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The Big Promise of 'Big Data'
snydeq writes "InfoWorld's Frank Ohlhorst discusses how virtualization, commodity hardware, and 'Big Data' tools like Hadoop are enabling IT organizations to mine vast volumes of corporate and external data — a trend fueled increasingly by companies' desire to finally unlock critical insights from thus far largely untapped data stores. 'As costs fall and companies think of new ways to correlate data, Big Data analytics will become more commonplace, perhaps providing the growth mechanism for a small company to become a large one. Consider that Google, Yahoo, and Facebook were all once small companies that leveraged their data and understanding of the relationships in that data to grow significantly. It's no accident that many of the underpinnings of Big Data came from the methods these very businesses developed. But today, these methods are widely available through Hadoop and other tools for enterprises such as yours.'"
It's actually his hip-hop name.
I think that the real innovation will be a variation of SQL that allows for the persistence of queries, such that they continue to yield new results as new data is found to match them in the database. If you have a database of a trillion web pages, and you continue to put more in, it doesn't make sense to re-scan all of the existing records each time you decide you need to get the results of the query again. It should be possible, and far more computationally efficient to have a stream of results from a LiveSQL query that can feed a stream, instead of batch mode.
I've registered the domain name livesql.org as a first step to helping to organize this idea and perhaps set up a standard.
Dells are cheaper than IBM, for the amount of performance they provide. Big Data is best handled in a large cluster, which provides reliability and parallelism to process a highly-scalable amount of data in a very short time. Beyond a certain point, high-end machines just raise the cost of failure, where low-end machines provide only marginally lower performance.
You do not have a moral or legal right to do absolutely anything you want.
It's actually his hip-hop name.
I thought "Big Data" was his Italian Gangsta name from when he played Furio on The Sopranos: http://www.imdb.com/media/rm597203712/nm0144843
Want to improve your Karma? Instead of "Post Anonymously", try the "Post Humously" option.
...and 'Big Data' tools like Hadoop are enabling IT organizations...
...these methods are widely available through Hadoop and other tools...
Oh... also... did I mention HADOOP!!??
Faith is a willingness to accept something w/o complete proof and to act on it. Reason allows you to correct that faith.
First, I'm not really sure what you mean by "copy it into HDFS", since that's usually where data is stored in the first place. Copying in lots of data without giving the cluster time to stabilize can cause it to go into safe mode, where it won't make changes until everything's properly distributed. It's version 0.20. Don't expect perfection just yet.
There are also experts out there who will be quite happy to help get things running better. My company has been using Hadoop quite successfully with their help.
You do not have a moral or legal right to do absolutely anything you want.
Looks like somebody got their PR spin piece relayed as a news story again. Bravo!
It isn't about Facebook so much as it's a shift in what problems are practically solvable.
First, realize that traditional approaches like SQL are limited mostly by the single box (or the few mirrors) the platform runs on. Querying a large (a billion rows) table can take minutes on a very fast machine, hours if there's significant disk access needed, and months if the query's complex enough. Clusters can process those same billion records far faster, bringing that time down from months to hours, or even seconds for a simple scan. Advances in cluster computing over the last few years have made this parallel processing much easier.
The promise is that problems that were previously too big to even think about are now easy. If your solved problem is something people want, like showing what their friends are up to, your product will do well.
You do not have a moral or legal right to do absolutely anything you want.
Related to using Big Data in Business is Big Data in Science. Wired ran a nice series of articles looking at this (http://www.wired.com/wired/issue/16-07). This raises all sorts of problems (for example, how can results be reproduced? What if the model of the data is as complex as the data? Are all results obtained with Small Data simply artefacts of sparse counts?).
A mainframe is>/i> a large cluster these days, together with the most robust physical hardware. There's a reason that some mainframes have had an uptime of more than a decade, with almost every part having been upgraded along the way.
Hadoop and similar products are finally making mainframe-style data processing cheaper on commodity hardware - or, rather, making such solutions available to the general public. Google's been doing it forever. Still, if you're mainframe has an uptime longer than Google has been around, there's not much incentive to change.
