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©2015 Talend Inc.
Adoption de Hadoop :
Des possibilités illimitées
18 juin 2015
2
Equipe de présentateurs
Benjamin Boutros
Presales Channel Manager EMEA
Nicolas Maillard
Solution Engineer EMEA
Page3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop for the Enterprise: Implement a
Modern Data Architecture with HDP
Winter 2015
Version 1.0
Hortonworks. We do Hadoop.
Page4 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Traditional systems under pressure
Challenges
• Constrains data to app
• Can’t manage new data
• Costly to Scale
Business Value
Clickstream
Geolocation
Web Data
Internet of Things
Docs, emails
Server logs
2012
2.8 Zettabytes
2020
40 Zettabytes
LAGGARDS
INDUSTRY
LEADERS
1
2 New Data
ERP CRM SCM
New
Traditional
Page5 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop emerged as foundation of new data architecture
Apache Hadoop is an open source data platform for
managing large volumes of high velocity and variety of data
• Built by Yahoo! to be the heartbeat of its ad & search business
• Donated to Apache Software Foundation in 2005 with rapid adoption by
large web properties & early adopter enterprises
• Incredibly disruptive to current platform economics
Traditional Hadoop Advantages
 Manages new data paradigm
 Handles data at scale
 Cost effective
 Open source
Traditional Hadoop Had Limitations
Batch-only architecture
Single purpose clusters, specific data sets
Difficult to integrate with existing investments
Not enterprise-grade
Application
Storage
HDFS
Batch Processing
MapReduce
Page6 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Modern Data Architecture emerges to unify data & processing
Modern Data Architecture
• Enable applications to have access to
all your enterprise data through an
efficient centralized platform
• Supported with a centralized
approach governance, security and
operations
• Versatile to handle any applications
and datasets no matter the size or
type
Clickstream Web
& Social
Geolocation Sensor
& Machine
Server
Logs
Unstructured
SOURCES
Existing Systems
ERP CRM SC
M
ANALYTICS
Data
Marts
Business
Analytics
Visualization
& Dashboards
ANALYTICS
Applications
Business
Analytics
Visualization
& Dashboards
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
HDFS
(Hadoop Distributed File System)
YARN: Data Operating System
Interactive Real-TimeBatch Partner ISVBatch BatchMP
P
EDW
Page7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop adoption follows a predictable journey
Cost Optimization, new analytic apps, and ultimately to a “data lake”
Page8 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop Driver: Cost optimization
Archive Data off EDW
Move rarely used data to Hadoop as active
archive, store more data longer
Offload costly ETL process
Free your EDW to perform high-value functions
like analytics & operations, not ETL
Enrich the value of your EDW
Use Hadoop to refine new data sources, such as
web and machine data for new analytical context
ANALYTICS
Data
Marts
Business
Analytics
Visualization
& Dashboards
HDP helps you reduce costs and optimize the value associated with your EDW
ANALYTICSDATASYSTEMS
Data
Marts
Business
Analytics
Visualization
& Dashboards
HDP 2.2
ELT
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
N
Cold Data,
Deeper Archive
& New Sources
Enterprise
Data
Warehouse
Hot
MPP
In-Memory
Clickstream Web
& Social
Geolocation Sensor
& Machine
Server
Logs
Unstructured
Existing Systems
ERP CRM SC
M
SOURCES
Page9 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Single View
Improve acquisition and
retention
Predictive Analytics
Identify your next best action
Data Discovery
Uncover new findings
Financial
Services
New Account Risk Screens Trading Risk Insurance Underwriting
Improved Customer Service Insurance Underwriting Aggregate Banking Data as a Service
Cross-sell & Upsell of Financial Products Risk Analysis for Usage-Based Car Insurance Identify Claims Errors for Reimbursement
Telecom
