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Flink Case Study: Capital One

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Flink forward 2015

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Flink Case Study: Capital One

  1. 1. Flink at Capital One: Case Study Slim Baltagi @SlimBaltagi Director of Big Data Engineering, Fellow Capital One
  2. 2. 2 Agenda 1. Capital One at a glance 2. Some elements of our Technology Strategy 3. What is the business problem of this case study? 4. What is the related solution architecture? 5. What values Flink added to the solution?
  3. 3. 3 A leading consumer and commercial banking institution with $306.2 billion in assets, $204.0 billion in loans and $210.4 billion in deposits – 8th largest bank based on U.S. deposits1 – 4th largest credit card issuer in the U.S.3 – 3rd largest issuer of small business Visas and MasterCards in the U.S.4 – 3rd largest independent auto loan originator5 – Largest US direct bank6 Conducts business in the US, Canada and the U.K. • More than 65 million customer accounts and 46,000 associates • Fortune 500 rank: 124 • Best Companies rank: 85 1. Capital One at a glance 1) Domestic deposits ranking as of Q4’14 2) Source: FDIC, June 2014, deposits capped at $1B per branch 3) Company-reported domestic credit card outstandings, Q1’15, American Express ex Charge Cards 4) Source: Nilson Report, Q4’13 5) Source: JD Power, 2014 6) FDIC, company reports as of Q4’14
  4. 4. 4 2. Some elements of our Technology Strategy Leverage the power of Open Source technology beyond just a ‘low cost’ alternative. Introduce new capabilities to address limitations of our legacy platforms. Shift the data processing paradigm from a batch to real-time stream processing. Build solutions easy portable from on- premise to the cloud. Empower our associates to dream, disrupt and contribute to Open Source projects.
  5. 5. 5 3. What is the business problem of this case study? Real-Time monitoring of customer activity data (Audit log event details, failure and success data, … ) to: • proactively detect and resolve issue immediately • prevent significant customer impact • enable flawless digital enterprise experience The current legacy solution uses expensive and proprietary tools. The current legacy solution offer very limited realtime and advanced analytics capabilities.
  6. 6. 6 4. What is the related solution architecture?
  7. 7. 7 5. What values Flink added to the solution? A costeffective solution with the same capabilities as proprietary logdata analytics tools Real-Time event processing which was not possible with our legacy system: • Reliable realtime, exactlyonce event processing. Example: Real-Time alerts • Transformations, enrichments, lookups with very low overhead in realtime A future proof solution to handle growing customer activity data
  8. 8. 8 5. What values Flink added to the solution? More advanced analytics on data streams, such as: • Advanced windowing to perform analytics beyond eventatatime operations. • Machine learning: Event correlation, automated fraud detection, event clustering, anomaly detection, user session analysis, etc Solution aligned with our technology strategy
  9. 9. 9 Please come to my talk! Day 1 - October 12, 2015 16:00 - 16:40 Flink and Spark: Similarities and Differences @SlimBaltagi @CapitalOne

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