Weitere Ă€hnliche Inhalte Ăhnlich wie Chicago Hadoop in Finance - Ted Dunning (20) Mehr von MapR Technologies (20) KĂŒrzlich hochgeladen (20) Chicago Hadoop in Finance - Ted Dunning2. 2©MapR Technologies - Confidential
Big is the next big thing
ï§ Big data and Hadoop are exploding
ï§ Companies are being funded
ï§ Books are being written
ï§ Applications sprouting up everywhere
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5. 5©MapR Technologies - Confidential
Why Now?
ï§ But Mooreâs law has applied for a long time
ï§ Why is Hadoop exploding now?
ï§ Why not 10 years ago?
ï§ Why not 20?
58/13/2013
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Size Matters, but âŠ
ï§ If it were just availability of data then existing big companies would
adopt big data technology first
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7. 7©MapR Technologies - Confidential
Size Matters, but âŠ
ï§ If it were just availability of data then existing big companies would
adopt big data technology first
They didnât
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8. 8©MapR Technologies - Confidential
Or Maybe Cost
ï§ If it were just a net positive value then finance companies should
adopt first because they have higher opportunity value / byte
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9. 9©MapR Technologies - Confidential
Or Maybe Cost
ï§ If it were just a net positive value then finance companies should
adopt first because they have higher opportunity value / byte
They didnât
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10. 10©MapR Technologies - Confidential
Backwards adoption
ï§ Under almost any threshold argument startups would not adopt
big data technology first
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11. 11©MapR Technologies - Confidential
Backwards adoption
ï§ Under almost any threshold argument startups would not adopt
big data technology first
They did
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12. 12©MapR Technologies - Confidential
Everywhere at Once?
ï§ Something very strange is happening
â Big data is being applied at many different scales
â At many value scales
â By large companies and small
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13. 13©MapR Technologies - Confidential
Everywhere at Once?
ï§ Something very strange is happening
â Big data is being applied at many different scales
â At many value scales
â By large companies and small
Why?
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14. 14©MapR Technologies - Confidential
More data is being produced more quickly
Data sizes are bigger than even a very large computer can hold
Cost to create and store continues to decrease
The Conventional Answer
15. 15©MapR Technologies - Confidential
Analytics Scaling Laws
ï§ Analytics scaling is all about the 80-20 rule
â Big gains for little initial effort
â Rapidly diminishing returns
ï§ The key to net value is how costs scale
â Old school â exponential scaling
â Big data â linear scaling, low constant
ï§ Cost/performance has changed radically
â IF you can use many commodity boxes
16. 16©MapR Technologies - Confidential
We knew that
We should have
known that
We didnât know that!
Youâre kidding, people do that?
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2,0000 500 1000 1500
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0
0.25
0.5
0.75
Scale
Value
Anybody with eyes
Intern with a spreadsheet
In-house analytics
Industry-wide data consortium
NSA, non-proliferation
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2,0000 500 1000 1500
1
0
0.25
0.5
0.75
Scale
Value
2,0000 500 1000 1500
1
0
0.25
0.5
0.75
Scale
Value
Net value optimum has a
sharp peak well before
maximum effort
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2,0000 500 1000 1500
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0.25
0.5
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Scale
Value
More than just a little
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2,0000 500 1000 1500
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0
0.25
0.5
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Scale
Value
They are changing a LOT!
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2,0000 500 1000 1500
1
0
0.25
0.5
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Scale
Value
Initially, linear cost scaling
actually makes things worse
A tipping point is reached and
things change radically âŠ
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Pre-requisites for Tipping
ï§ To reach the tipping point,
ï§ Algorithms must scale out horizontally
â On commodity hardware
â That can and will fail
ï§ Data practice must change
â Denormalized is the new black
â Flexible data dictionaries are the rule
â Structured data becomes rare
29. 29©MapR Technologies - Confidential
The Standard Sort of Model
ï§ People talk about the law of large numbers as if it were âŠ
ï§ Well, as if it were a law
ï§ Itâs not âŠ
ï§ It is a context and assumption dependent theorem
30. 30©MapR Technologies - Confidential
What if âŠ
ï§ These assumptions are:
ï§ Changes have a
â stationary,
â independent,
â finite variance distribution
ï§ What happens if these assumptions are wrong?
ï§ And which of them is really wrong?
33. 33©MapR Technologies - Confidential
What if the Assumptions are Wrong?
ï§ Take the finite variance as a simple example
ï§ This leads to Levy stable distributions
ï§ Like the Cauchy distribution
38. 38©MapR Technologies - Confidential
But is it Really Infinite Variance?
ï§ Or are there other kinds of phenomena that show this?
ï§ What about the independence assumption?
ï§ What if the supposedly independent components of the system
communicate?
ï§ Like we do. Everyday. All the time.
39. 39©MapR Technologies - Confidential
Why the Difference?
Law of large
numbers
Infinite
variance
Interacting
agents
Apologies and credit to
Simon DaDeo, SFI
The space of
all things that
change
The space of
interacting
things
40. 40©MapR Technologies - Confidential
What Happens with Interactions
ï§ Social phenomena defeat the law of large numbers
ï§ Distributions are well modeled by ârich get richerâ processes
â Pittman-Yar process, Indian Buffet
ï§ Limiting dstributions are heavy tailed, power law
ï§ We see these distributions everywhere
â price of cotton in the 19th century
â word frequencies
â popularity of Github projects
â equity pricing and volumes
â sizes of cities
â popularity of web-sites
43. 43©MapR Technologies - Confidential
In a Nutshell
ï§ Scalability is much more important than we thought
ï§ Mashups are more important than we thought
ï§ Network effects are more important than we thought
ï§ Exploration is more important than we thought
ï§ Hadoop style linear scaling must be mixed with ad hoc analysis
45. 45©MapR Technologies - Confidential
whoami?
ï§ Ted Dunning
â @ted_dunning
â tdunning@maprtech.com (MapR distribution for Hadoop)
â tdunning@apache.com (Mahout, Hadoop, Lucene, Zookeeper, Drill)
â ted.dunning@gmail.com (me)
ï§ More info:
http://www.mapr.com/company/events/hadoop-in-finance-2012
Hinweis der Redaktion Why is big data sooo fashionable with big and small companies from different industries? What has suddenly changed? Google searches are up 10x over just four years ago. Hadoop use is exploding. We chose this example, which shows job trends for Hadoop. Further evidence that you should pay attention during this talk. But we have seen constant growth for a long time. And simple growth would only explain some kinds of companies starting with big data (probably big ones) and then slow adoption. Databases started with big companies and took 20 years or more to reach everywhere because the need exceeded cost at different times for different companies. The internet, on the other hand, largely happened to everybody at the same time so it changed things in nearly all industries at all scales nearly simultaneously. Why is big data exploding right now and why is it exploding at all? The different kinds of scaling laws have different shape and I think that shape is the key. The value of analytics always increases with more data, but the rate of increase drops dramatically after an initial quick increase. In classical analytics, the cost of doing analytics increases sharply. The result is a net value that has a sharp optimum in the area where value is increasing rapidly and cost is not yet increasing so rapidly. New techniques such as Hadoop result in linear scaling of cost. This is a change in shape and it causes a qualitative change in the way that costs trade off against value to give net value. As technology improves, the slope of this cost line is also changing rapidly over time. This next sequence shows how the net value changes with different slope linear cost models. Notice how the best net value has jumped up significantly And as the line approaches horizontal, the highest net value occurs at dramatically larger data scale. And as the line approaches horizontal, the highest net value occurs at dramatically larger data scale.