Top Trends for Hadoop in 2015

Confidential © 2014 Actian Corporation1 Confidential © 2015 Actian Corporation1
Top Trends for Hadoop in 2015
March 4, 2015

Top Trendsin Hadoop for 2015 - Forrester
Big Data Trends - Hortonworks
High performance analytics at scale in Hadoop - Actian
Agenda

John Kreisa
Vice President,
Strategic Marketing
Hortonworks
Mike Gualtieri
Principal Analyst
Forrester
Presenters
Christy Maver
Global Product
Marketing Director
Actian

Hadoop Trends And The Future Of
Analytics
Mike Gualtieri, Principal Analyst
March 4, 2015

© 2015 Forrester Research, Inc. Reproduction Prohibited 5
7%
16%
15%
18%
20%
24%
5%
12%
18%
17%
16%
33%
Social related projects
Mobile related projects
Cloud related projects
Systems of engagement
applications
Systems of record applications
Data related projects
2nd Priority
Top Priority
Please rank the following technologies according to their
importance and investment within your firm?
Executives and technology decision-
makers are remembering the power of
data.

Why?
Meet business demand for analytics of all kinds.

What are the main business and technical requirements or inadequacies of earlier-generation business
intelligence technologies that lead you to consider new BI techniques and technologies?
Base: 452 North American technology decision-makers
Respondents answering “don’t know” are not shown
Source: Global Data and Analytics Survey, 2014
Base: 249 North American business decision-makers
Respondents answering “don’t know” are not shown
Source: Global Data and Analytics Survey, 2014
Most want deeper insights through advanced analytics
but familar challenges persist.
2%
16%
20%
28%
29%
31%
32%
32%
34%
35%
45%
2%
12%
14%
26%
23%
35%
28%
31%
27%
33%
44%
Other (please specify)
Earlier-generation technology is too expensive
The velocity of data is too high for earlier technologies
The number of data formats that we must be able to deal with exceeds
our ability to cost-effectively integrate
Analysis requirements change too fast to keep up with
The performance of certain analysis is not sufficient
We don't know what our entire data universe contains, we need new ways
to explore data and discover patterns and insights
We want to access data that was not accessible for us with existing
technologies
Data changes or becomes available much faster than we can process in
support of business decisions
Data volumes have grown beyond what we can cost-effectively manage
We want deeper insights through advanced analytics
Business decision makers
Technology decision makers

Growth and customer experience are priorities
to business leaders

#Royalty

Trend
Consumers want to be treated like royalty.

Royalty experiences
have contextual
awareness and adapt to
serve a single consumer.

Treat consumers like royalty to get
their loyalty.

Behavior
Attributes
Activities
Relationships
Content
Usage
Social
Likes
Devices
Me

#Analytics

Businesses often think of analytics as a set of
historical reports and dashboards…

…but, analytics is also about the future.

It’s important to have all kinds of analytics.
Past Present Future
Learn React Anticipate
Predictive
Analytics
Real-time
Analytics
Descriptive
Analytics
(Traditional Analytics)

ANALYTICS
UBIQUITOUSInformation-driven business culture that uses
completely available analytics of all kinds to
continuously improve customer experience,
operations efficiency, and decision-making.

What percentage of enterprise data do firms
use for analytics?
A. 12%
B. 34%
C. 53%
D. 76%
Enterprise
Data
Quiz

#Hadoop

Gather all your data and do it cost effectively.
Data

Analyze the heck out of data - every which way.
Process

#DataOS

Hadoop adoption is not an option.

#EveryIndustry

Every industry is graced with more data…
› Richer transactional data from portfolio of dozens or
hundreds of business applications
› Usage and behavior data from web and mobile apps
› Social media data
› Sensor and event data from IoT devices
› Data economy – firms buying and selling data
› Derived data from analytics

Financial
Use Case
How can you prevent this dude from fleecing
you?

Financial Services
Use Case
What are movers and shakers saying about
equities that we cover?

How can you listen to customers to measure
share of voice, efficacy, and side effects?
Healthcare
Use Case

How can you know if your baby is sleeping
soundly or if something is wrong?
Healthcare
Use Case

What if you knew your customer was near your
store on a sunny day?
Retail Use
Case

How can show an ad that this household will
find relevant?
Retail Use
Case

#Trends

110010011011001
01001001101100
010011001101101
0100100110
1. Hadooponomics make enterprise adoption
mandatory.

Hadoop is an
enterprise
priority for 2015.

2. SQL becomes Hadoop’s killer app for 2015.

SQL must be blazing fast,
ANSI-compliant, and
optimized for Hadoop.

SELECT sum(S.commission)
FROM Customers C, Salesperson S
WHERE C.prodName = “SQL Edition’
AND S.salesperson = “You”

3. Enterprise software vendors close Hadoop’s
data management and governance gaps.

Ubiquitous analytics requires
an industrialized, end-to-end
analytics platform for
whereever data lives..

4. The Hadoop skills shortage disappears.

Predictive analytics
tools reduce need
for data scientists.
Analytics platform
operationalizes
analytics faster.
SQL is a
ubiquitous
skill.

