longitudinal patient data, if I am correct in what you have written, your large datasets simply have a new name, paranthetically Big Data, and that you could get what you need to save money without the newfangled algorithms. Within academic bioscience, I think there is great consensus on what Big Data is - I have not seen much argument at all; however, it is still very hard to define within that field. The best I can do, over this cup of coffee, is to state that there is a clear distinction between the study of a gene, up to a few pathways vs. computational analysis of multiple OMICS (genomics, metabolomics and proteomics) datasets. I know that definition is terribly lacking and I am fighting the urge to delete it for the sake of getting the post completed. Anyway, Big Data is clearly changing the academic biosciences through the funding trend. That is, grants with a computational focus, or sub focus, certainly seem to be doing comparably well. I mention this because todays academic funding trends influence the direction of tomorrows startups as those being trained are disproportionally within the better funded labs, and draw upon their previous experience when forming companies. So, I personally believe this Big Data thing, however it is best defined over all, or within a given field, is in some way something new, and will continue to shape the startup sphere for years to come, especially in the areas of genomics, metabolomics and proteomics. This is my first post: ) Thanks!

" This is my first post: )"
I really don't mean to be snarky, but please try to use paragraphs. It'll make it much more likely people will read your post.

Out of curiosity, when does it effectively become "big data"?
I ask not to be snarky, but it might be the case that it's "big data" to someone else, but not necessarily to you. I figured it was a relative term for your industry/business, but the hacker crowd definitely seems to peg that amount in the millions of data points before calling it big data at all.
Seems fair, but I'd rather clarify.

Big Data used to mean petabytes, ie above the limits of performant scale up.

I believe the accepted definition for big data is data you can't handle on a single machine and need a cluster to process. So Moore's law makes it a moving threshold.

I think Hadley Wickham has a decent description of big data in terms of the analytical process... to expand slightly on his description:
On normal data you can iteratively explore and visualise it hitting return and seeing plots or model results instantaneously or at most a few seconds.
When you have time to grab a coffee after hitting return then you have bigger data.
If you carefully think through what you are about to ask the computer to do before pressing return then maybe you have big data.
I actually think this is a better description than just size of files or data distributed across many computers as an algorithm that just streams over a massive dataset maybe in parallel can be less challenging than one that has to hold a much smaller e.g many Gb dataset fully in memory.

So my complicated algorithm that processes 200,000 data points is big data because it takes 1/2 hour to run, but someone else's petabyte algorithm that takes 1 second on a cluster is small to "bigger" data? I don't think this makes sense.
It's a measure of the size of the problem to be sure, but it is not a measure of the size of the data, or an indication of what techniques might be required to solve the problem.

Yes to a data analyst that is big data. If you are doing some MCMC and that is really what it takes on that size of data then you have a big data problem.
The more sophisticated a statistic, the more high dimensional the data, the more sampling required, or the more of the dataset it requires to memorise at once - then the smaller your big data threshold will be.
It depends a lot on your point of view too. If I google something now it may bounce across lots of crazy server farms but to me I don't feel like I'm doing big data.. the person who built it all probably feels differently.

I think defining "big data" as something that depends on the algorithm you are applying is not very useful. In that sense, almost anything is big data when you are trying to solve an NP-complete problem (nice course about algorithmic approaches to these problems: https://www.coursera.org/course/optimization).
The problem is, if you define "big data" as something that depends on the algorithm, then it makes no sense to include the word "data" in it. The expression "big data" as it is commonly used refers to flows of data so big that you need specialized approaches even when applying simple transformations to the data.
Since the dawn of computing we've always wanted to solve problems, with small or large amounts of data, that required complex algorithms. The usage of a new expression is justified by the fact that huge flows of data are now available to many companies (mainly because of the web), not because these companies are attempting to perform extremely complicated transformations to the data.
TL;DR: Big data means big volume/flow of data, and not (as you are defining it) using large Big-O complexity algorithms on some set of data. In fact, the size of big data precludes applying large Big-O complexity algorithms to said data.

I usually follow DevOps Borat's definition [1]:
"Big Data is any thing which is crash Excel."
Many a true word spoken in jest.
[1] https://twitter.com/DEVOPS_BORAT/status/288698056470315008

"Small Data is when is fit in RAM. Big Data is when is crash because is not fit in RAM."
https://twitter.com/DEVOPS_BORAT/status/299176203691098112

This is very inaccurate/misleading IMHO. Big Data is something which does not fit in a regular machine for a given operation. You can sort billions of records on an iPhone, for example. You can grep a string within a terabyte-file data on a single personal computer, and I am not convinced you'd go faster with a distributed system (reading the file on cold storage will be the limiting factor). People claiming to do "big data" in these situations do not generally understand the underlying concepts.

With a distributed storage system you should be able to read said terabyte file using far more disk heads.
It would also be easier to engineer it so the terabyte file was entirely in RAM by distributing it across multiple machines (although single machines with TB ram capacity are no doubt continuing to become more common)
Sure, store it on a single tape or disk and distributing the computation won't help. You need distributed storage to properly leverage distributed computation for otherwise I/O bound processes.

You underestimate Excel! You can point Excel to a DB table and use pivot tables on top.
I know it can at least get up to several million, didn't have a chance to test it beyond that! :)

Haha, perfect.

