Everything as a Service, including Big Data: BDaaS

1. Everything as a Service, including Big Data: BDaaS Franco-British bilateral workshop on Big Data London November 2012 Mick Symonds Principal Solutions Architect, Atos MS NL,7 November 2012

2. Introducing Atos Atos is an international information technology services company, delivering hi-tech transactional services, consulting, systems integration and managed services. Atos is focused on business technology that powers progress and helps organizations to create their firm of the future. It is the Worldwide Information Technology Partner for the Olympic Games and is quoted on the Paris Eurolist Market. Atos operates under the brands Atos, Atos Consulting & Technology Services, Atos Worldline and Atos Worldgrid. • Annual revenues of € 8,6 billion (pro-forma 2010) • Almost 74,000 business technologists worldwide in 42 countries • Worldwide headquarters in Bezons / Paris, France • Atos was established on July 1st 2011, following the successful integration of Atos Origin and Siemens IT Solutions and Services and the establishment of a global strategic partnership with Siemens AG

3. Foundation IT Services:Global Delivery to Trusted Partner ERP Applications Data Centers Enterprise Management Centers 900,000 SAP users 13 Global Data Centers +50 Local / Regional Data Centers 93,000 sq meters Data Center1.42 average global virtualization ratio Hosting: • 40,000 MIPS • 41,500 terabytes storage • 6000 COD/IaaS/Cloud instances Global EMC’s in Timisoara and Kuala Lumpur & 15 local EMCs with almost 700 staff Desktop 2,4 Million Seats 45 Million calls / year Server Management 105,000 managed server instances (74,000 managed physical servers) Network & Security services 40,000 switches, 6,000 routers, 12,500 WLAN access points, 327,000 voice end users, 350,000 RAS users, 31,000 unified communications end users, filtering for 146,000 email mailboxes

4. Powering progress for our clients Financial Services Public Sector, Healthcare & Transport Energy & Utilities Manufacturing, Retail & Services Telecom, Media & Technology

5. What I want to tell you about • It feels strange to come and tell Researchers about “Innovation” • Most of our “innovation” is finding out what researchers and vendors are reporting • we are a long way down the food chain in most respects • However, we find that one man’s business-as-usual is sometimes another man’s innovation • The real development in Cloud in general and Helix Nebula in particular is not really technology • it is deploying services, between suppliers, and making it work as a business • In Cloud, everything is “as a Service” (XaaS) • including, potentially, Bid Data storage and management • You can liberate yourselves from the tedious grind of production operations: • a. by delegating the structured deployment of rules and policies to us • b. by using us to supply point/niche capabilities • c. to provide enabling facilities for people who provide real added value

6. What is Big Data, the 3-4 traditional V’s Source: Oracle

7. From the traditional 3-4 V’s towardsthe 5-6 V’s Value Viscosity – Viscosity measures the resistance to flow in the volume of data. This resistance can come from different data sources, friction from integration flow rates, and processing required to turn the data into insight. Technologies to deal with viscosity include improved streaming, agile integration bus’, and complex event processing. Virality – Virality describes how quickly information gets dispersed across people to people (P2P) networks. Virality measures how quickly data is spread and shared to each unique node. Time is a determinant factor along with rate of spread.

8. Big Data is transforming business, as well as research • Large Hadron Collider: An example of sensor and machine data is found at the Large Hadron Collider at CERN, the European Organization for Nuclear Research. CERN scientists can generate 40 terabytes of data every second during experiments. • Boeing Jets: Boeing jet engines can produce 10 terabytes of operational information for every 30 minutes they turn. A four-engine jumbo jet can create 640 terabytes of data on just one Atlantic crossing; multiply that by the more than 25,000 flights flown each day, and you get an understanding of the impact that sensor and machine-produced data can make on a BI environment. • Twitter: The micro blogging site Twitter serves more than 200 million users who produce more than 90 million "tweets" per day, or 800 per second. Each of these posts is approximately 200 bytes in size. On an average day, this traffic equals more than 12 gigabytes and, throughout the Twitter ecosystem, the company produces a total of eight terabytes of data per day. In comparison, the New York Stock Exchange produces about one terabyte of data per day. • Wal-Mart: Transactional data has grown in velocity and volume at many companies. As recently as 2005, the largest data warehouse in the world was estimated to be 100 terabytes in size. Today, Wal-Mart, the world's largest retailer, is logging one million customer transactions per hour and feeding information into databases estimated at 2.5 petabytes in size. • Financialservices: Discover fraud patterns based on multi-years worth of credit card transactions and in a time scale that does not allow new patterns to accumulate significant losses. Measure transaction processing latency across many business processes by processing and correlating system log data. • Internetretailers: Discover fraud patterns in Internet retailing by mining web click logs. Assess risk by product type and session Internet Protocol (IP) address activity. • Retailers: Perform sentiment analysis by analysing social media data. • Drugdiscovery: Perform large-scale text analytics on publicly available information sources. • Healthcare:Analyse medical insurance claims data for financial analysis, fraud detection, and preferred patient treatment plans. Analyse patient electronic health records for evaluation of patient care regimes and drug safety. • Mobiletelecom: Discover mobile phone churn patterns based on analysis of call detail records and correlation with activity in subscribers' networks of callers. • IT technicalsupport: Perform large-scale text analytics on help desk support data and publicly available support forums to correlate system failures with known problems. • Scientificresearch:Analyse scientific data to extract features (e.g., identify celestial objects from telescope imagery). • Internettravel: Improve product ranking (e.g., of hotels) by analysis of multi-years worth of web click logs.