5. Enterprises will let thousands of Hadoop
clusters bloom in the cloud.

Data often
originates in
the cloud.

6. Hadoop won’t just be for analytics anymore.

Applications must
embed applications
to be more personal
and contextual.

7. The Hadoop ecosystem standardizes.

Next
prediction.
Enterprises need a
portable set of
Hadoop services.

#Opportunity

Your customers’ customers’
are ready

Get ready
1. Walk through critical or challenging business processes
- At each step of the business process ask how analytics could
improve the process
2. Walk through customer journey to improve customer
experience
- At each step of the customer journey, ask how analytics could
improve the customer experience

#2015

Thank you
Mike Gualtieri
mgualtieri@forrester.com

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
CR
M
SC
M
New
Traditional

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
Application
Storage
HDFS
Batch Processing
MapReduce

OPERATIONAL TOOLS
DEV & DATA TOOLS
INFRASTRUCTURE
Hadoop is deeply integrated in the data centerSOURCES
EXISTING
Systems
Clickstream Web &Social Geolocation Sensor &
Machine
Server Logs Unstructured
DATASYSTEM
RDBMS EDW MPP
HANA
APPLICATIONS
BusinessObjects BI
Deep Partnerships
Hortonworks engages
in deep engineered relationships
with the leaders in the data center,
such as HP, Microsoft, Redhat, SAP,
SAS & Teradata
Broad Partnerships
Over 600 partners work with us to
certify their applications to work with
Hadoop so they can extend big data
to their users
HDP 2.1
Governance
&Integration
Security
Operations
Data Access
Data Management
YARN

Actian and the Modern Data Architecture
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
Actian Extends Hadoop’s Reach
• Unleashing SQL resources on Hadoop increases
value to customers
• Actian’s Native YARN offering excels at meeting
market demand for SQL
• Actian tools help to move POC to Production
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 Batch
M
PP
EDW

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

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

Hadoop Driver: Enabling the data lake
• 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.
SCALE
SCOPE
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

Case Study: 12 month Hadoop evolution at TrueCarDataPlatformCapabilities
12 months execution plan
June 2013
Begin
Hadoop
Execution
July 2013
Hortonworks
Partnership
May ‘14
IPO
Aug 2013
Training
& Dev
Begins
Nov 2013
Production
Cluster
60 Nodes
2 PB
Jan 2014
40% Dev
Staff
Perficient
Dec 2013
Three
Production
Apps
(3 total)
Feb 2014
Three More
Production
Apps
(6 total)
12 Month Results at TRUECar
• Six Production Hadoop Applications
• Sixty nodes/2PB data
• Storage Costs/Compute Costs
from $19/GB to $0.23/GB
“We addressed our data platform capabilities
strategically as a pre-cursor to IPO.”

Next Steps…
• Download the Hortonworks Sandbox
Learn Hadoop
Build Your Analytic App
Try Hadoop 2
More about Actian & Hortonworks
http://hortonworks.com/partner/Actian/
Contact us: events@hortonworks.com

High performance analytics at scale
in Hadoop
Christy Maver,
Global Product Marketing Director, Actian

What to look for in a SQL in Hadoop Platform
• Collaborative architecture
• Open access to Actian
formats
• Support for non-Actian
formats
No vendor lock-in
• Fastest data prep and
ingestion
• Fastest analytic engines
• Unbridled processing
power on data nodes in a
Hadoop cluster
• Full SQL support
• Extreme scalability
• Full security
• High Availability &
Disaster Recovery
Results you need when
you need them
Proven technology
advantages
Open Fast Enterprise-Grade

Actian Vortex – High Performance Analytics at Scale in
Hadoop

Fully ACID compliant – Get transactional
integrity in Hadoop to prevent
inaccurate results
What Sets Actian Vortex Apart from other SQL on Hadoop
offerings?
Full ANSI SQL 92 support – You can use
of ALL standard BI tools and apps
Native DBMS Security – Gain authentication,
user and role-based security, data protection,
and encryption
Open APIs – Your third-party ecosystem can gain
read access to our block format
Highly Performant – Get answers up to 30x
faster than possible with another offering
Mature, proven planner and fastest optimizer
– Maximize number of nodes, CPU, memory
and cacheHadoop distribution agnostic – Avoid vendor
lock-in
Native in-Hadoop YARN – Manage Hadoop
resources automatically to prevent inefficiencies
Collaborative architecture - Query native Hadoop
file formats (like Parquet) without ingestion
Update Capability – Update Hadoop data
without impacting read performance
Highest Concurrency – Deploy simultaneous
users and tasks without long wait times

Questions And Answers

Learn more
• Download Actian Vortex Express:
• Actian is joining the Hortonworks Roadshow – find out more:
o http://www.actian.com/resources/#UpcomingEventsbrWebinars
• Additional questions? Contact us at info@actian.com
Bigdata.actian.com/sql-in-hadoop

Top Trends for Hadoop in 2015

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