When you are constrained to O(n) methods, you have big data.

Some people are constrained to O(WTF) methods and have no idea about O. So everything is Big Data.

I prefer this definition to others involving the size of memories, or number of computers, because it underscores the data rate instead of just its' (instantaneous) volume.

I appreciate the definition of Big Data as requiring >1 machine.
However... Small Data, that which traditional researchers handle, is normally much, much smaller than that: perhaps 10-10000 data points (and most often on the small end of that). An experienced researcher can essentially can know everything about this data set, including its outliers and quirky points, and get a good sense of it by drawing out simple graphs.
There is clearly some disconnect between these two ideas: is that "Medium Data"?
I would accept a concept of "Big Data" as data that cannot easily be eyeballed to get a sense of what's going on, so 10000+ points would count (under some circumstances). Maybe the concept of "six sigma" is useful - enough data that you would reasonably expect a six sigma outlier.
Mathematically/statistically, the storage limit is not a particularly important milestone: the ideas and methods don't change once you reach this scale (except for potential parallelisation).

I was just going to post the ">1 machine" definition when I saw your comment.
I think there are also at least two meanings of "Big Data". The more popular one is simply a trendy name for good old and boring "statistics", but with a twist that the data comes by way of the Internet, social media, all that.
The second one (and a little closer to my heart) is what ">1 machine" means from a developer/sysadmin perspective. This is where the hadoops, hives, cassandras, etc. come into play, and it's A LOT to learn, even for seasoned developers.
I think it's also a little intimidating for people who have become very comfortable with the typical rdbms stack. Parallel processing can be hard to understand, it's not something you can tinker with on your laptop over the weekend, and it's not surprising to hear all the "your big data thing is stupid" comments.

When you can't read all the data.
~(From a math professor I worked with)

Big Data is less about size and more of a characterization. Human generated data can never be big data - there's just not enough humans to make it all. Possibly with the exception of the biggest social networks.
Big Data is machine generated by systems. Typically its logs, IoT etc.

Not sure how you came to that conclusion. Humans generate voice data, which are then translated to a time series of frequencies for analysis.
Comment data on this site alone would be a pretty big task to analyze.

Fourteen years in IT, ten of them in BI... My definition is as follows: data pertaining to and generated by the source systems that govern various business processes are 'data' data (internal data owned by the business.)
Data pertaining (in whatever abstract sense) to the business, generated by systems outside of the business are 'big'(external data.)
Nothing to do with rowcounts directly IMHO.

I thought the definition in the article was actually really insightful: big data is when you start to behave as if you have N=all for a non-trivial sample.

The typical definition is where standard data management approaches do not work due to high volume, velocity, and/or variety of data sources.
What are standard data management approaches? I don't know. Usually they mean single machine relational db's.
But the thing is that once you get to a certain point on these three you need specialized solutions. High volumes of transactional data with real-time reporting might be handled well by something like Postgres-XC, but that won't handle data of sufficient variety. High velocity data may be best handled with something like VoltDB, but it can't handle volume. Etc....

I think "big data" is a term characterized more by the analytical techniques you apply on them rather than the size of the data. Traditional inferential statistical techniques work on "small data", while newer Bayesian techniques work on "big data" - note that this does not imply that one cannot work on the other.

Not necessarily. Try running basic descriptive statistics on terabyte scale data.

Re: copying from FT, if you're using Firefox you can set dom.event.clipboardevents.enabled to false to get around that. Will probably break copying in some web apps.

NoScript also solves the problem, since they need to run JavaScript code to disable copying. (Their site displays just fine without JavaScript.)

Noam Chomsky had the best response to "big data": it's basically a nonsense concept (which I agree with) because "thinking is hard."

Who said anything about thinking, and how do you know it's hard?
EDIT: I'm getting downvoted, but your statement is incredibly vague and I believe wrong. "Big Data" might be overused as a buzzword, but it's not a "nonsense concept". "Thinking is hard", I assume you are talking about strong AI, and it's not related to this at all. Saying it's "hard" adds nothing of value, and we don't even know if it's true (in the sense that when someone does figure it out, it might seem simple and obvious in retrospect.)

>Saying it's "hard" adds nothing of value, and we don't even know if it's true (in the sense that when someone does figure it out, it might seem simple and obvious in retrospect.)
If it takes decades of hard working geniuses to figure it out, then even if "it seems simple and obvious in retrospect", it IS hard.

Downvoted for mentioning downvoting but totally agree.
I see no connection between big data and AI. Like everything you can of course apply AI to it but I think step one is getting the analytic side down pat.
And also agree thinking may not be hard. It is hard to create a thinking machine (Other than using DNA) but I don't necessary think there is anything special to it actually thinking.
I'd be disappointed if Chomsky actually thought this way, would need context.

Aiming to support the parent's claim (ElDiablo66, is this what you're referring to?), here's a quote from an article [1] about the Chomsky vs. Norvig debate a couple years ago:
Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.
[1] http://www.theatlantic.com/technology/archive/2012/11/noam-c...
[2] HN thread on [1]: https://news.ycombinator.com/item?id=4729068

They only add their preface if the text is greater or equal to 185 characters, it seems.

I believe Tynt pioneered this copy/paste: http://www.tynt.com/product_copypaste
http://daringfireball.net/2010/05/tynt_copy_paste_jerks

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