Demystify Big Data Breakfast Briefing - Juergen Urbanski, T-Systems


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Hortonworks Demystify Big Data Breakfast Briefing 9th July, London slides by Juergen Urbanski, T-Systes

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  • Line of BusinessDemand 360 view of customer, employee, market, etc, but cannot be certain about what matters for analysisBusiness AnalystsNeed to incorporate more data into analysis, LOBs not sure what matters; want to reuse existing skill setsData Warehouse OwnersMust efficiently store, process, organize, deliver massive and growing data volume and variety while meeting SLAsIT ManagementDrive innovation, reduce costs, meet growing analytic demands of LOBs, mitigate risk of adopting new technologySystem AdministratorsEnsure stability and reliability of systemsBuyers:VP AnalyticsVP/Director Business IntelligenceVP/Director Data Warehousing/ManagementVP/Director InfrastructureVP/Director Operations/IT SystemsFaster customer acquisitionBetter product developmentBetter qualityLower churn
  • Which distribution will ensure you stay on the main path of open source innovation, vs. trap you in proprietary forks?
  • Demystify Big Data Breakfast Briefing - Juergen Urbanski, T-Systems

    1. 1. Capturing Big Value in Big Data at Deutsche Telekom Jürgen Urbanski VP Big Data Architectures & Technologies T-Systems Board Member Big Data & Analytics BITKOM (German IT Industry Association) Christian Wirth VP BI & Big Data T-Systems
    2. 2. Introducing Deutsche Telekom and T-Systems  Deutsche Telekom is Europe‟s largest telecom service provider – Revenue: €58 billion – Employees: 232,342  T-Systems is the enterprise division of Deutsche Telekom – Revenue: €10 billion – Employees: 52,742 – Services: data center, end user computing, networking, systems integration, cloud and big data 1
    3. 3. Disruptive Innovations in Big Data 2 Relational Database HADOOP MPP Analytics Data Warehouse Schema Pre-defined, fixed Required on write Required on read Store first, ask questions later Processing No or limited data processing Compute & storage co-located Parallel scale out processing Data typesStructured Any, including unstructured .. Physical infrastructure Default is enterprise grade Mission critical Default is commodity Much cheaper storage
    4. 4. Target Hadoop Use Cases 3 IT Infrastructure & Operations Business Intelligence & Data Warehousing Line of Business Potential valueHighModerate  Lower Cost Storage for Tier 3 / 4 workloads (active archive)  Enterprise Data Warehouse Offload  Enterprise Data Warehouse Archive Telecommunications & Media  Data Products  Capacity Planning & Utilization  Customer Profiling & Revenue Analytics  Targeted Advertising Analytics  Service Renewal Implementation  CDR based Data Analytics  Fraud Management Other Industries  Connected Car  Smart Home Cost effective storage, processing, and analysis Foundation for profitable growth = Highlighted today 1 2 3 4 N
    5. 5. Enterprise Data Warehouse Offload 4 The Challenge  Many EDWs are at capacity  Running out of budget before running out of relevant data  Older data archived “in the dark”, not available for exploration The Solution  Hadoop for data storage and processing: parse, cleanse, apply structure and transform  Free EDW for valuable queries  Retain all data for analysis! Operational (44%) ETL Processing (42%) Analytics (11%) DATA WAREHOUSE Storage & Processing HADOOP Operational (50%) Analytics (50%) DATA WAREHOUSE Cost is 1/10th 1
    6. 6. Data Products: ImmobilienScout (a DT subsidiary) 5 The Situation  Europe„s leading real estate marketplace with data on... – 1m properties listed currently – 20m properties cumulative – 6 million saved searches – Geographical coordinates – Enriched by socio-demographic data on 19m properties  Team – Product Manager – Data Scientists – 2 Scrum Teams The Solution  “Market Navigator” service – Supports realtors in acquiring customers – Local market analysis helps with price setting for rent and buy – Integrates third-party data  Functionality includes – Price heat maps & trending – Demand- and supply-side info – Local area information – Comparable transactions 2
    7. 7. Seite 6 Turning Big Data into Products!2
    8. 8. Connected Car (a T-Systems offering) When cars go online... Calling the repair center Read out vehicle data On-Board signaling Online combinations Machine data enriched with Web data Based on Cloud Technology Reduced incidence of product recalls Better management of product life cycle Early error detection Direct online link to dealers and the OEM Preventative maintenance quicker repair turnaround Usage-based feedback for product development 40 millon new mobile contracts Higher customer satisfaction V Volume Velocity Variety  Value  3 7
    9. 9. Smart Home: Gigaset (a T-Systems customer)  Gigaset Elements is a sensor- and cloud-based solution for home networks  Cutting-edge sensors are combined with each other and linked with an Internet-capable DECT ULE base station and a secure Web server  That permits a large number of applications in the home notably home security and elderly assisted living  The intelligent, learning system is powered by Hadoop  At a price of less than €200 for a Starter Kit, the system is intended to be suitable for the mass market 4 8
    10. 10. Which Distribution is Right for You Today and Tomorrow?  13 original Apache Hadoop projects  No commercial support  Fully open source distribution (incl. management tools)  Reputation for cost-effective licensing  Strong developer ecosystem momentum  GTM partners incl. Microsoft, Teradata, Informatica, Talend, NetApp  Widely adopted distribution  Management tools and Impala not fully open source  GTM partners include Oracle, HP, Dell, IBM  Appeals to some business critical use cases prior to Hadoop 2.0  GTM partner AWS (M3 and M5 versions only)  Just announced by EMC, very early stage Open Open & proprietary Proprietary 9
    11. 11. How We Evaluate Hadoop Distributions 10 Hortonworks well positioned prior to HDP2.0
    12. 12. HDP 2.0 is Architected to be a Good Fit with these Enterprise Requirements 11
    13. 13. T-Systems Approach to Big Data Projects Assessment in three phases: Maturity & Potential Evaluation  Capability maturity benchmarking  Identification and prioritization of potential vs. challenges Deliverables: Current versus future mode gap analysis 1 Proof of Concept  Selection of initial use case  Standup of test environment with customer data  Validation of feasibility and potential Deliverables: Testing of customer-specific scenario including cost-benefit analysis 2 Strategy & Roadmap  Development of enterprise- wide Big Data strategy  Prioritization of road map  Implementation planning Deliverables: Business case, prioritized roadmap, implementation plan 3 12
    14. 14. Deutsche Telekom Perspective  The Hadoop ecosystem delivers powerful innovation in storage, databases and business intelligence, promising unprecedented price / performance compared to existing technologies  Hadoop is becoming an enterprise-wide landing zone for big data. Increasingly it is also used to transform data  We look forward to realizing cost reductions in areas such as enterprise data warehousing. More importantly, Big Data opens up new business opportunities for ourselves and our customers  In that journey we are partnering closely with 13
    15. 15. Big Data = Big Opportunity! Jürgen Urbanski Christian Wirth