No Time-Outs: How to Empower Round-the-Clock Analytics

Eric.kavanagh@bloorgroup.com

Twitter Tag: #briefr 8/28/12

!   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

!   August: Analytics
!   September: Integration
!   October: Database
!   November: Cloud
!   December: Innovators


!  Analytics has always been about discovering
insights that lead to better business decisions.

!  More organizations are demanding faster time-to-
insight, while at the same time expecting
connectivity to and analytics on a wide variety of
data and data sources.

!  Clever vendors look for ways to provide solutions
that not only scale at lightening speeds, but
deliver actionable insights.


Robin Bloor is
Chief Analyst at
The Bloor Group.

Robin.Bloor@Bloorgroup.com


Rick Sherman is the founder of Athena IT
Solutions, a Massachussetts-based firm that
provides business intelligence, data
integration & data warehouse consulting,
training and vendor services.

In addition to having more than 20 years of
experience in BI solutions, Rick writes on IT
topics and is a frequent speaker at industry
events.

He blogs at The Data Doghouse and can be
reached at:

rsherman@athena-solutions.com


! Actian Corporation is a database and software
development company.

!   Its premier database platform is Vectorwise, a highly
performant analytic engine that implements
parallelism at every level, from the processor core to
data storage.

! Actian offers a cloud development platform for
building Action Apps, lightweight applications that
automate business actions triggered by real-time
changes in data.


Fred Gallagher is GM, Vectorwise at Actian
Corporation and is responsible for managing the
business activities for this breakthrough product. He
joined Actian in 2006 as vice president of business
development.

Before joining Actian, Fred worked for Qlusters,
where he was responsible for worldwide sales,
marketing, and business development. At Qluster, he
successfully launched the industry's first open source
systems management project. Prior to that, he
worked at VMWare, where he was responsible for
worldwide software alliances, and where he
established 15 successful strategic alliances during a
high-growth period of two years. Previously he was
at Seagate Technology, where he was vice president
of worldwide channels and business development for
Seagate's XIOtech subsidiary. Fred holds Bachelor of
Arts and an MBA from Stanford University.


Briefing Room
August 28

Speakers:
Rick Sherman
Fred Gallagher, General Manager Vectorwise, Actian Corporation

Actian Today

•  Global
Reach,
Growing
with
Strong
Balance
sheet

•  Highly
Profitable
with
strong
Cash
Balances

•  200
employees
across
11
Offices

•  World’s
Fastest

Big
Data
Analy5cal
Engine

Vectorwise
•  Experiencing
RAPID
GROWTH

•  Affordable,
Leverages
Standard
Hardware
and
SoHware

•  10,000+
mission
cri5cal
applica5ons
–
inc
Data
Warehouses

Ingres
•  Very
high
client
sa5sfac5on

•  S5ll
Innova5ng:
GeoSpa5al
features

•  Next
Genera5on
of
Ac5onable
BI
–
Connect
Insight
to
Ac5on

Ac5on
Apps
•  Analyzing
Events
and
Data

12

But
most
enterprises
s5ll
only
use
1-‐5%
of

their
data

What
if
that
number
doubled,
tripled,
quadrupled…
?

Source:
Forrester

Enormous Opportunities for Big Data

$300bn
value
per

year
€250
bn
value
per
$600
bn
value

year
per
year

SOURCE:
McKinsey
Global
InsNtute
analysis

14

“Big” Data Management Challenge

Local Data

Data silos, distributed and disparate
! 

Existing solutions are not real-time
! 

Data in the Cloud
Expensive, inefficient or inflexible
! 

Scalability not there
! 