Unified Household View of the Customer Searchable Data for NPTB Recommendations Protect Customer Data from Employee Misuse
Analyze Call Center Contacts Records Network Infrastructure Capacity Planning Call Detail Records (CDR) Analysis
Inferred Demographics for Improved Targeting Proactive Maintenance on Transmission Equipment Tiered Service for High-Value Customers
Retail
360° View of the Customer Supply Chain Optimization Website Optimization for Path to Purchase
Localized, Personalized Promotions A/B Testing for Online Advertisements Data-Driven Pricing, improved loyalty programs
Customer Segmentation Personalized, Real-time Offers In-Store Shopper Behavior
Manufacturing
Supply Chain and Logistics Optimize Warehouse Inventory Levels Product Insight from Electronic Usage Data
Assembly Line Quality Assurance Proactive Equipment Maintenance Crowdsource Quality Assurance
Single View of a Product Throughout Lifecycle Connected Car Data for Ongoing Innovation Improve Manufacturing Yields
Healthcare
Electronic Medical Records Monitor Patient Vitals in Real-Time Use Genomic Data in Medical Trials
Improving Lifelong Care for Epilepsy Rapid Stroke Detection and Intervention Monitor Medical Supply Chain to Reduce Waste
Reduce Patient Re-Admittance Rates Video Analysis for Surgical Decision Support Healthcare Analytics as a Service
Oil & Gas
Unify Exploration & Production Data Monitor Rig Safety in Real-Time Geographic exploration
DCA to Slow Well Declines Curves Proactive Maintenance for Oil Field Equipment Define Operational Set Points for Wells
Government
Single View of Entity CBM & Autonomic Logistic Analysis Sentiment Analysis on Program Effectiveness
Prevent Fraud, Waste and Abuse Proactive Maintenance for Public Infrastructure Meet Deadlines for Government Reporting
Hadoop Driver: Advanced analytic applications
Page10 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop Driver: Enabling the data lakeSCALE
SCOPE
Data Lake Definition
• Centralized Architecture
Multiple applications on a shared data set
with consistent levels of service
• Any App, Any Data
Multiple applications accessing all data
affording new insights and opportunities.
• Unlocks ‘Systems of Insight’
Advanced algorithms and applications
used to derive new value and optimize
existing value.
Drivers:
1. Cost Optimization
2. Advanced Analytic Apps
Goal:
• Centralized Architecture
• Data-driven Business
DATA LAKE
Journey to the Data Lake with Hadoop
Systems of Insight
Page11 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Challenges to Hadoop Adoption
• Where do I start? Why is this of value to me
and my organization?
• Hadoop is complex, what do I use for what?
• It is too complex. I don’t have any trained
Hadoop resources.
Many have been down this path…
12
Dynamiser l’entreprise par ses données
13
Les plus grands défis du marché de l’intégration de
données
BIG DATA
Plus de données, moins structurées
PRODUCTIVITE
Ne peut pas suivre la demande
COUT
Solutions onéreuses
COMPETENCES
Difficultés à trouver des compétences
14
La demande de Big Data
4.4 MILLIONS d’EMPLOIS
DANS LE BIG DATA EN 2015
mais seulement un tiers
de ces emplois seront pourvusSource: Gartner
15
L’écosystème Hadoop est complexe
Source : “Hadoop Ecosystem Overview”, Forrester 2014
16
Talend apporte une productivité inégalable
CODAGE à la MAIN
• Contre-productif
• Nécessite des
compétences spécifiques
• Difficile à maintenir
• Support limité
TALEND Big Data
• + de 800 composants
• Génère du code optimisé
• Collaboration & management
• Support Gold (SLAs)
30 X PLUS
PRODUCTIF
17
Architecture intemporelle avec génération de code
natif
ETL
Intégration
quotidienne
ELT
Data Warehouse
ESB
Messaging, Routing,
Transformation
HADOOP
Hautement
évolutif
La Grande
Nouveauté
Spark
18Select Icons made by Freepik, Situ Herrera, www.flaticon.com
Talend Big Data
Systèmes
hérités
ERP
Internet
des Objets
DBMS /
EDW
NoSQL
Rapports
standards
Outils de
requêtes ad-hoc
Data
Mining
MDD/OLAP
Applications
analytiques
NoSQL
Web Logs
Développe et teste Equipe opérations
