Hadoop is no longer optional. Companies of all sizes are in various phases of their own Big Data journey. Whether you are just starting to explore the platform or have multiple clusters up and running, everyone is presented with a similar challenge - developing their internal skillset. Hadoop specialists are hard to find. Hand coding is too prone to error when it comes to storing, integrating or analyzing your data. However, it doesn’t need to be this difficult. In this recorded webinar, Talend and Hortonworks help you learn how to unify all your data in Hadoop, with no specialized Big Data skills. Find the recording here. www.talend.com/resources/webinars/challenges-to-hadoop-adoption-if-you-can-dream-it-you-can-build-it This webinar covers: How Hadoop opens a new world of analytic applications, How to bridge the skills gap with our Big Data solutions, Experience a real-world, simple technical demo

1. 1
©2015 Talend Inc.
Challenges
to
Hadoop
Adop0on:
If
You
Can
Dream
It,
You
Can
Build
It
February 12, 2015

2. 2
Welcome
A
few
logis0cal
points..
• All
par0cipants
are
muted
• You
may
ask
ques0ons
using
the
Q&A
panel
located
on
boFom
or
GoToWebinar
applet
• Answers
will
be
provided
aJer
the
presenta0on
• If
0me
is
too
short
to
address
all
ques0ons,
answers
will
be
provided
via
email
• To
receive
a
replay
of
our
webinar
today,
please
send
us
an
email
to
webinar@talend.com
• If
you
are
experiencing
connec0on
problems,
please
use
the
Q&A
panel
to
communicate

3. 3
©2015 Talend Inc.
Challenges
to
Hadoop
Adop0on:
If
You
Can
Dream
It,
You
Can
Build
It
February 12, 2015

4. 4
Your
Speakers
Today
Jim Walker
Director, Product Marketing
Shawn James
Director, Alliances & Business Development
Mark Balkenende
Sr. Sales Solution Architect

5. Page 5 © 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.

6. Page 6 © 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

7. Page 7 © 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
ü Manages new data paradigm
ü Handles data at scale
ü Cost effective
ü 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

8. Page 8 © 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
Geoloca3on
Sensor
&
Machine
Server
Logs
Unstructured
SOURCES
Existing Systems
ERP
CRM
SCM
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

9. Page 9 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
Hadoop adoption follows a predictable journey
Cost Optimization, new analytic apps, and ultimately to a “data lake”

10. Page 10 © 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
Geoloca3on
Sensor
&
Machine
Server
Logs
Unstructured
Existing Systems
ERP
CRM
SCM
SOURCES

11. Page 11 © 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

12. Page 12 © 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

13. Page 13 © 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…

14. 14
Connec3ng the Data-­‐Driven Enterprise

15. 15
Main
Challenges
in
the
Data
Integra3on
Market
BIG
DATA
More data, less structure
PRODUCTIVITY
Can’t
keep
up
with
demand
COST
Expensive
solu3ons
SKILLS
Hard
to
find
talent

16. 16
The
Big
Data
Demand
4.4
MILLION
JOBS
IN
BIG
DATA
BY
2015
but
only
one
third
of
those
jobs
will
be
filled
Source: Gartner

17. 17
The
Hadoop
Ecosystem
is
Complex
Source:
“Hadoop
Ecosystem
Overview”,
Forrester
2014

18. 18
Talend
Brings
Unmatched
Produc3vity
HAND-­‐CODING
• Unproduc3ve
• Need specialized skills
• Hard to maintain
• Limited support
TALEND
ENTERPRISE
• 800+ components
• Generates op3mized code
• Collabora3on & management
• Gold support (SLAs)

19. 19
Future-­‐Proof
Architecture
With
Na3ve
Code
Gen
ETL
Day-­‐to-­‐day
integra3on
ELT
DW Appliance
ESB
Messaging, Rou3ng,
Transforma3on
HADOOP
Highly
Scalable
Spark

20. 20Select Icons made by Freepik, Situ Herrera, www.flaticon.com
Talend
Big
Data
Legacy
Systems
ERP
Internet of
Things
DBMS /
EDW
NoSQL
Standard
Reports
Ad-hoc
Query Tools
Data Mining
MDD/OLAP
Analytical
Applications
NoSQL
Web
Logs
Develop and Test Operations Team
Studio
Talend Big Data
Ingestion
Map Profile Parse Match
Cleanse Standardize
Change Data
Capture
Machine
Learning
Share Schedule
Native
Access
Future Proof Architecture
Lowest TCO
Increased Productivity
Benefits