Socialism: a lie told by totalitarians and believed by fools.
In situations where you are using Hadoop, your "primary" data store should BE the HDFS store you are using to analyze it. That's a big part of the actual efficiency proposition of Hadoop.
The big trick with the "big data" approaches is to recognize that you keep _everything_ distributed, _all the time_. Your input dataset is not "copied into the system" for some particular analysis task, it _exists in the system_ from the time you acquire it, and the analysis results from it are kept distributed. It's only at specific points in time (exporting data to send to someone external, importing data into your infrastructure) that you should be messing around with copying stuff in and out of HDFS.
-Laxitive
Mainframes don't offer the data capacity that a large cluster does. The topic here is handling big datasets (measured in the billions of records), and single machines just can't do that as efficiently as a cluster, no matter how beefy they are. The future is parallel.
You do not have a moral or legal right to do absolutely anything you want.
Damn dude couldn't you use your dads disability check to put him and your mom in a nice retirement home instead of using it for to build your own server farm in the garage?
Because their business is based entirely on how that data correlates.
99.999999999% of the rest of the world do other things as their primary business model. Small businesses aren't going to do this because it requires a staff that KNOWS how to work with this software and get the data out.
Walmart might care, but they aren't a small business.
The local auto mechanic, or plumber, or even small companies like lawn services or maid services simply aren't big enough to justify having a staff of nerds to make the data useful to them, and they really don't have enough data to matter. It simply is too expensive on the small scale.
Companies that can REALLY benefit from the ability to comb vast quantities of data have been doing it for well over a hundred years. Insurance companies are a prime example. You know what? They aren't small in general, so they have the staff to do the data correlation and find out useful information because it works on that scale.
Anyone who cares about churning through massive amounts of data already has ways to do it. Computing will make it faster, but its not going to change the business model.
I'm kind puzzled why virtualization has anything to do with this, unless someone is implying that a smart thing to do is setup a VM server, and then run a bunch of VMs on it to get a 'cluster' to run distributed apps on ... if thats the point being made then I think someone needs to turn in their life card (they clearly never had a geek card).
So now that I've written all that, I went and read the article.
Now I realize that article is written by someone who has absolutely no idea what they are talking about and simply read a wikipedia page or two and threw in a bunch of names and buzzwords.
Hadoop doesn't help the IT department do anything with the data at all.
Its the teams of analyists and developers that write the code to make Hadoop (which is only mentioned because of its OSS nature here) and a bunch of other technologies and code all work together and produce useful output.
This article is basically written like the invention of the hammer made it so everyone would want to build their own homes because they could. Thats a stupid assumption and statement.
Slashdot should be fucking ashamed that this is posted anywhere, let alone front page.
Persistent Volume manager for Kubernetes - https://github.com/dwimsey/openshift-pvmanager
Rummaging in the Bitlocker
Starring everybody's favorite...
Peta Bites
and costarring...
Bare Bones
and making his professional debut:
Big Data!
you need to know what to look for. In order to know what to look for you need to know what's meaningful and that requires some sort of useful model. Accumulating data in itself isn't that interesting.
You do realize that 'a cluster' is really just 'a bunch of mirrors' that you're distributing the query across ... right? So yes, two is more than one, and two can be faster than one. Thats true regardless of what you call it. Two mirrored machines ARE a cluster. Hell the summary makes it sound like they want to run virtual machines as part of the cluster ... probably running on the same VM server ...
Clusters REALLY AREN'T DOING ANYTHING WE HAVEN'T BEEN DOING FOR 50 YEARS.
STOP PRETENDING THIS IS NEW.
Just because you just started to become aware of the exact same things that have been done since the 60-70s doesn't mean its actually NEW.
Persistent Volume manager for Kubernetes - https://github.com/dwimsey/openshift-pvmanager
EVAR?
The so-called computational scientists I used to work for through an entire alphabet soup of FFRDCs were barely able to program in FORTRAN, much less something as sophisticated at Hadoop.
Notice that the article skirts this issue -- yes, they work with "Big Data" but they don't use any dev tools developed post-1963 to do it in, believe me.