15

The Need for Speed and Agility

!  Business users require interactivity
!   Desktop users tolerate 10 to 20 seconds

!   Mobile users tolerate 2 to 5 seconds

!  Increases in user concurrency

!  Take action in “business time”

!   Be faster than your competition

!   Optimize your business

16

Reduce Latency Between Events and Action

Event

Latency

Capture

Value

Latency

Analyze

Informed
decision
ready

Latency
to
be
made

A*ribu/on:

Ac5on
Jean-‐Michel
Franco
of

Business
&
Decision

Time
latencies

17

Vectorwise:
Affordable Performance – Proven!
QphH 0

100,000
200,000
300,000
400,000

June
‘12
Vectorwise

445,529

May
‘11
Vectorwise

436,788

Aug
‘11
SQL
Server

219,888

June
‘11
Oracle

209,534

Sept
‘11
Oracle

201,487

Apr
‘11
SQL
Server

173,962

Dec
‘10
Sybase
IQ

164,747

Apr
‘10
Oracle

140,181

Dec
‘11
SQL
Server

134,117

Fastest TPC-H QphH@1TB Benchmark (non-clustered)
Source:
www.tpc.org
/
June
15,
2012

18

Vectorwise:
Affordable Performance – Proven!
QphH 0

100,000
200,000
300,000
400,000

Hardware
Cost

(excluding
discounts)

June
‘12
Vectorwise

445,529
$57,146

May
‘11
Vectorwise

436,788
$85,621

Aug
‘11
SQL
Server

219,888
$460,869

June
‘11
Oracle

209,534
$2,402,706

Sept
‘11
Oracle

201,487
$753,392

Apr
‘11
SQL
Server

173,962
$278,527

Dec
‘10
Sybase
IQ

164,747
$1,229,968

Apr
‘10
Oracle

140,181
$1,249,967

Dec
‘11
SQL
Server

134,117
$258,880

Fastest TPC-H QphH@1TB Benchmark (non-clustered)
Source:
www.tpc.org
/
June
15,
2012

19

Vectorwise Technology
1
3

Vector
Processing

2nd
Gen
Column
Store

Single

Limit
I/O

InstrucNon
Eﬃcient
real
Nme
updates

MulNple

Data

4

2
Smarter
Compression

On
Chip
CompuNng

Maximize
throughput

Millions

DISK

Vectorized
decompression

Time / Cycles to Process

RAM

150-‐250

5

CHIP

MulN-‐core
Parallelism

2-‐20

…

40-‐400MB
2-‐3GB
10GB

Maximize
system
resource

Data Processed
uNlizaNon
Process
data
on
chip
–
not
in
RAM

Confidential © 2012 Actian Corporation 20

Customer Stories: Sheetz and Zoho

! Leader in Convenience Stores ! SaaS Company with 6 million Users
!  Problem !  Problem and Requirements
!   Customer data growing rapidly
!   Multiple data sources
!   Ease of use for self-service BI
!   Need to control costs
!   Affordability
!   Huge data growth
!   200,000 users of Zoho Reports
!  Vectorwise results Vectorwise results
!   Expand data to analyze two years !   Exceptional performance
!   Manage growth for three years !   Affordability for a SaaS offering

21

Hadoop/Vectorwise Production Use Cases

NK
! 

!   Social media
!  Optimize user experience and ad revenue

!   250 TB in Hadoop
!   Extracting ~5 TB into Vectorwise


Customer Stories: Badoo

! Fastest Growing Social Network
!  Problem
!   Limited slice and dice analytics

!   Better target ad campaigns

!   Huge data growth

!  Vectorwise results
!   Detailed answers in seconds

!   Immediate actions

23

Summary

!  Successful businesses require speed and agility

!  BI solutions must address these requirements

!  Recommendations for how to get started and succeed:

!   Align IT goals and organization with user needs and business goals

!   Include operational processes in requirements (business and IT)

!   POC with Vectorwise for affordable performance and scalability

24

For more information and to download a free trial of
Vectorwise visit: www.actian.com/vectorwise

Contact us at:

1 877-644-6343

learnmore@actian.com

Join the conversation:

Twitter: @Actiancorp #Vectorwise


The Changing Face of Analytics: How to Stay Ahead
State of Data & Analytics

•  Data Exploding (sometimes Big Data)
ü  Volume: ever accelerating data
û  90% of data in world created in last two years
ü  Velocity: time-sensitive, real-time
ü  Variety: structured & unstructured data such as:
text, audio, video, click streams, log files

•  Analytics has Expanded
ü  Pre-built reports
ü  Business Graphics, Trending, Drill-down to Details
ü  Spreadsheets pervasive
ü  Predictive Analytics
ü  Data Visualization
ü  Operational BI
ü  Mobile BI
ü  Self-Service BI

Slide 28 Copyright © 2012 Athena IT Solutions All rights reserved.

