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


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The Briefing Room with Rick Sherman and Actian

Slides from the Live Webcast on Aug. 28, 2012

The appetite for high-powered analytics is greater than ever these days, with increasing numbers of business users clamoring for insights. At the same time, source systems are proliferating, and the nature of questions being asked is getting more complex. Indeed, the entire landscape of analytics is changing in fundamental ways. How can your organization stay ahead of the curve?
Register for this episode of The Briefing Room to learn from veteran Analyst Rick Sherman how a variety of technologies can change the manner in which analytics are done. He'll be briefed by Fred Gallagher of Actian, who will explain how his company's Vectorwise technology leverages vector processing to expedite even the most complex queries when compared to traditional columnar or relational databases.

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

  1. 1. Eric.kavanagh@bloorgroup.comTwitter Tag: #briefr 8/28/12
  2. 2. !   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
  3. 3. !   August: Analytics !   September: Integration !   October: Database !   November: Cloud !   December: InnovatorsTwitter Tag: #briefr
  4. 4. !  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.Twitter Tag: #briefr
  5. 5. Robin Bloor is Chief Analyst at The Bloor Group. Robin.Bloor@Bloorgroup.comTwitter Tag: #briefr
  6. 6. Twitter Tag: #briefr
  7. 7. 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.comTwitter Tag: #briefr
  8. 8. ! 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.Twitter Tag: #briefr
  9. 9. 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 industrys 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 Seagates XIOtech subsidiary. Fred holds Bachelor of Arts and an MBA from Stanford University.Twitter Tag: #briefr
  10. 10. Briefing Room August 28Speakers:Rick ShermanFred Gallagher, General Manager Vectorwise, Actian Corporation
  11. 11. 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
  12. 12. But  most  enterprises  s5ll  only  use  1-­‐5%  of   their  data  What  if  that  number  doubled,  tripled,  quadrupled…  ?   Source:  Forrester  
  13. 13. 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
  14. 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
  15. 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
  16. 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
  17. 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:  /  June  15,  2012   18
  18. 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:  /  June  15,  2012   19
  19. 19. Vectorwise Technology1   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
  20. 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
  21. 21. Hadoop/Vectorwise Production Use Cases NK!  !   Social media !  Optimize user experience and ad revenue !   250 TB in Hadoop !   Extracting ~5 TB into Vectorwise Confidential © 2012 Actian Corporation 22
  22. 22. 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
  23. 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
  24. 24. For more information and to download a free trial of Vectorwise visit: Contact us at: 1 877-644-6343 Join the conversation: Twitter: @Actiancorp #VectorwiseConfidential © 2011 Actian Corporation 25
  25. 25. Twitter Tag: #briefr
  26. 26. The  Briefing  Room  The  Changing  Face  of  Analy1cs:     How  to  Stay  Ahead     Rick  Sherman   Athena  IT  SoluNons   617-­‐835-­‐0546  (C)   rsherman@athena-­‐     Copyright © 2012 Athena IT Solutions
  27. 27. The Changing Face of Analytics: How to Stay AheadState 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.
  28. 28. The Changing Face of Analytics: How to Stay AheadResponse 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 Slide 29 Copyright © 2012 Athena IT Solutions All rights reserved.
  29. 29. The Changing Face of Analytics: How to Stay AheadState of Business Analytics & BI Today Slide 30 Copyright © 2012 Athena IT Solutions All rights reserved.
  30. 30. The Changing Face of Analytics: How to Stay AheadNeed 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 Slide 31 Copyright © 2012 Athena IT Solutions All rights reserved.
  31. 31. The Changing Face of Analytics: How to Stay AheadBI 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 Slide 32 Copyright © 2012 Athena IT Solutions All rights reserved.
  32. 32. The Changing Face of Analytics: How to Stay AheadSpeaker’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 Blog on BI/DW industry •  Thought Leadership: ü  TDWI – Boston User Group Officer ü  Boulder BI Brain Trust Slide 33 Copyright © 2012 Athena IT Solutions All rights reserved.
  33. 33. •  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?Twitter Tag: #briefr
  34. 34. •  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!)?Twitter Tag: #briefr
  35. 35. Twitter Tag: #briefr
  36. 36. !   August: Analytics !   September: Integration !   October: Database !   November: Cloud !   December: InnovatorsTwitter Tag: #briefr
  37. 37. Twitter Tag: #briefr