This document summarizes a presentation about using Hadoop as an analytic platform. It discusses how Actian has added seven key ingredients to Hadoop to unlock its full potential for analytics. These include high-speed data integration, a visual framework for data science and modeling, open-source analytic operators, high-performance data processing engines, vector-based SQL processing natively on HDFS, an extremely fast parallel analytics engine, and a next-generation big data analytics platform. The goal is to transform Hadoop from merely a data reservoir to a fully-featured analytics platform.

1. Grab some
coffee and
enjoy the
pre-show
banter
before the
top of the
hour!

2. Hadoop as an Analytic Platform: Why Not?
The Briefing Room

3. Twitter Tag: #briefr
The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh

4. ! Reveal the essential characteristics of enterprise
software, good and bad
! Provide a forum for detailed analysis of today’s innovative
technologies
! Give vendors a chance to explain their product to savvy
analysts
! Allow audience members to pose serious questions... and
get answers!
Twitter Tag: #briefr
The Briefing Room
Mission

5. This Month: ANALYTIC PLATFORMS
November: DISCOVERY & VISUALIZATION
December: INNOVATORS
Twitter Tag: #briefr
The Briefing Room
Topics
2014 Editorial Calendar at
www.insideanalysis.com/webcasts/the-briefing-room

6. A NEW ERA of Architecture
Twitter Tag: #briefr
The Briefing Room
Executive Summary
Ø Don’t build
CARRIAGES for
highways
Ø Focus on NEW
opportunities
Ø SLOWLY ween off
old systems

7. Twitter Tag: #briefr
The Briefing Room
Analyst: William McKnight
William is President of McKnight Consulting Group. His
clients have included 17 of the Global 2000. Many
clients have gone public with their success story. His
team's implementations have won multiple Best
Practices awards. William is an Entrepreneur of the
Year Finalist, a frequent best practices judge and an
expert witness. He has hundreds of articles and dozens
of white papers in publication. William has also given
numerous keynote presentations worldwide at major
conferences and has given hundreds of public seminars
and webinars. William’s experience includes taking his
company to placement on the Inc. 500 and the Dallas
100 to seller of a multi-million dollar consulting firm.
He is a passionate communicator and motivator, and a
former IT VP of a Fortune 50 company.

8. Twitter Tag: #briefr
The Briefing Room
Actian
! Actian is a database and software development company
! The Actian Analytics Platform connects to data and Big Data
sources to perform actionable and advanced analytics
! Actian recently released Hadoop SQL Edition, a component
that enables SQL access on data stored in Hadoop

9. Twitter Tag: #briefr
The Briefing Room
Guest: Jim Hare
Jim Hare is Senior Director of Product Marketing
for the Actian Analytics Platform, helping
organizations transform big data into business
value. Prior to Actian, he was Director of
Marketing at IBM responsible for go-to-market
strategy and messaging for the big data
platform. Prior to joining IBM in 2008, Jim was
vice president of product marketing and
business development at Celequest, a
California-based operational business
intelligence vendor, which was acquired by
Cognos in 2007. He has over 16 years of deep
experience in enterprise software, business
intelligence, business process management, business activity monitoring, big data,
and automated software testing & monitoring. Jim holds a MS in Systems
Management from the University of Southern California, and an undergraduate
degree from the University of Colorado at Boulder.

10. Hadoop
as
an
Analy'c
Pla8orm:
Why
Not?
Jim
Hare
14
October
2014
Confiden'al
©
2014
10
Ac'an
Corpora'on

11. Ac'an
is
Delivering
Transforma'onal
Value
11 “Actian is now very powerfully
positioned in the big data and
analytics markets.” Robin Bloor
Confiden'al
©
2014
11
$140M Revenues + Profitable
10,000+ Customers
Global Presence: 8 world-wide offices, 7x 24 multinational support model
Ac'an
Corpora'on
“Actian has assembled all of the next generation
IPs into a single analytics platform, allowing
users a level of flexibility in data interaction that
competitors have not been able to match.”
siliconANGLE

