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Case study: Building a Holistic View of Data - Big Data Expo 2019

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Many organisations will be grappling with large, distributed and high-load data systems. Over time they have become complex, hard to maintain, and don’t provide a consolidated view of data available to the organisation. These were the challenges faced by Liberty Global who, together with EPAM, have tackled these issues by creating an Operational Data Hub - a centralized operational data framework to replace multiple monitoring solutions.

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Case study: Building a Holistic View of Data - Big Data Expo 2019

  1. 1. CONFIDENTIAL. Copyright © 2018 Data Analytics for Digital TV Platforms Paul Amiss, VP of Service Transition & In Life, Liberty Global Val Tsitlik, VP of Technology Solutions, EPAM Big Data Expo, 19 September 2019
  2. 2. Who We Are Liberty Global is the world’s largest international TV and broadband company, with operations in 10 European countries under the consumer brands Virgin Media, Ziggo, Telenet and UPC. LIBERTY GLOBAL EPAM SYSTEMS Complete Services for Digital Business Our multi-disciplinary teams combine business expertise with design thinking, world-class engineering, modern operations practices and knowledge of leading tools and frameworks to optimize performance.
  3. 3. A R C H I T E C T U R E S E C U R I T Y S C A L A B I L I T Y S T A B I L I T Y B U S I N E S S A N A L Y S I S Partnership +
  4. 4. INSIGHT ANALYTICS SCALABILITY MERGING DATA AGILE DEVELOPMENT INTEGRATION HOLISTIC VIEW Guiding Principles
  5. 5. • Millions of users • Multiple clients: Set-top boxes,TV apps, mobile, web • Country-specific functionality • Content availability • Complexity – hundreds of components • Wi-Fi and broadband connectivity • Millions of users • Affects digitalTV experience and vice versa • Historically, a separate data silo from digitalTV and entertainment Connectivity and Entertainment
  6. 6. 6 Initial Problem Statements R E A C T I V E P R O B L E M S O L V I N G M U L T I P L E M O N I T O R I N G S O L U T I O N S S E P A R A T E D A T A S I L O S H A R D T O S C A L EN O D A T A A N A L Y S I SH A R D T O I N T E G R A T E A N D I M P R O V E When monitoring is not a part of the core platform, it is hard to integrate new components and costly to maintain Lack of data and correlation capabilities makes troubleshooting expensive Scaling of traditional monitoring solutions is challenging Traditionally, DTV platforms have been launched with minimal monitoring and then expanded reactively Multiple monitoring solutions for different parts of the system Connectivity Monitoring and Entertainment monitoring are not connected
  7. 7. Centralized operational data platform to replace multiple co-existing monitoring solutions with a holistic system – and to provide insight in the end-to-end health of the overall systems. ONE MONITORING PLATFORM
  8. 8. ODH MODEMS SET-TOP BOXES PERFORMANCE VALIDATION PLATFORM DATA CENTER HEAD-END LAYER NETWORK LAYER Operational Data Hub
  9. 9. 9 Facts & Figures Servers with 87 VMs 138 Production and Satellite Clusters 18 Kafka Messages per Second ES Documents per Second 100,000 Pull RequestsSpark Jobs 500,000 6000100+
  10. 10. Aggregating logs from backend components Setting queues Aggregating messages Storage and Parsing Visualization Data Pipeline F L U M E K A F K A S P A R K E L A S T I C S E A R C H K I B A N A
  11. 11. AGILE SUPPORT ODH supports Agile methodology by easily adapting to any release and testing cadence MULTI-COUNTRY ODH supports separate release trains in multiple countries and environments FREQUENT RELEASES ODH supports Agile methodology by allowing frequent production code drops in a complex DTV ecosystem Agile Support
  12. 12. DATA PROCESSING OPTIONS Using historical data for proactive capacity management, e.g. consistent feedback for knowing how and when to expand different elements of the infrastructure EASY ADOPTION & INTEGRATION Every new component is integrated and components do not need to think about it HOLISTIC VIEW All the data is in one place: backend components, set-top boxes, modems, network, performance Data Availability
  13. 13. ANOMALY DETECTION Neural-network based anomaly detection trained on historical data CORRELATIONS Capabilities to correlate data from different sources view the best insight into end user experience and undertake root-cause analysis PREDICTIVE MODELLING Using historical data for proactive capacity management Data Products
  14. 14. • Build a talented team, it will become experienced in the process • Easy integration is the way to adopt a new solution • When people recognize the value, it's time to get pickier, but first, be flexible and willing to help • The platform worked perfectly well without data and without users. When the real integration began, we had masses of data, users and requirements, needing effort to stabilize and optimize. All architectural issues and lack of optimization surfaced • Monitoring the monitoring system is important Lessons Learned
  15. 15. Where We Are Developers 50+ Countries 15 Continents 4
  16. 16. CONFIDENTIAL. Copyright © 2018 Thank You Paul Amiss, VP of Service Transition & In Life, Liberty Global Val Tsitlik, VP of Technology Solutions, EPAM

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