9. Long-term service trends

10. Towards an open future … • As a Tier 1 player in Cloud, Atos is becoming much more pro-active in moving developments forward • often in an open and collaborative manner with others, who may also include our competitors • Examples of this include: • the Open Data Centre Alliance: a cloud user group, defining common requirements for how cloud services are delivered to (initially) large enterprises. See: https://www.opendatacenteralliance.org • Helix Nebula: an initiative to deliver cloud services (initially) to European-based scientific research organisations • Both are cases where an initial development and deployment is expected to propagate to a much wider community, over time • Another common factor between these developments is the prevalence of Open Standards and Open Source tooling, and close involvement with the research community which pioneers them • Atos have an inside track on these developments with Atos Research and Innovation (ARI), who live in this world: see http://www.atosresearch.eu

11. Staffing trends and approaches with Utility and Cloud • Addressing the issue will help avoid local (re-)inventions • What can we do about it? • Training • Re-deployment • Recruitment • Alliances • Acquisitions + Increase ability to analyse customer needs (solution architecture) + Plan capacity, manage risks + Address governance issues within customer + Develop more flexible services + Developing and using Solutions Templates Design Administration - Automate, e.g. using RBA - Off-shore + Monitor operational exceptions Operations - Further automate- Outsource to vendors

12. Big Data competences and roles • New rolls and skills arise: • Data Scientist • Data Virtualization Specialist • Data Stewards • Big Data Architects • Big Data Analists • …. • New knowledge is necessary!

13. 10. Information lifecycle management 7. Rational consolidation 11. On Demand, Utility Computing 4. Global sourcing/off-shoring 8. Run Book automation 5. Physical consolidation Operational Cloud Services • Infrastructure, Platform, Software as a Service • Service aggregation • Identity, authorisation, security monitoring • Contingency, recovery 9. Contract, SL different-iation 2. Outsourcing 6. Demand/ Supply structures 3. Logical consolid-ation 1. Select standards Atos’ services during a migration and services lifecycle Solutions architecture and planning • Opportunity assessment to determine business needs • TCO analysis, norms, trends, to help build a business case • Establish portfolio of Cloud offerings and capabilities • Solutions selection, architecture and brokerage • Plateau Planning Service implementation and integration • Transition, standardisation, consolidation • Conversion and migration • Identity management • Service integration Customerto be Customeras is Traditional Services • Optimize legacy use

14. Governance Prices Business (= customers) Demand Supply What: service portfolio Services Directories Specificapplications Genericapplications Security Who: supplierinvolvement and integration Database Middleware Workplace Datacentres Servers Storage Networks Costs Processes How: common techniques Organisation Tools Service component relationships Outcomes Outputs Environment(s) to be managed Inputs

15. ILM Quick Scan – ILM Assessment – ILM Business Case ILM Consultancy Services Alignment of Technology Business Goals Data/Information Dynamic Management of Data/Information ILM Application Integrated Solutions Tier 1 Tier 2 Tier 3 Tier 4 Tier 5 Tiered Storage Solutions Storage On Demand Central Backup Services Archiving on Demand Tier 6 Compliant Archive InstantAccess Archive Mission critical Business Critical Productivityimportant Archive and Backup to tape ILM service offerings:Consult - Build - Operate

16. Using IaaS as a basis for adding value SaaS: use AppStore and DevPay-type facilities deliver added value information pay-per-use, with transactional charging SaaS PaaS: test and development facilities to create added value services PaaS Big Data Processing facilities Network access IaaS: from HNsuppliers

17. EO Application Platform Data & Catalogue public User (CNR) private Sandbox API Cloud Controller Web Interface OCCI ESA UNCLASSIED - For Offical Use 05/07/2012

18. EO Application Platform at work User • Instantiates the virtual machine and a development environment • Uploads his/her software and defines input data • Adaptsapplications to the Cloud to exploit the distributed computing (PaaS abstraction of the HadoopMapReduce model) • Uses the available toolbox that makes easier the transition between the local development environment (local workstation) and the Cloud • Tests, re-test, re-re-test, … • Transparently deploys the application and runs in cluster mode against large archives of data! ESA UNCLASSIED - For Offical Use 05/07/2012

19. For more information please contact: Mick Symonds Principal Solutions Architect/Loose Cannon Atos B.5.L08, Papendorpseweg 93, 3528 BJ Utrecht The Netherlands michael.symonds@atos.net m +31 651 755 779

20. More information and details… • More information is documented in various White Papers • Shaping the Cloud • And from the scientific community and others • Cloud Orchestration • Written in summer 2010 • Proof of concept created with Cordysand Open Source • Augmented by: A Cloud Message Broker • Extending connectivity to whatever else is out there • Downloadable from the Atos web site: • http://atos.net/en-us/about_us/insights-and-innovation/scientific_community/scientific_community_whitepapers/default.htm

21. More on the Atos Cloud platforms… Cloud Services