1. Hadoop for the disillusioned
Steve Watt, Red Hat
CC flickr rubenswieringa
@wattsteve

2. @wattsteve

3. Wired Magazine - July 2008
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4. Hadoop in 2013
Platform Layers
Technologies
Computational
Runtimes
YARN, GiRAPH, MapReduce,
HBase, Phoenix, Spark/BDAS,
Drill, Impala, Stinger & more
FileSystems
Azure, CassandraFS, CephFS,
CleverSafe, GlusterFS, GridGain,
HDFS, Lustre
MapR FS, S3, SWIFT, Quantcast
FS, Symantec VCFS & more
Infrastructures
System on a Chip, x86,
Virtualization and Cloud
Distributions
Cloudera, Hortonworks, IBM,
Intel, MapR, WanDisco
CC flickr lowfatbrains
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5. Source: Gartner Hype Cycle
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6. Your data is growing beyond your ability to manage & query it
CC flickr kakadu
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7. Save money when asking the same questions of your data
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8. Hadoop Customer, “Great, but now what?”
Innovators
Early
Adopters
Early
Majority
Late
Majority
Laggards
CHASM
Geoffrey Moore’s Technology Adoption Lifecycle
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9. new
and build data products
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10. 
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Ask your domain experts and LOB folks what unanswered questions they have
Where can you get the data you need to answer that question? (domain experts should know
where to get it)
Some of this data may be outside your organization (Social Media, Sensor Data, Data
brokerages/Marketplaces, Web Pages) and some of it may be inside.
If the data for the query doesn’t exist, figure out how to instrument or gather it.
Pair your domain experts with your data engineers so they can work out how to obtain and
massage the data given the types of queries desired
CC flickr birdwatcher63
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11. • Building data products is a similar exercise except that it involves typical product planning,
such as identifying a market.
• This is also a great way for an organization to explore what assets they have within their data
CC flickr syume
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12. Mapping the night sky
CC flickr bobfamiliar
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13. Analyzing farm soil content
to predict human conflict
CC flickr oxfam
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14. Crisis Management for the
Chilean Earthquake
CC flickr flodigrip
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15. Thanks for listening
Steve Watt
swatt@redhat.com
@wattsteve