Big Data:Das zweite Jahr.

Joerg Blumtritt

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The Future of Market Research

Hardware

Traditional• exotic hardware• big central servers• SAN• RAID• hardware reliability• expensive• limited scalability

Big Data• commodity HW• racks of pizza boxes• Ethernet• JBOD• unreliable HW• cost effective• scales further

Software

Traditional• monolithic• centralized storage• RDBMS• schema first• proprietary

Big Data• distributed• storage & compute• nodes• raw data• open source

Quanti fication

VolumeVelocityVariety

DataScience

1. Volume– Very large data sets– Data Center → Data Warehouse → Internet Scale– Typical dimensions: billions or trillions of records, millions

or billions of variables– e.g. Twitter: > 400 M Tweets per day– Technologies: MapReduce, HDFS, Project Voldemort

... das erste V

Map-Reduce

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http://hadoop.apache.org/docs/r0.18.3/mapred_tutorial.html#Example%3A+WordCount+v2.0

1. Volume2. Velocity

– Very fast data streams– sensor data, smartphones, socia media:– Typical dimensions: 15k-300k/s– Real time inputs / real time outputs– Stream/event pocessing– Technologies: Storm, S4, Esper, HBase, Kafka

zweites V

Storm

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http://engineering.twitter.com/2011/08/storm-is-coming-more-details-and-plans.html

1. Volume2. Velocity3. Variety / Variability

– Manifold and highly variable data structures– data market places, e.g. Datasift, GNIP, Enigma.io– No schema / NoSQL– Distributed storage– Immutability

... und das letzte V

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Statt die Konsistenz der Daten schon in der Struktur festzulegen,wird eine Funktion definiert, die jeden Record nach den vorgegebenen Kriterien überprüft:

function IsConsistent(Record, Schema) as Boolean

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Operation SQL Create INSERT Read (Retrieve) SELECT Update (Modify) UPDATE Delete (Destroy) DELETE

"mutable"

"Each event happens at a particular time and is always true"

• Just C+R; nothing gets ever "updated"

• Records are stored as files. Each record is a new file.

"immutable"

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Query

Precomputed View(Batch Mode)

Data Stream

All Data

Precomputed realtime view

Quanti fication

VolumeVelocityVariety

DataScience

known knowns known unknowns unknowns unkonws

„data puking“(Dashboards)

„analysis throwing“(Modellings)

„data democracy“(Big Data)

Avinash Kaushik

As we know, There are known knowns. There are things we know we know. We also know There are known unknowns. That is to say: We know there are some things

We do not know. But there are also unknown unknowns, The ones we don't know We don't know.

Donald Rumsfeld

Data Science

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• Text comparism of party programmes

• Cosinus-Vector distance

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DSDSTatort

So 10.3.Sa 9.3.Fr 8.3.

Personahttp://twitter.com/FlaviaReil/statuses/308321057499144193http://twitter.com/froschmann1968/statuses/308321920200364034http://twitter.com/VeronikaTangen/statuses/308322141676388352http://twitter.com/froschmann1968/statuses/308322188501602304http://twitter.com/QWallyTy/statuses/308322522863128576http://twitter.com/Duftlavendel/statuses/308322911444406272http://twitter.com/kakakiri/statuses/308323144836456448http://twitter.com/Chake/statuses/308323468179566592http://twitter.com/RegulaAeppli/statuses/308323570386350083http://twitter.com/Imissmycat1/statuses/308323602342764544http://twitter.com/WorldNewsGerman/statuses/308323834749140995http://twitter.com/Zoran2010/statuses/308324446035386368

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männlichweiblichn.a.

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http://www.jasondavies.com/parallel-sets/

http://www.nytimes.com/interactive/2012/05/17/business/dealbook/how-the-facebook-offering-compares.html?_r=0

http://www.senchalabs.org/philogl/PhiloGL/examples/winds/

Quanti fication

VolumeVelocityVariety

DataScience

D3

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Quantified Self

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Digital Darwinismis the Evolution ofConsumer Behavior whenSociety & TechnologyEvolve FasterThan the AbilityTo Adapt

Brian Solis

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