12. Emergence
of
Hadoop
as
the
Data
Reservoir
Confiden'al
©
2014
12
Low
Cost
Storage
for
New
Data
&
Offload
Ac'an
Corpora'on

13. Benefits
of
Hadoop
Ø Scalable
-­‐
Ø Cost
effec.ve
–
Confiden'al
©
2014
13
store
large
data
sets
across
low
cost
servers
Ac'an
Corpora'on
1/10th
the
cost
of
tradi'onal
data
storage
Ø Flexible
-­‐
quickly
and
easily
land
any
data
in
raw
format
Ø Fast
Access
–'maps'
data
wherever
it
is
located
on
a
cluster
Ø Resilient
-­‐
data
is
replicated
to
other
nodes
in
the
cluster

14. Hadoop Deployment Survey Results
First
Solu.on
Deployed
10%
Pilot
First
14%
Solu.on
Confiden'al
©
2014
14
Suppor.ng
Mul.ple
Analy.cs
10%
Ac'an
Corpora'on
Only 22% of Hadoop
projects are in
production today!
14
8%
41%
16%
11%
Exploring
and
educa.ng
41%
16%
8%
14%
11%
Conduc.ng
POC
Developing
First
Solu.on
Source: SandHill Group Research, “How do you Hadoop? A
Survey of Big Data Practitioners”, May 2014

15. Because It isn’t Easy to Analyze Hadoop Data
Confiden'al
©
2014
15
Ac'an
Corpora'on
Batch
performance is
Slow
Lengthy Time to
Discover Insights
Expensive Skills
Lack of
Data
Access &
Security
Data preparation is
time-consuming
Analytics
Complexity

16. Organiza'ons
are
Replica'ng
Hadoop
Data
to
Overcome
Analy'c
Challenges
Confiden'al
©
2014
16
Ac'an
Corpora'on
Predic've
Rela'onal
Data
Store
OLTP,
ERP,
CRM
Unstructured
docs,
emails
Server
logs
Social/Web
data
Sensor,
machine
data
Geoloca'on
Clickstream
Discovery
Analy'cs
BI
Hadoop
• Duplicate
storage
&
infrastructure
costs
• More
IT
resources
to
manage
• Network
bandwidth
usage
• Less
accuracy
from
data
Sampling
• Slower
?me
to
analysis
results

17. Crea'ng
Transforma'onal
Value
from
Hadoop
Data
Data
Explosion
Confiden'al
©
2014
17
Ac'an
Corpora'on
Transforma.onal
Value
? Customer
Delight
Competitive
Advantage
World-Class Risk
Management
Disruptive New
Business Models
Actian Analytics PlatformTM
Time-­‐Sensi.ve
Analy.cs
Discovery
Analy.cs
Ac6an
Transforms
Hadoop
from
a
Data
Reservoir
into
a
High
Performance
Analy6cs
PlaCorm
! Highest
performing,
most
industrialized
Hadoop
analy'cs
pla8orm
! Only
end-­‐to-­‐end
analy.c
processing
na'vely
in
Hadoop
! Most
consumable,
accessible,
manageable
Hadoop
analy'cs
Analyze
Act
Connect
Hadoop

18. 7
Ingredients
Added
to
Hadoop
to
Unlock
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
Connect to Any
Data Anywhere
200+ Connectors
Confiden'al
©
2014
18
Ac'an
Corpora'on
Elastic and Secure
• Schedule
• Transform
• Validate
• Aggregate
• Reformat
• Join
• Orchestrate
ENERPRISE
Data
SOCIAL
Data
MACHINE
Data
CLOUD
Data
LEGACY
Data
DEVICE
Data
Embeddable
High Throughput
Engine
Drag and Drop
Workflow
Designs
Capture Data
Feeds in Batch
or Real-Time
Expandable
Plugin
Framework
High Volume
Parallel Data
Processing
source
and
any
type

19. 7
Ingredients
Add
to
Hadoop
to
Unlock
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
source
and
any
type
2. Visual
Framework
for
connec'ng,
blending,
&
enriching
data,
data
science
discovery,
building
and
tes'ng
predic've
models
Connect
Blend
&
Enrich
Discover
Build
&
Test
Models
Confiden'al
©
2014
19
Coding
Ac'an
Corpora'on