It isn't about what's practically solvable, it's about what's cheaply solvable. These problems have been practical for anybody with money for a while. Hadoop lowers the barrier to entry.
If you've got the cash, IBM will set you up with a monster SQL cluster that will take that massive complex SQL query (the one that takes a month to run on your desktop), and return results in 2 seconds. If you have to ask how much it costs, you can't afford it.
This setup is still not cheap, it's just much cheaper than it used to be. You still have to build and maintain a large cluster of machines, but you can buy commodity servers instead of IBM mainframes. Now anybody with a couple hundred thousand dollars can play, instead of only Fortune 500 companies.
What large commodity clusters provide is a price per cycle low enough that the owner doesn't have to worry about efficiency. For example, Google's Dean and Ghemawat ("MapReduce: Simplified Data Processing on Large Clusters") managed to successfully sort 10^10 100-byte records over 891 seconds, or about 6MB sorted per processor per second. Very fast overall, but hardly efficient use of modern hardware. There's an important place for the new big dataset system, but the argument is cost, not efficiency.
Think about the term "mirror" for a while. In most mirrored setups, all data goes everywhere. Yes, we've had mirrors for quite a while, and they've provided linear speedup, and increase the cost of upgrades. A cluster doesn't just copy the data everywhere. Hadoop, for example, replicates each block only three times by default. On a large cluster, it's very likely that two randomly-selected nodes won't share any common data. The idea of having parallel data access isn't new, but the technology to do it efficiently while maintaining a low cost is. That's why I included the word "practically" in my first sentence.
It's a new and more efficient use of an old concept. Bicycles have been around for about two hundred years, and yet the Tour de France still makes the news.
You do not have a moral or legal right to do absolutely anything you want.
A mainframe stopped being a "single machine" in any sense but the physical chassis a long time ago. They are clusters, from a performance perspective. The z196 will put 80 cores (each quite beefy compare to an Intel core) in the main chassis, but allow quite a few more blades to be added for DB query processing and similar tasks (112 blades per node, not sure how many nodes are supported, but I suspect max config will be thousands of cores).
A modern mainframe is really just a large cluster with centralized management and a focus on hot-swappable components. As Google-style "loose clusters" mature, so you just don't care if an individual machine fails, the premium for mainframe hot-swap reliability becomes questionable (where you don't have to care if an individual component fails).
That's what's interesting about Hadoop - it's not that it scales in ways never seen before, but that it scales at prices never seen before (for a non-proprietary solution)
Socialism: a lie told by totalitarians and believed by fools.
Furry gangsters?
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I drank what? -- Socrates
Money and hardware are interchangeable. Price per cycle is still a measure of efficiency.
You do not have a moral or legal right to do absolutely anything you want.
Assuming the maximum configuration is thousands of cores, how does it compare in other aspects to Facebook's 23,000 cores and 36 petabytes of data, with unlimited scalability to come?
For all intents and purposes, mainframes are still mainframes. They're parallel, and they grow, but they still have those limits that clusters just don't have.
(I consider price to be a limit as well)
You do not have a moral or legal right to do absolutely anything you want.
For the most part, Google has moved onto Caffeine and GFS2 for their support. Apparently, Big Table was taking too long to regenerate the entire index, forcing Google to refresh only part of their index frequently. The new Caffeine framework supposedly lets Google get closer-to-real-time search results because newly-indexed/crawled data can be continuously tossed into the search database without requiring an entire batch process. Perhaps that's why quotes from Slashdot comments show up in Google so quickly. This technology allows Google to chase news, blogs, and Twitter feeds while they're still relevant, which is pretty freaking cool.
The guys who were complaining about Google Instant and how Google should make better search results didn't mention Caffeine. Hopefully, Google can figure out how to use this technology to weed out the spam links and SEO crap that dominates some searches.
A NYC lawyer blogs. http://www.chuangblog.com/
"You do realize that 'a cluster' is really just 'a bunch of mirrors' that you're distributing the query across"
You do realize that the kind of cluster we are talking here is not "just 'a bunch of mirrors'" by big margin: you don't copy the whole data set to every node; you don't "copy" the computing load to every node.