Studio
Talend Big Data
Ingestion
Map Profile Parse Match
Nettoie Standardise
Change Data
Capture
Machine
Learning
Partage Planifie
Natif
Accès
Avantages
Productivité
améliorée
TCO plus bas
Future Proof
Architecture
19
La solution d’intégration de Big Data la plus facile et la plus puissante
Talend Big Data
Créer
Collaborer
DéployerGérer
Adapter
• Interface utilisateur visuelle, glisser-
déposer
• Plus de 800 connecteurs intégrés
• Génère du code MapReduce Java ou SQL
• S’exécute au niveau du
cluster
• Répartition de charge
et haute disponibilité
• Optimisation du code
• Aucune installation de Talend sur
Hadoop
• Nettoie et enrichie
• Supporte nativement Kerberos
• Supporte des consoles de gestion
Big Data
• Sécurité intégrée nativement
• Planification, monitoring et
gestion centralisés
• Référentiel partagé
• Auto-documentation
20
Les plus grands défis du marché de la donnée
EVOLUTIF AGILE
TCO plus basFACILE
21
Finance
et assurance
Services
Distribution
et industrie
Secteur public
et éducation
Une large base de clients
22
©2015 Talend Inc
Démonstration
23
Les points clés
• Talend Big Data Platform résout le problème des compétences
• Talend vous permet d’augmenter votre productivité Big Data
• Talend et Hortonworks ont la technologie et les compétences pour satisfaire
les besoins de votre entreprise.
BIG DATA
Plus de données, moins
structurées
PRODUCTIVITY
Ne peut pas suivre la demande
COMPETENCES
Difficulté de trouver des talents
24
Démonstration d’un cas d’usage
Objectif : identifier les problèmes de qualité de données avant de charger les données dans
l’entrepôt de données de l’entreprise sans augmenter le nombre de chargements en cours.
• Charger 500 TB de fichiers compressés dans HFDS
- Fichiers de ventes aux tiers/prescriptions délivrés par des fournisseurs
• Calculer les totaux mensuels
- Avant de charger dans la base de données, comparer les totaux des mois précédents aux
totaux du mois actuel dans de nouveaux fichiers de données.
• Afficher les résultats de ces comparaisons dans un outil analytique
- Afficher les comparaisons de ventes pour chaque produit pour montrer les problèmes de qualité de
données avant la mise en place du chargement dans la base de l’entreprise.
25
Chargement de données avec des tiers
Préparation des données
Traitement de la base
de données
Rapports finaux / Vérification
de la qualité
Les problèmes de mauvaise qualité des Big Data entraînent une perte
de temps, de ressources et de revenus
26
Optimisation de l’entrepôt de données
Cluster
Hadoop  Vérifications des données au préalable
 Identifier plus tôt les Master records
 Charger des données non-compressées
directement dans l’entrepôt de données
Chargement optimisé
Préparation des données
Traitement de la base
de données
Rapports finaux / Vérification
de la qualité
27
©2015 Talend Inc
Démonstration
28
Les points clés
Récap’ de la démonstration?
• Hortonworks et Talend peuvent vous aider à réduire vos coûts,
• Ils vous déchargent des processus ETL onéreux,
• Ils augmentent la valeur de votre entrepôt de données,
• Ils mettent à disposition un environnement visuel graphique
glissez-déposez.
29
Hortonworks/Talend Sandbox
• Environnement visuel graphique glissez-déposez mettant en avant Hortonworks
- Permet de montrer les résultats d’un travail d’intégration de façon visuelle
• Accélère le chargement de données et la transformation avec Hadoop
- Construire et déployer des jobs MapReduce et Pig dans YARN
• Cas d’utilisation préconstruits : optimisation des entrepôts de données, données de
parcours de clics, analyse sentimentale des données de Twitter, Analyse des weblogs
Apache
• Démonstrations de plusieurs bases de données NoSQL
30
De zero au Big Data en 10 minutes
Téléchargez la sandbox gratuite
fr.talend.com/hortonworks-sandbox
• Commencez en quelques minutes (pas en
semaines), avec une sandbox Big Data et une
démonstration
• Inclut : Une analyse de sentiments,
chargement ETL, analyse de fichiers Log
• Commencez à travailler avec Talend &
Hortonworks dès aujourd’hui !
