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1

AN ADVENTUROUS JOURNEY
THROUGH THE FIELD OF

BIG DATA
P ROGRAMME


Concept de ma thèse


Étude de la littérature



Cas: FOD Justice



Cas: Adswizz



Démo



Conclusion

2
3

C ONCEPT DE MA THÈSE


M’initier au Big Data



Expérimenter avec les machines virtuelles (HortonWorks)





Contacts externes, événements, Médias sociaux, webinars
Réunions sur BigData.be (+pratique)

Projet


FOD Justice



SAS Visual Analytics



UITDAGING

Configurer un environnement de Big Data (HDP)



INNOVATIE

Adswizz (‘réserve’)

OPPORTUNITEIT
4

T HESIS


Data aspect


Volume, variëteit en velocity





External data

Technological aspect




HADOOP

MOBILE

Distributed (HDFS/MapReduce)

Analytical aspect



Prescriptive

Externe
data

Predictive

CLOUD

SOCIAL MEDIA
T HESIS


Customer churn



Fraudulent transactions



Customer insight



Discover new patterns between data

5
T HESIS
1.

Research

2.

Formulate opportunities

3.

Develop use case(s)

4.

Identify requirements

5.

Set-up testing environment

6.

Evaluate results

6
7

C ASE : FOD J USTITIE
Duidelijke afspraken

2.

Scope afgebakend

3.

Project goedgekeurd

4.

Veelheid aan procedures

5.

Moeilijke communicatie

6.

SAS Visual Analytics

“

PROCEDURES
COMMUNICATIE

A man’s errors are his portals of discovery.
by James Joyce

“

1.
CASE: A DSWIZZ


Injectie advertenties



Logfiles



75GB  750GB/maand



Amazon S3 & EMR



WEBLOGS

Internet radio webstreams



8

Pig scripts

SCRIPTS

Skill to do comes from doing.
by Ralph Waldo Emerson

“

“

PIG
9

DEMO
by Anton Chekhov

“

“

Knowledge is of no value unless
you put it into practice.
B ESLUIT
A LG E M E E N :


Big Data is een ‘hot’ topic



Uitbreiding op klassieke Business Intelligence



Wordt steeds belangrijker



Vereist nieuwe kennis (Analytics, Pig/Hive, Linux, … )



Goede voorbereiding en klein beginnen

P E RS O O N L I J K :


Boeiende en leerrijk traject



Communicatie, Toepassing Big Data, PigQL, SAS Programming & VA



Ontwikkeling professionele attitude



Aan een interessante & uitdagende job geholpen

10
11

VRAGEN?
by George Bernard Shaw

“

“

No question is so difficult to answer as
that to which the answer is obvious.

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Presentation Thesis Big Data

  • 1. 1 AN ADVENTUROUS JOURNEY THROUGH THE FIELD OF BIG DATA
  • 2. P ROGRAMME  Concept de ma thèse  Étude de la littérature  Cas: FOD Justice  Cas: Adswizz  Démo  Conclusion 2
  • 3. 3 C ONCEPT DE MA THÈSE  M’initier au Big Data   Expérimenter avec les machines virtuelles (HortonWorks)   Contacts externes, événements, Médias sociaux, webinars Réunions sur BigData.be (+pratique) Projet  FOD Justice  SAS Visual Analytics  UITDAGING Configurer un environnement de Big Data (HDP)  INNOVATIE Adswizz (‘réserve’) OPPORTUNITEIT
  • 4. 4 T HESIS  Data aspect  Volume, variëteit en velocity   External data Technological aspect   HADOOP MOBILE Distributed (HDFS/MapReduce) Analytical aspect   Prescriptive Externe data Predictive CLOUD SOCIAL MEDIA
  • 5. T HESIS  Customer churn  Fraudulent transactions  Customer insight  Discover new patterns between data 5
  • 6. T HESIS 1. Research 2. Formulate opportunities 3. Develop use case(s) 4. Identify requirements 5. Set-up testing environment 6. Evaluate results 6
  • 7. 7 C ASE : FOD J USTITIE Duidelijke afspraken 2. Scope afgebakend 3. Project goedgekeurd 4. Veelheid aan procedures 5. Moeilijke communicatie 6. SAS Visual Analytics “ PROCEDURES COMMUNICATIE A man’s errors are his portals of discovery. by James Joyce “ 1.
  • 8. CASE: A DSWIZZ  Injectie advertenties  Logfiles  75GB  750GB/maand  Amazon S3 & EMR  WEBLOGS Internet radio webstreams  8 Pig scripts SCRIPTS Skill to do comes from doing. by Ralph Waldo Emerson “ “ PIG
  • 9. 9 DEMO by Anton Chekhov “ “ Knowledge is of no value unless you put it into practice.
  • 10. B ESLUIT A LG E M E E N :  Big Data is een ‘hot’ topic  Uitbreiding op klassieke Business Intelligence  Wordt steeds belangrijker  Vereist nieuwe kennis (Analytics, Pig/Hive, Linux, … )  Goede voorbereiding en klein beginnen P E RS O O N L I J K :  Boeiende en leerrijk traject  Communicatie, Toepassing Big Data, PigQL, SAS Programming & VA  Ontwikkeling professionele attitude  Aan een interessante & uitdagende job geholpen 10
  • 11. 11 VRAGEN? by George Bernard Shaw “ “ No question is so difficult to answer as that to which the answer is obvious.

Editor's Notes

  1. Bonsoir tout le monde, je m’appelle Natan et le dernière neuf mois j’ai travaillé sur le topique de ‘Big Data’. C’est faisait une voyage aventureux qui j’ai beaucoup d’appris et je suis vraiment content j’ai choisis ce topique. Les dix minutes prochain je vais vous donnez un peu plus d’information comme ma thèse et les projets que j’ai travaillé.
  2. IntroductionPe programme pour ce soir: je vais vous expliquer le concept de ma thèse, quelque chose des projet lesquelles j’ai travaillé et vous donnez un démo de ‘SAS Visual Analytics’ et Pig scripts. Je vais finir avec un conclusion.
  3. L’objectifinitialétaitd’étudier de manièrethéorique , de me documenter et d’étudierle monde des Big Data et d’appliquer mes connaissancesthéoriques à unprojetconcretchez FOD Justice. Pour réalisercelaj’aiparticipé à des événements Big Data, j’y ai interagi, j’aisuivi des Webinars, j’aiconsulté en détails les médiassociaux et j’ai fait intervenir des contactsexternes. Ce sontvraimentles meilleurschemins pour en apprendre plus sur les Big Data et vraimentcomprendre les aspectsconcrets et pratiquesliésaux Big Data.Au ministère de la Justice, ma mission était de construireunenvironnementaveccinqserveurs et la plateforme de donnés de HortonWorks (HDP) avec comme but de transformer les données. Ma mission étaitaussi de travaillersurleserveurdémo de SAS Visual Analytics.