Distributing is quite different from mirroring.
"It isn't about what's practically solvable, it's about what's cheaply solvable."
Aren't they the same? Isn't cost a practical constraint?
"These problems have been practical for anybody with money for a while."
No matter how rich you are, if you need six dollars to get five, it's not practical.
"Hadoop lowers the barrier to entry."
By means of lowering production costs. And that means that now you need four dollars instead of six for your five-dollar opportunity which is exactly which turns an impractical bussiness into a practical one.
"If you've got the cash, IBM will set you up with a monster SQL cluster that will take that massive complex SQL query "(the one that takes a month to run on your desktop), and return results in 2 seconds. If you have to ask how much it costs, you can't afford it."
Your last sentence is *only* valid for luxury goods. For anything else everybody, no matter how rich they are, asks for the costs and rightly so. Do you think rich people get rich by going into negative-ballance bussiness?
Vertica can handle lots of data in a very fast manner (at least for data warehousing). They use a MPP architecture. Commodity hardware in a cluster running Linux.
No need for big machines. You can use lots of little ones.
Except for ending slavery, the Nazis, communism, & securing American independence, war has never solved anything.
"Isn't cost a practical constraint?"
Yes, but the degree varies. In a low margin business, I might be willing to invest a couple million dollars, if it will lower my costs by 1%. Who's in a better position to make that statement, the Fortune 500 company, or the guy in the garage?
"No matter how rich you are, if you need six dollars to get five, it's not practical."
Right. But if you need $10M to get $20M, most people can't play. Even if you need $10M to get $100M, you have to convince somebody to give you $10M. I can't do that. But, if I can come up with a business that needs $50k to get $500k, I can talk somebody into that.
BTW, thanks for calling me out.
I was replying to the "First, realize that traditional approaches like SQL are limited mostly by the single box" statement in the GP. But I did gloss over a few details...
I think some approaches call this "sharding?"
@CmdrTaco, et al., You might go to the 'in-depth guide" Olhorst mentions [http://www.pwc.com/us/en/technology-forecast/2010/issue3/index.jhtml], and assess that separately. We did a lot of research with the CIO and the rest of the C-suite in mind as a target audience. Of course Google has moved on beyond Bigtable, etc.... According to @royans [http://www.royans.net/arch/pregel-googles-other-data-processing-infrastructure/], Google uses Pregel to mine graphs. Allegedly 20% of their data they mine with Pregel; the other 80% they mine with MapReduce. Two of the Google engineers presented on Pregel at SIGMOD in June. In other words, these companies are developing and using different methods to mine different kinds of data. Much of the tool innovation happens at the companies doing the mining. @BitZstream, et al., Try to step back a bit and think about the frogs in the ponds next to yours. There is life beyond SQL and relational data. IT departments at large enterprises, particularly those with a significant Web presence or large collections of less-structured data, are using Hadoop, and we cite some of them in the issue. Others we have spoken to since we published in the Spring. Hadoop is a true ecosystem with lots of developers who've been plugged in for years, and they work at Web scale. Yes, the challenge of operating at Web scale is not a challenge everyone has, but it's a challenge more will face. @AlanMorrison
I'm no DBA, but my limited understanding is that sharding requires advance knowledge of the data being stored and the application thereof. My understanding also is that there are some designs that can't be effectively sharded without a huge cost penalty.
That's great if your programmers are sitting right next to the DBAs, but it's pretty bad for systems where other have to access the database as well. A project I'm working on now involves a painfully-slow SQL query, because we need to query by date, and the table is partitioned by an id number. Politics and other issues mean we won't be getting an index just to help our very-limited use case.
Hadoop (and more directly equal to SQL databases, HBase) distributes data with no prior knowledge. The distribution is done automatically, with no concern for what the data actually is or how it might be used. It keeps generality up and costs down, going back to my original comment about practical solutions.
You do not have a moral or legal right to do absolutely anything you want.