33
©2015 Talend Inc.
Question & Réponses
34
©2015 Talend Inc.
Merci pour votre attention
A bientôt

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Adoption de Hadoop : des Possibilités Illimitées - Hortonworks and Talend

  • 1. 1 ©2015 Talend Inc. Adoption de Hadoop : Des possibilités illimitées 18 juin 2015
  • 2. 2 Equipe de présentateurs Benjamin Boutros Presales Channel Manager EMEA Nicolas Maillard Solution Engineer EMEA
  • 3. Page3 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Hadoop for the Enterprise: Implement a Modern Data Architecture with HDP Winter 2015 Version 1.0 Hortonworks. We do Hadoop.
  • 4. Page4 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Traditional systems under pressure Challenges • Constrains data to app • Can’t manage new data • Costly to Scale Business Value Clickstream Geolocation Web Data Internet of Things Docs, emails Server logs 2012 2.8 Zettabytes 2020 40 Zettabytes LAGGARDS INDUSTRY LEADERS 1 2 New Data ERP CRM SCM New Traditional
  • 5. Page5 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Hadoop emerged as foundation of new data architecture Apache Hadoop is an open source data platform for managing large volumes of high velocity and variety of data • Built by Yahoo! to be the heartbeat of its ad & search business • Donated to Apache Software Foundation in 2005 with rapid adoption by large web properties & early adopter enterprises • Incredibly disruptive to current platform economics Traditional Hadoop Advantages  Manages new data paradigm  Handles data at scale  Cost effective  Open source Traditional Hadoop Had Limitations Batch-only architecture Single purpose clusters, specific data sets Difficult to integrate with existing investments Not enterprise-grade Application Storage HDFS Batch Processing MapReduce
  • 6. Page6 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Modern Data Architecture emerges to unify data & processing Modern Data Architecture • Enable applications to have access to all your enterprise data through an efficient centralized platform • Supported with a centralized approach governance, security and operations • Versatile to handle any applications and datasets no matter the size or type Clickstream Web & Social Geolocation Sensor & Machine Server Logs Unstructured SOURCES Existing Systems ERP CRM SC M ANALYTICS Data Marts Business Analytics Visualization & Dashboards ANALYTICS Applications Business Analytics Visualization & Dashboards ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° HDFS (Hadoop Distributed File System) YARN: Data Operating System Interactive Real-TimeBatch Partner ISVBatch BatchMP P EDW
  • 7. Page7 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Hadoop adoption follows a predictable journey Cost Optimization, new analytic apps, and ultimately to a “data lake”
  • 8. Page8 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Hadoop Driver: Cost optimization Archive Data off EDW Move rarely used data to Hadoop as active archive, store more data longer Offload costly ETL process Free your EDW to perform high-value functions like analytics & operations, not ETL Enrich the value of your EDW Use Hadoop to refine new data sources, such as web and machine data for new analytical context ANALYTICS Data Marts Business Analytics Visualization & Dashboards HDP helps you reduce costs and optimize the value associated with your EDW ANALYTICSDATASYSTEMS Data Marts Business Analytics Visualization & Dashboards HDP 2.2 ELT ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N Cold Data, Deeper Archive & New Sources Enterprise Data Warehouse Hot MPP In-Memory Clickstream Web & Social Geolocation Sensor & Machine Server Logs Unstructured Existing Systems ERP CRM SC M SOURCES