20. 7
Ingredients
Added
to
Hadoop
to
Unlock
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
source
and
any
type
2. Visual
Framework:
connec'ng,
blending,
&
enriching
data,
data
science
discovery,
building
and
tes'ng
predic've
models
3. 1500
KNIME
Operators
+
R
analy'cs
running
in
parallel
on
HDFS
+
Hadoop
=
The
Open
Source
Trifecta
Confiden'al
©
2014
20
Ac'an
Corpora'on
Gartner Magic
Quadrant for
Advanced Analytics
Platforms
Source: Gartner (February 2014)

21. Complete
End-­‐to-­‐End
Analy'cs
on
Hadoop
Confiden'al
©
2014
21
Ac'an
Corpora'on
Source:
2013
Rexer
Analy'cs
Survey

22. 7
Ingredients
Added
to
Hadoop
to
Unlock
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
source
and
any
type
2. Visual
Framework:
connec'ng,
blending,
&
enriching
data,
data
science
discovery,
building
and
tes'ng
predic've
models
3. 1500
KNIME
Operators
+
R
analy'cs
running
in
parallel
on
HDFS
+
Hadoop
=
The
Open
Source
Trifecta
4. High-­‐Performance,
YARN-­‐based
data
processing
engine
running
on
HDFS
Confiden'al
©
2014
22
Read Write Prepare Analyze Analyze Read Write
Ac'an
Corpora'on
On-Node Processing
Actian DataPrep
LEADER
Optimizer

23. High
Performance,
Parallelized
Processing
on
HDFS
Without
Any
Programming
Confiden'al
©
2014
23
Ac'an
Corpora'on
Ac.an
Analy.cs
PlaPorm
Query Pipelining
CPU Pipelining
Optimized, On-HDFS Processing
Hadoop – Leader Node
Reuse and share all
components from
operators to
workflows
Optimize
Choose from five sets
of operators:
Connections
Transformation
Data Quality
Analytics
Data Science
Automatically detect
resources, plan
optimal utilization,
and parallelize all
workloads on Hadoop
Use dual pipeline
parallelism to
accelerate
performance 30X
Take processing to
where the data lives,
runs natively on
Hortonworks
Run fully optimized
processing directly on
the Hadoop node via
YARN
Visual Framework
Manage the entire
analytic process in a
visual framework with
no coding required.
≠ ☼ ≡ ∞ Δ Σ √ ≈ Σ = ? # ~ ‰

24. 7
Ingredients
Added
to
Hadoop
to
Create
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
source
and
any
type
2. Visual
Framework:
connec'ng,
blending,
&
enriching
data,
data
science
discovery,
building
and
tes'ng
predic've
models
3. 1500
KNIME
Operators
+
R
analy'cs
running
in
parallel
on
HDFS
+
Hadoop
=
The
Open
Source
Trifecta
4. High-­‐Performance,
YARN-­‐based
data
processing
engine
running
on
HDFS
5. High-­‐Performance,
vector
processing
engine
as
the
pajern
for
SQL
on
Hadoop
Confiden'al
©
2014
24
Ac'an
Corpora'on

25. Vector-­‐based
SQL
Processing
Na'vely
on
HDFS
Confiden'al
©
2014
25
HADOOP
YARN
Datanode
Visual Data
& Analytics
Workbench
High Performance, Namenode
Industrialized SQL
X100 X100 X100
HDFS
HDFS
Ac'an
Corpora'on
Datanode
HDFS
Datanode
HDFS
Datanode
HDFS
Read
Load
Ac'an
Vector
Blend
&
Enrich
Data
Science
&
Analy'cs
Datanode
HDFS
X100
Database
High Performance,
Parallelized Data
Flow Engine
SQL
Industrialized
Standards - ANSI SQL 92 plus
advanced analytics
Optimized - mature, proven
planner and optimizer
Secure – native DBMS security
Reliable - full ACID-compliance
Manageable – YARN certified
Performance - 30X faster than
Impala
Scalable – unlimited expansion
as Hadoop cluster grows
Native – runs natively on top of
HDFS via YARN

