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

Twitter Tag: #briefr

[email protected]

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!


!  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.

[email protected]



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: [email protected]


! 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

Speakers: Rick Sherman

Fred Gallagher, General Manager Vectorwise, Actian Corporation

August 28

Actian Today

12

•  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 •  Experiencing RAPID GROWTH •  Affordable, Leverages Standard Hardware and SoHware

Vectorwise

•  10,000+ mission cri5cal applica5ons – inc Data Warehouses •  Very high client sa5sfac5on •  S5ll Innova5ng: GeoSpa5al features

Ingres

•  Next Genera5on of Ac5onable BI – Connect Insight to Ac5on •  Analyzing Events and Data Ac5on Apps

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

14

$300bn value per year €250 bn value per

year $600 bn value per year

SOURCE: McKinsey Global InsNtute analysis

! Data silos, distributed and disparate

! Existing solutions are not real-time

! Expensive, inefficient or inflexible

! Scalability not there

“Big” Data Management Challenge

Local Data

Data in the Cloud

15

The Need for Speed and Agility

16

! 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

Reduce Latency Between Events and Action

Value

Time latencies

Latency

Analyze

A*ribu/on: Jean-‐Michel Franco of Business & Decision

Event

Capture

Latency

Latency

Ac5on

Informed decision ready to be made

17

0 100,000 200,000 300,000 400,000

Vectorwise 445,529

Vectorwise 436,788

SQL Server 219,888

Oracle 209,534

Oracle 201,487

SQL Server 173,962

Sybase IQ 164,747

Oracle 140,181

SQL Server 134,117

June ‘12

May ‘11

Aug ‘11

June ‘11

Sept ‘11

Apr ‘11

Dec ‘10

Apr ‘10

Dec ‘11

QphH

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

Vectorwise: Affordable Performance – Proven!

18

0 100,000 200,000 300,000 400,000

Vectorwise 445,529

Vectorwise 436,788

SQL Server 219,888

Oracle 209,534

Oracle 201,487

SQL Server 173,962

Sybase IQ 164,747

Oracle 140,181

SQL Server 134,117

June ‘12

May ‘11

Aug ‘11

June ‘11

Sept ‘11

Apr ‘11

Dec ‘10

Apr ‘10

Dec ‘11

$57,146

$1,229,968

$460,869

$2,402,706

$753,392

$278,527

$85,621

$1,249,967

$258,880

Hardware Cost (excluding discounts) QphH

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

Vectorwise: Affordable Performance – Proven!

19

Vectorwise Technology

Confidential © 2012 Actian Corporation 20

Vector Processing Single InstrucNonMulNple Data

2nd Gen Column Store Limit I/O Efficient real Nme updates

Smarter Compression

Maximize throughput Vectorized decompression

On Chip CompuNng

Tim

e / C

ycle

s to

Pro

cess

Data Processed

DISK

RAM

CHIP

10GB 2-‐3GB 40-‐400MB

2-‐20

150-‐250

Millions

Process data on chip – not in RAM

1

2

3

4

MulN-‐core Parallelism

Maximize system resource uNlizaNon

5

…

Customer Stories: Sheetz and Zoho

!SaaS Company with 6 million Users ! Problem and Requirements

!   Customer data growing rapidly !   Ease of use for self-service BI !   Affordability !   200,000 users of Zoho Reports

Vectorwise results !   Exceptional performance !   Affordability for a SaaS offering

!Leader in Convenience Stores ! Problem

!   Multiple data sources

!   Need to control costs

!   Huge data growth

! Vectorwise results !   Expand data to analyze two years

!   Manage growth for three years

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

24

! 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

For more information and to download a free trial of

Vectorwise visit: www.actian.com/vectorwise

Contact us at:

1 877-644-6343

[email protected]

Join the conversation:

Twitter: @Actiancorp #Vectorwise



Copyright © 2012 Athena IT Solutions

Rick Sherman Athena IT SoluNons 617-‐835-‐0546 (C) rsherman@athena-‐soluNons.com

The Changing Face of Analy1cs: How to Stay Ahead

The Briefing Room

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

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


The Changing Face of Analytics: How to Stay Ahead 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


The Changing Face of Analytics: How to Stay Ahead State of Business Analytics & BI Today


The Changing Face of Analytics: How to Stay Ahead 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


The Changing Face of Analytics: How to Stay Ahead 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


The Changing Face of Analytics: How to Stay Ahead 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!)?



!  August: Analytics

!  September: Integration

!  October: Database

!  November: Cloud

!  December: Innovators