  • 9. Page9 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Single View Improve acquisition and retention Predictive Analytics Identify your next best action Data Discovery Uncover new findings Financial Services New Account Risk Screens Trading Risk Insurance Underwriting Improved Customer Service Insurance Underwriting Aggregate Banking Data as a Service Cross-sell & Upsell of Financial Products Risk Analysis for Usage-Based Car Insurance Identify Claims Errors for Reimbursement Telecom Unified Household View of the Customer Searchable Data for NPTB Recommendations Protect Customer Data from Employee Misuse Analyze Call Center Contacts Records Network Infrastructure Capacity Planning Call Detail Records (CDR) Analysis Inferred Demographics for Improved Targeting Proactive Maintenance on Transmission Equipment Tiered Service for High-Value Customers Retail 360° View of the Customer Supply Chain Optimization Website Optimization for Path to Purchase Localized, Personalized Promotions A/B Testing for Online Advertisements Data-Driven Pricing, improved loyalty programs Customer Segmentation Personalized, Real-time Offers In-Store Shopper Behavior Manufacturing Supply Chain and Logistics Optimize Warehouse Inventory Levels Product Insight from Electronic Usage Data Assembly Line Quality Assurance Proactive Equipment Maintenance Crowdsource Quality Assurance Single View of a Product Throughout Lifecycle Connected Car Data for Ongoing Innovation Improve Manufacturing Yields Healthcare Electronic Medical Records Monitor Patient Vitals in Real-Time Use Genomic Data in Medical Trials Improving Lifelong Care for Epilepsy Rapid Stroke Detection and Intervention Monitor Medical Supply Chain to Reduce Waste Reduce Patient Re-Admittance Rates Video Analysis for Surgical Decision Support Healthcare Analytics as a Service Oil & Gas Unify Exploration & Production Data Monitor Rig Safety in Real-Time Geographic exploration DCA to Slow Well Declines Curves Proactive Maintenance for Oil Field Equipment Define Operational Set Points for Wells Government Single View of Entity CBM & Autonomic Logistic Analysis Sentiment Analysis on Program Effectiveness Prevent Fraud, Waste and Abuse Proactive Maintenance for Public Infrastructure Meet Deadlines for Government Reporting Hadoop Driver: Advanced analytic applications
  • 10. Page10 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Hadoop Driver: Enabling the data lakeSCALE SCOPE Data Lake Definition • Centralized Architecture Multiple applications on a shared data set with consistent levels of service • Any App, Any Data Multiple applications accessing all data affording new insights and opportunities. • Unlocks ‘Systems of Insight’ Advanced algorithms and applications used to derive new value and optimize existing value. Drivers: 1. Cost Optimization 2. Advanced Analytic Apps Goal: • Centralized Architecture • Data-driven Business DATA LAKE Journey to the Data Lake with Hadoop Systems of Insight
  • 11. Page11 © Hortonworks Inc. 2011 – 2015. All Rights Reserved Challenges to Hadoop Adoption • Where do I start? Why is this of value to me and my organization? • Hadoop is complex, what do I use for what? • It is too complex. I don’t have any trained Hadoop resources. Many have been down this path…
  • 13. 13 Les plus grands défis du marché de l’intégration de données BIG DATA Plus de données, moins structurées PRODUCTIVITE Ne peut pas suivre la demande COUT Solutions onéreuses COMPETENCES Difficultés à trouver des compétences
  • 14. 14 La demande de Big Data 4.4 MILLIONS d’EMPLOIS DANS LE BIG DATA EN 2015 mais seulement un tiers de ces emplois seront pourvusSource: Gartner
  • 15. 15 L’écosystème Hadoop est complexe Source : “Hadoop Ecosystem Overview”, Forrester 2014
  • 16. 16 Talend apporte une productivité inégalable CODAGE à la MAIN • Contre-productif • Nécessite des compétences spécifiques • Difficile à maintenir • Support limité TALEND Big Data • + de 800 composants • Génère du code optimisé • Collaboration & management • Support Gold (SLAs) 30 X PLUS PRODUCTIF
  • 17. 17 Architecture intemporelle avec génération de code natif ETL Intégration quotidienne ELT Data Warehouse ESB Messaging, Routing, Transformation HADOOP Hautement évolutif La Grande Nouveauté Spark