26. 7
Ingredients
Added
to
Hadoop
to
Unlock
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
source
and
any
type
2. Visual
Framework:
connec'ng,
blending,
&
enriching
data,
data
science
discovery,
building
and
tes'ng
predic've
models
3. 1500
KNIME
Operators
+
R
analy'cs
running
in
parallel
on
HDFS
+
Hadoop
=
The
Open
Source
Trifecta
4. High-­‐Performance,
YARN-­‐based
data
processing
engine
running
on
HDFS
5. High-­‐Performance,
vector
processing
engine
as
the
pajern
for
SQL
on
Hadoop
6. Extreme-­‐Performance,
super-­‐low
latency,
massively
parallel
analy'cs
engine
Confiden'al
©
2014
26
Ac'an
Corpora'on

27. Ac'an
Analy'cs
Pla8orm:
Next
Genera'on
Big
Data
Analy'cs
Amazon
Redshift
Confiden'al
©
2014
27
Ac'an
Corpora'on
Actian Analytics PlatformTM
Libraries of Analytics
Visual Framework for Data and Analytic Workflows
Massively Parallel
Integration
Hadoop
Sophisticated,
Low Latency
Analytics in
Database
Connections for Any Data
Enterprise Data
Machine Data
Social Data
Users
Machines
Business
Processes
Applications
Data Warehouse
Analytic Services
Real-Time
SaaS Data
High
Performance
Data Science
Natively on
Hadoop

28. 7
Ingredients
Added
to
Hadoop
to
Unlock
Value
1. High
speed
integra'on
to
on-­‐board
data
from
any
data
source
and
any
type
2. Visual
Framework:
connec'ng,
blending,
&
enriching
data,
data
science
discovery,
building
and
tes'ng
predic've
models
3. 1500
KNIME
Operators
+
R
analy'cs
running
in
parallel
on
HDFS
+
Hadoop
=
The
Open
Source
Trifecta
4. High-­‐Performance,
YARN-­‐based
data
processing
engine
running
on
HDFS
5. High-­‐Performance,
vector
processing
engine
as
the
pajern
for
SQL
on
Hadoop
6. Extreme-­‐Performance,
super-­‐low
latency,
massively
parallel
analy'cs
engine
7. Blueprints
to
accelerate
analy'cs
applica'on
development
and
value
crea'on
Confiden'al
©
2014
28
Ac'an
Corpora'on

29. Big
Data
2.0
Media
Mix
Modeling
Blueprint
All Relevant Sales
Histories
Detailed ePOS
Receipts
Marketing Vehicle
Details
All Relevant
Account Info and
Demographics
Campaign Response
Notes
Confiden'al
©
2014
29
Ac'an
Corpora'on
MARKETING
IMPACT
ANALYSIS
IMPACT
FORECAST
ANALYSIS
CRMdb
CONNECT
BUILD
CUSTOMER
PROFILE
EDWdb
ANALYZE ACT
MAXIMIZE
REVENUE
FROM
MARKETING
SENTIMENT
AND
CONTENT
ANALYSIS
AGGREGATE
SALES
DATA
Hadoop
Logs
SKU LEVEL
SALES DATA
BY GEO
JOIN
DERIVE
AGGREGATE
PREPARE
EDWdb
CAPTURE
MARKETING
VEHICLES
MARKETING
MIX SALES
CONTRIBUTION
YEARLY CHANGE
REPORT
SALES
VOLUME,
EFFECTIVENES
S, EFFICIENCY
AND ROI
REPORT
NEW MEDIA
MIX
OPTIMIZATION
MINIMIZE
MARKETING
SPEND TO
REVENUE
RATIO
Hadoop
Text Files
CUSTOMER
MATCH
PREPARE FOR
TEXT
ANALYTICS
WITH
CAMPAIGNS
VEHICLE
RESULTS
AT GEO,
STORE, SKU
AND
CUSTOMER
LEVEL

