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Analytics as a Service

Analytics as a Service. OLIVER RATZESBERGER Sr. Director Architecture & Operations Cloud Computing, Analytics as a Service eBay inc. October 2008. eBay Inc Overview. As of December 31, 2007

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Analytics as a Service

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  1. Analytics as a Service OLIVER RATZESBERGER Sr. Director Architecture & Operations Cloud Computing, Analytics as a Service eBay inc. October 2008

  2. eBay Inc Overview • As of December 31, 2007 • Founded in September of 1995, eBay is a global online marketplace where practically anyone can trade practically anything. • eBay has a global presence in 39 markets, including the U.S. • eBay has approximately 276 million registered users worldwide. • Marketplaces net revenues totaled a record $1.5 billion in Q4-07, representing a year-over-year growth rate of 21 percent. With 46 percent from US operations and 54 percent from our International business. • In Q4-07, gross merchandise volume (GMV), the total value of all successfully closed items on eBay's trading platforms, was $16.2 billion. (Total GMV for the full year 2007 was more than $59 billion.) • eBay users worldwide trade more than $2,039 worth of goods on the site every second.

  3. eBay Inc Overview (cont) • There were 637 million new listings added to eBay worldwide in Q4-07. At any given time, there are approximately 113 million listings worldwide, and approximately 6.7 million listings are added per day. eBay users trade in more than 50,000 categories. • At the end of Q4-07, eBay hosted approximately 532,000 stores worldwide, with approximately 46 percent of stores hosted on eBay's international sites. • eBay members worldwide have left more than 6 billion feedback comments for one another regarding their eBay transactions. • The most expensive item sold on eBay to date is a private business jet for $4.9 million.

  4. Velocity of trading • On an average day on eBay… • A Diamond Ring is sold every two minutes

  5. Velocity of trading • On an average day on eBay… • Morethan3 Watches are sold every minute

  6. Velocity of trading • On an average day on eBay… • 5 Women’s handbagsaresoldevery minute

  7. Velocity of trading • On an average day on eBay… • Over 3600 MP3 players are sold

  8. Velocity of trading • On an average day on eBay… • A makeup product sold every 2 minutes

  9. Velocity of trading • On an average day on eBay… • 4,827 fragrance products sold per day

  10. Velocity of trading • On an average day on eBay… • A hair product sold everysecond

  11. Velocity of trading • On an average day on eBay… • Over 300 stamps are sold every hour

  12. Velocity of trading • On an average day on eBay… • An automobile is sold every minute

  13. eBay Analytics Technology Highlights >50 TB/day of new, incremental data >100k data elements >50 PB/day >50^10 new records/day Processed >50k chains of logic >5000 business users & analysts Active/Active 5 seconds turning over a TB every 24x7x365 Millions of queries/day Always online 99.9+% Availability Near-Real-time

  14. eBay Analytics Core Data Access MicroStrategy Business Objects Unica Crystal SAS SQL MAX SOA/DAL Primary Secondary Relational Data MPP Relational Data MPP 2.5PB 2.2PB Teradata Teradata Linux Linux Local Interconnect Local Interconnect Wide Area Interconnect 1000 miles Sun Fire 4xxx Sun Fire 4xxx Solaris Solaris 2.2PB 6.6PB XML, name/value, raw MPP/HPC/Grid MPP/HPC/Grid Phoenix, AZ Sacramento, CA Data Integration Ab Initio Informatica Golden Gate UC4 BES MAX SOA 14

  15. Analytics DNA • Embedded in our daily life • Bottoms-up & Tops-down • Think and Live Analytics • Always • But know when do avoid Analysis Paralysis!

  16. Types of Analytics at eBay • Basically measure anything possible - A few examples: Marketing Buyer Experience Finance Trust & Safety Technology Operations Customer Service Loyalty Information Security Infrastructure Finding User Behavior Seller Experience

  17. Key Performance Indicators Align individual and departmental performance objectives with corporategoals

  18. KPI Example: Technology Operations • Parallel Efficiency – (simplified) The effectiveness of distributing large amounts of workload over pools and grids of servers. • 100% is GOODLess than 70% is BAD • 10,000 Server running at an average PE of 50% • Established through Analytics of Operations Data – Minute by minute utilization metrics of entire infrastructure • Raising PE from 50%to80% equals Millions in OpEx savings

  19. KPI Example: Technology Operations (cont) • Individual process rollup • Grid level Parallel Efficiency at 99.9%

  20. Design for the Unknown • >85% of eBay analytical workload is NEW & Unknown • Exploration is the core of an analytical company • The metrics you know are ‘cheap’ • The metrics you don’t know are expensive but also high in potential ROI • Design can’t be static or dependent on specific questions or dimensions 20

  21. Proliferation of Analytics • Decentralized Analytics • Shortened Time to Market Requirements • Adhoc Exploration • Departmental Data • Prototyping - Can’t wait for EDW • ”We Need Data Marts!” 21

  22. Proliferation of Analytics • Hub and Spoke Architecture ‘The Solve’ 22

  23. Data Mart Dilemma A Data Mart cannot be ‘cheap’ enough to justify its existence • Total Cost of Ownership (TCO) • Fully loaded cost staggering$500k++ • Biggest drivers are • Maintaining separate databases • weekly/daily/hourly data transfers • Data inconsistencies • Data redundancy • Increased complexity • Loss of lineage over time

  24. Agile Analytics needs Analytics as a Service • Massive scale AnalyticalUtility Computing • Bring your data - Perform your Analytics • From Simple Web based data upload • ...to fully private Utility access • Combine custom data and code with ALL existing data

  25. Analytics as a Service

  26. Analytics as a Service From simple web based table upload

  27. Analytics as a Service ...to fully private utility access We call them PET (Prototyping Environment = Sandbox) More than 75 active right now In most cases they are small (<500GB) since all the main data is already in the EDW They are free to the business units 27

  28. Analytics as a Service - Benefits • Improved Time To Market - Days/Weeks vs Months • Enable the business to do agile prototyping • Enable the users to “Fail Fast” - Make it easy • to try out new ideas • Eliminate stray Data Marts

  29. Resource Allocation Model (RAM)Activity Based Costing Model • Resource Budgeting for Business Units • Aid in Enterprise Capacity Planning • Enable Agile Analytics as a Service 29

  30. Questions • ? 30

  31. Find out more.... • On our recently launched technology blog:

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