  • 18. 18Select Icons made by Freepik, Situ Herrera, www.flaticon.com Talend Big Data Systèmes hérités ERP Internet des Objets DBMS / EDW NoSQL Rapports standards Outils de requêtes ad-hoc Data Mining MDD/OLAP Applications analytiques NoSQL Web Logs Développe et teste Equipe opérations Studio Talend Big Data Ingestion Map Profile Parse Match Nettoie Standardise Change Data Capture Machine Learning Partage Planifie Natif Accès Avantages Productivité améliorée TCO plus bas Future Proof Architecture
  • 19. 19 La solution d’intégration de Big Data la plus facile et la plus puissante Talend Big Data Créer Collaborer DéployerGérer Adapter • Interface utilisateur visuelle, glisser- déposer • Plus de 800 connecteurs intégrés • Génère du code MapReduce Java ou SQL • S’exécute au niveau du cluster • Répartition de charge et haute disponibilité • Optimisation du code • Aucune installation de Talend sur Hadoop • Nettoie et enrichie • Supporte nativement Kerberos • Supporte des consoles de gestion Big Data • Sécurité intégrée nativement • Planification, monitoring et gestion centralisés • Référentiel partagé • Auto-documentation
  • 20. 20 Les plus grands défis du marché de la donnée EVOLUTIF AGILE TCO plus basFACILE
  • 21. 21 Finance et assurance Services Distribution et industrie Secteur public et éducation Une large base de clients
  • 23. 23 Les points clés • Talend Big Data Platform résout le problème des compétences • Talend vous permet d’augmenter votre productivité Big Data • Talend et Hortonworks ont la technologie et les compétences pour satisfaire les besoins de votre entreprise. BIG DATA Plus de données, moins structurées PRODUCTIVITY Ne peut pas suivre la demande COMPETENCES Difficulté de trouver des talents
  • 24. 24 Démonstration d’un cas d’usage Objectif : identifier les problèmes de qualité de données avant de charger les données dans l’entrepôt de données de l’entreprise sans augmenter le nombre de chargements en cours. • Charger 500 TB de fichiers compressés dans HFDS - Fichiers de ventes aux tiers/prescriptions délivrés par des fournisseurs • Calculer les totaux mensuels - Avant de charger dans la base de données, comparer les totaux des mois précédents aux totaux du mois actuel dans de nouveaux fichiers de données. • Afficher les résultats de ces comparaisons dans un outil analytique - Afficher les comparaisons de ventes pour chaque produit pour montrer les problèmes de qualité de données avant la mise en place du chargement dans la base de l’entreprise.
  • 25. 25 Chargement de données avec des tiers Préparation des données Traitement de la base de données Rapports finaux / Vérification de la qualité Les problèmes de mauvaise qualité des Big Data entraînent une perte de temps, de ressources et de revenus
  • 26. 26 Optimisation de l’entrepôt de données Cluster Hadoop  Vérifications des données au préalable  Identifier plus tôt les Master records  Charger des données non-compressées directement dans l’entrepôt de données Chargement optimisé Préparation des données Traitement de la base de données Rapports finaux / Vérification de la qualité
  • 28. 28 Les points clés Récap’ de la démonstration? • Hortonworks et Talend peuvent vous aider à réduire vos coûts, • Ils vous déchargent des processus ETL onéreux, • Ils augmentent la valeur de votre entrepôt de données, • Ils mettent à disposition un environnement visuel graphique glissez-déposez.
  • 29. 29 Hortonworks/Talend Sandbox • Environnement visuel graphique glissez-déposez mettant en avant Hortonworks - Permet de montrer les résultats d’un travail d’intégration de façon visuelle • Accélère le chargement de données et la transformation avec Hadoop - Construire et déployer des jobs MapReduce et Pig dans YARN • Cas d’utilisation préconstruits : optimisation des entrepôts de données, données de parcours de clics, analyse sentimentale des données de Twitter, Analyse des weblogs Apache • Démonstrations de plusieurs bases de données NoSQL
  • 30. 30 De zero au Big Data en 10 minutes Téléchargez la sandbox gratuite fr.talend.com/hortonworks-sandbox • Commencez en quelques minutes (pas en semaines), avec une sandbox Big Data et une démonstration • Inclut : Une analyse de sentiments, chargement ETL, analyse de fichiers Log • Commencez à travailler avec Talend & Hortonworks dès aujourd’hui !
  • 32. 34 ©2015 Talend Inc. Merci pour votre attention A bientôt