30. Learn
more
about
the
Ac'an
Analy'cs
Pla8orm
–
Hadoop
SQL
Edi'on
1) Stay
tuned
for
several
exci'ng
announcements
on
16
October
2014
at
the
Strata
Conference
in
NYC!
2) Visit
Ac'an
at
Booth
Confiden'al
©
2014
30
Ac'an
Corpora'on
225
for
a
demo
of
Ac'an
Analy'cs
Pla8orm
-­‐
the
Highest
Performing
Analy'cs
&
SQL
in
Hadoop
3) Download
and
try
it
out
yourself:
bigdata.ac'an.com/sql-­‐in-­‐Hadoop

31. Thank
You
www.ac'an.com
facebook.com/ac'ancorp
@ac'ancorp
Confiden'al
©
2014
31
Ac'an
Corpora'on

32. Twitter Tag: #briefr
The Briefing Room
Perceptions & Questions
Analyst:
William McKnight

33. ANALYTICS: A BUSINESS
IMPERATIVE
Formed from SUMMARIES of data
Tied to Business Actions
Continual Re-evaluation
i.e., Customer Segmentation and Profit
Adding Big Data!

34. ANALYTICS EXAMPLES
Number of customers in each customer state (optionally by product or
multiple products)
Average balance of customers by geo
Average start date in each customer lifetime value decile by geo and device
New number of customers in each state
Propensity to churn by age band and device
Cost of acquisition by age and gender
Average session duration by cost of acquisition
Session duration differences between first and tenth session
Network with highest up time last month
Number of calls per session
Best performing ad network by day part in a geo, age band and device
And on and on and on and on….

35. ANALYTICS ACTION
35

36. SMARTER MARKETING
Spend + Media
Arbitrage
Opportunities +
Incremental Direct
Marketing Spend
Improvement:
Map Media Buys to the
Best Customer
Demographic
Do sponsorships align
with customer base?
Monitored transactions, renewals, customer care calls
Leveraged data to pitch right product, right time
Decrease in marketing cost
Increase in revenue, profit, customer satisfaction

37. VEHICLES FOR BIG DATA
Data Warehouse
Regional and
Departmental
Views
Applications
& Engines
Operational
Analytics &
Hot Views
Data Marts
ADS
Dependent
Independent
Relational
Data
Conformed
Dimensions

38. Last
Year
THE EVER-EXPANDING DATA WAREHOUSE
This
Year
Next
Year
• Enterprise Data Warehouse users
face huge annual upgrade
expenses
• To avoid this spend,
organizations are looking for
lower cost alternatives.
• Movement of data to tape not
desired, because data is offline
and not available for analytics
• Moving infrequently used data to
Hadoop is a cost-effective, online
option that preserves ability to
query
Cost

39. DATA WAREHOUSE EXPANSION
2
Offload data to
less expensive
Hadoop cluster to
save on data
management costs
As data
volume
increases
exponentially,
cost of
warehousing
rises also
Add operational data
for greater insight and
agility
in analytics and BI
4
1
Combine Hadoop data with
DW data for a more
comprehensive view of
history 3
HD
FS
HD
FS
HD
FS

40. QUESTIONS FOR ACTIAN
Where should analytics be created – in a relational environment or in Hadoop?
Where should they be analyzed? Do we have enough tools in a Hadoop
environment to do analysis there?
How do businesses analyze a combination of structured and unstructured
data?
Is it as simple as ‘structured data to the data warehouse or analytic one-offs
and unstructured data to Hadoop’?
Is using Hadoop as a data refinery the best use of Hadoop?
Does any data go to both environments? Or do just summaries get shared?
Can price/performance of a database vendor’s product be superior to an open
source product?

41. Twitter Tag: #briefr
The Briefing Room

42. This Month: ANALYTIC PLATFORMS
November: DISCOVERY & VISUALIZATION
December: INNOVATORS
Twitter Tag: #briefr
The Briefing Room
Upcoming Topics
2014 Editorial Calendar at
www.insideanalysis.com/webcasts/the-briefing-room
2015 Editorial Calendar coming soon!
www.insideanalysis.com

43. Twitter Tag: #briefr
THANK YOU
for your
ATTENTION!
Opening slide image courtesy of Wikimedia Commons
The Briefing Room