Big data and Hadoop are growing rapidly due to changes in computing scaling laws that enable analytics to scale linearly on commodity hardware. This tipping point has led to widespread adoption of big data technologies by both large enterprises and startups across many applications and data scales. For large companies, compatibility challenges must be addressed to integrate big data with existing systems, while startups face fewer such constraints.

1. MapR: The Next Generation
Big Data Platform
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2. Big is the next big thing
 Big data and Hadoop are exploding
 Companies are being funded
 Books are being written
 Applications sprouting up everywhere
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3. Slow Motion Explosion
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4. Hadoop Explosion
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5. Why Now?
 But Moore’s law has applied for a long time
 Why is Hadoop exploding now?
 Why not 10 years ago?
 Why not 20?
6/1/2012
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6. Size Matters, but …
 If it were just availability of data then existing big companies would
adopt big data technology first
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7. Size Matters, but …
 If it were just availability of data then existing big companies would
adopt big data technology first
They didn’t
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8. Or Maybe Cost
 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. Or Maybe Cost
 If it were just a net positive value then finance companies should
adopt first because they have higher opportunity value / byte
They didn’t
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10. Backwards adoption
 Under almost any threshold argument startups would not adopt
big data technology first
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11. Backwards adoption
 Under almost any threshold argument startups would not adopt
big data technology first
They did
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12. Everywhere at Once?
 Something very strange is happening
– Big data is being applied at many different scales
– At many value scales
– By large companies and small
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13. Everywhere at Once?
 Something very strange is happening
– Big data is being applied at many different scales
– At many value scales
– By large companies and small
Why?
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14. The Conventional Answer
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
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15. Analytics Scaling Laws
 Analytics scaling is all about the 80-20 rule
– Big gains for little initial effort
– Rapidly diminishing returns
 The key to net value is how costs scale
– Old school – exponential scaling
– Big data – linear scaling, low constant
 Cost/performance has changed radically
– IF you can use many commodity boxes
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16. You’re kidding, people do that?
We didn’t know that!
We should have
known that
We knew that
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17. NSA, non-proliferation
1
0.75
Industry-wide data consortium
Value
0.5
In-house analytics
Intern with a spreadsheet
0.25
Anybody with eyes
0
0 500 1000 1500 2,000
Scale
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18. 1
0.75
Net value optimum has a
Value
0.5 sharp peak well before
maximum effort
0.25
0
0 500 1000 1500 2,000
Scale
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19. But scaling laws are changing
both slope and shape
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20. 1
0.75
Value
0.5
More than just a little
0.25
0
0 500 1000 1500 2,000
Scale
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21. 1
0.75
Value
0.5
They are changing a LOT!
0.25
0
0 500 1000 1500 2,000
Scale
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24. 1
0.75
Value
0.5
0.25
0
0 500 1000 1500 2,000
Scale
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25. 1
0.75
Value
0.5
0.25
0
0 500 1000 1500 2,000
Scale
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26. 1
0.75
A tipping point is reached and
things change radically …
Value
0.5
Initially, linear cost scaling
actually makes things worse
0.25
0
0 500 1000 1500 2,000
Scale
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27. Pre-requisites for Tipping
 To reach the tipping point,
 Algorithms must scale out horizontally
– On commodity hardware
– That can and will fail
 Data practice must change
– Denormalized is the new black
– Flexible data dictionaries are the rule
– Structured data becomes rare
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28. But there is more
Especially for large enterprises
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29. Physics of startup companies
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30. For startups
 History is always small
 The future is huge
 Must adopt new technology to survive
 Compatibility is not as important
– In fact, incompatibility is assumed
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31. Physics of large companies
Absolute growth
still very large
Startup
phase
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32. For large businesses
 Present state is always large
 Relative growth is much smaller
 Absolute growth rate can be very large
 Must adopt new technology to survive
– Cautiously!
– But must integrate technology with legacy
 Compatibility is crucial
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33. The startup technology picture
No compatibility
requirement
Old computers
and software
Expected hardware
and software growth
Current computers
and software
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34. The large enterprise picture
Must work
together
?
Current hardware
and software
Proof of concept
Hadoop cluster
Long-term Hadoop
cluster
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35. So that is why and why now
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36. So that is why, and why now
What can you do with it?
And how?
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37. Scale-free Computing
 Map-reduce
– pure functions for practical batch parallel computation
– high level languages like Hive and Pig available
– MapR provides standard access systems via NFS and ODBC
 BSP
– pure functions for synchronous iterative actor-based compute
– Apache Giraph provides practical implementation
 Actors
– tuple passing with transformations
– Storm provides practical implementation
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38. Future Proof Schemas
 Denormalize data where possible to avoid seeks
– use embedded lists
– duplicate data
 Flexible Schemas
– use standard system for data serialization
– must provide protocol migration without versioning
– Protobufs (Google), Avro (Apache) and Thrift can all be used
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39. Open Compute and Storage
 Big data has mass and inertia
– once it lands, it should not move
 Computation must move to the data
– map-reduce, Storm, Giraph … all OK
– conventional relational models … not OK
 One model is not enough
– must allow access by multiple models of computation
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40. More Information
 Contact:
– tdunning@maprtech.com
– @ted_dunning
 Slides and such:
– http://info.mapr.com/ted-paris-05-2012
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41. Thank You
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