Response to BI Demand

•  Infrastructure
ü  Software: Database, ETL, BI
ü  Traditional servers dedicated to above
ü  Storage: local, SAN, NAS

•  Data Architecture
ü  Source Systems
ü  ETL: daily or “near” real-time updates
ü  DW & Data Marts (in Hub & Spoke) – Relational Database & OLAP
ü  Many BI data stores created & tuned for specific analytics

•  Analytics Architecture
ü  BI Suites: Dashboards, Reporting, Office Integration & Mobile
ü  Data restructured for business processes & BI tool(s)
ü  OLAP (On-Line Analytical Processing) for Power Users
ü  Spreadsheets primary BI tool for business people


State of Business Analytics & BI Today


Need to Change Approach

•  Emerging Technologies have concentrated on:
ü  Data Variety
ü  Complexity of Data & Analytics
ü  Skills Shortfall

•  Emerging Technologies
ü  Columnar Databases
ü  Massively Parallel Processing (MPP)
ü  Data Virtualization
ü  In-Database Analytics
ü  In-Memory Analytics
ü  BI Analytical Appliances
ü  Cloud-based Applications


BI Analytical Appliances

•  Appliances have evolved
ü  Special purpose “devices”
ü  Target DW, BI/Analytics & Database Processing
•  Architectures Vary Widely
ü  Hardware & Software vs Software Only
ü  Hardware:
û  Commodity vs Proprietary
û  Commodity with selected specialized components
ü  Software
û  Proprietary versus Open Source
û  Open to selected DB, ETL and/or BI partners
ü  Emerging Technologies
û  MPP, Columnar, In-Database Analytics, In-Memory Analytics & Cloud
Appliances
•  Benefits: Lower TCO, Reliability, Scalable, Extensible


Speaker’s Expertise & Experience

•  Experience
ü  25 years of DW & BI experience
ü  30 years relational database experience
ü  Consulting, IT and Software Engineering
•  Consulting
ü  Business & IT Groups
ü  Software Vendors
•  Instructor
ü  Northeastern University, Graduate School of Engineering
ü  DW & BI Conferences; DW & BI Courses
•  Writer, Columnist, Blogger
ü  200+ Published Articles
ü  White papers, Webinars, Podcasts & Seminars
ü  DataDoghouse.com Blog on BI/DW industry
•  Thought Leadership:
ü  TDWI – Boston User Group Officer
ü  Boulder BI Brain Trust


•  The query patterns for BI business analytics are much different than
transactional processing. What are the key differences? How do you address
them?

•  Traditionally BI implementations required a sophisticated data architecture
including a DW, data marts (dimensional), OLAP, “flattened” datasets,
aggregated/summarized tables and other reporting data stores. Also maybe
an ODS (operational data store), staging tables and various data shadow
systems. Do you reduce the complexity of the traditional data architecture?

•  A key component of developing the business analytics is to define what data
the business needs, how they plan to analyze on it, design the queries, tune
the database, etc. And then do it again for each query. How do you change
that?

•  Business analytics typically involves a variety of BI tools such as reports,
dashboards, scorecards, ad-hoc analytics, data visualization, data discovery
and predictive analytics. How do you interact with these tools?


•  Business analytics/BI, data integration and DW requires a lot of varied skills.
What type of skills are needed to successfully implement your solutions? Do
you raise the increase on skills needed for implementation?

•  The limiting factor on many enterprise-wide BI and DW programs has been
cost, but emerging technology is perceived as more expensive. How do you
lower the TCO?

•  Assume that most enterprises have a DW (maybe even MDM) in order to
enable consistent and conformed data. How does your solution leverage the
DW? Does you solution lessen the need for a DW?

•  There was a lot of hype regarding BI Appliances a while ago and many
vendors used that term to label various hardware & software combinations.
From the hype, what has emerged to impact BI & how? What are the
“pretender” technologies that have not fulfilled on hype (no vendor names!)?


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