21. 21
Easiest and Most Powerful Integra3on Solu3on for Big Data
Talend
Big
Data

22. 22
Main
Challenges
in
the
Data
Market
SCALABLE
AGILE
LOWEST
TCO
EASY

23. 23
1,800
Leading
Brands
Use
Talend
FINANCE & INSURANCE
SERVICES
MANUFACTURING
& RETAIL
PUBLIC SECTOR
& EDUCATION

24. 24
©2015 Talend Inc
Live
Demo

25. 25
Key
Takeaways
• See how Talend’s Big Data Pla[orm addresses the Skills Gap
• See how Talend will increase your Big Data Produc3vity
• Agree Talend and Hortonworks has the technology and skills to
sa3sfy your business requirements
BIG
DATA
More data, less structure
PRODUCTIVITY
Can’t
keep
up
with
demand
SKILLS
Hard
to
find
talent

26. 26
Demonstra0on
Use
Case
Objec3ve of the Use Case was to iden3fy data quality issues prior to loading data to the
EDW without increasing the actual load window.
• Load 500 TB Compressed Files to HFDS
- 3rd Party Sales/Prescribing files delivered by Vendor
• Compute Monthly Totals
- Prior to loading to EDW compare prior month’s totals to current Month totals within new data
files
• Display Comparison results in Analy3cal Tool
- Display total Sales comparison for each Product to quickly show Data Quality issues before
loading to EDW Staging

27. 27
Typical
3rd
Party
Data
Load
Data Preparation Warehouse Processing Final Reports / Quality Check
Bad Big Data Quality issues results in lost time, resource & revenue

28. 28
Data
Warehouse
Op0miza0on
Data Preparation Warehouse Processing Final Reports / Quality Check
Hadoop Cluster
ü Upfront Quality Checks
ü Identify Master records earlier
ü Load Uncompressed data
directly to DWH staging
Optimized Loading

29. 29
©2015 Talend Inc
Live
Demo

30. 30
What stood out most?
Recap
on
the
Demonstra0on?
• Hortonworks and Talend can help you reduce costs
• Offload costly ETL process
• Enrich the value of your EDW
• Graphical drag and drop visual environment showcasing
Talend and Hortonworks

31. 31
Hortonworks/Talend
Sandbox
• Graphical drag and drop visual environment showcasing Hortonworks
- Visually see the results of integra3on process
• Accelerates data loading and transforma3on with Hadoop
- Build and deploy MapReduce and Pig jobs on YARN
• Pre-­‐built use cases: data warehouse op3miza3on, clickstream data, Twiger sen3ment,
Apache weblogs
• Demonstra3ons of several NoSQL databases

32. 32
From
Zero
to
Big
Data
in
10
Minutes
Download free www.talend.com/hortonworks-­‐sandbox
• Get up and running in minutes, not weeks,
with a big data Sandbox and demos
• Includes: Sentiment analysis, ETL Offload,
Log file analysis
• Start working with Talend & Hortonworks
today!

33. 33
©2015 Talend Inc
Back
up
slides

34. 34
HDFS2
(Redundant,
Reliable
Storage)
YARN
(Cluster
Resource
Management)
BATCH
(MapReduce)
INTERACTIVE
(Tez)
STREAMING
(Storm,
Spark)
GRAPH
(Giraph)
NoSQL
(MongoDB)
Events
(Falcon)
ONLINE
(HBase)
OTHER
(Search)
TRANSFORM
(Data
Refinement)
PROFILE
PARSE
MAP
CDC
CLEANSE
STANDARD-­‐
IZE
MACHINE
LEARNING
MATCH
TAP
(Inges3on)
SQOOP
FLUME
HDFS
API
HBase
API
HIVE
800+
DELIVER
(as
an
API)
Ac3veMQ
Karaf
Camel
CXF
Kaca
Storm
Meta
Security
MDM
iPaaS
Govern
HA
Reference
Architecture