1.An Operational Data Layer is Critical for Transformative Banking Applications Richard Henderson Solution Engineer DataStax
2.Todays Agenda • Introduction • Who DataStax are • Why transformation is needed • Customer success stories • The key requirements • The common features that meet those requirements • Where the solution lives in your architecture • How it should be implemented • Why it also works internally • Live Q & A
3.Some of our customers
4. In a rapidly evolving world… accelerates expectations © 2017 DataStax, All Rights Reserved. Company Confidential
5.You must respond Regulatory change User Non-traditional Players Expectations (Fintechs, And Behaviours Challenger Banks)
6.The Opportunity is now! Requirements: • Present the data we already have to customer applications. • Combine deep analytics with session data (“fast analytics”) to provide intelligent predictions to applications. • Present the data we already have to internal applications. • Safely and efficiently expose the data we already have to 3rd parties. • Have unified data for a customer across products.
7. Macquarie THE CHALLENGE: Drive digital transformation initiatives to enhance customer experience. • Transformed from no retail presence to a digital consumer banking leader in less than 2 years. • Macquarie used DSE as the core of a operational data-layer to enhance rather than replace. • Consolidated data from many existing disparate systems delivers 360o, real-time customer visibility. • Their world-class consumer banking app utilizes real-time analytics and full text search 7
8. ING Focuses on Customer Experience and Micro-Services THE CHALLENGE: Availability. • Focusing on customer experience ING has moved to a active-active and having an always-on architecture. touch-point architecture based increasingly on micro- services • Need for availability, consistency, and scalability • Lots of small use cases, DevOps teams, no ephemeral storage • 12 clusters (4/5 environments) • Cassandra eases availability challenges by being © DataStax, All Rights Reserved.
9.Banking Transformation with an Operational Data Layer
10.Requirement Zero: Be Demonstrably Secure
11. DSE advanced security features ● At-rest Transparent Data Encryption ● Authorization ○ Local Key ○ Role Based Access Control ○ External Key Manager via KMIP ○ Internal Role Management ○ Configuration Value Encryption ○ External Role Management ○ System Info Encryption ○ Row Level Access Control ● Authentication ● In-flight Encryption ○ Internal or Password Authentication ○ Inter-node SSL ○ Kerberos Authentication ○ client-to-node SSL ○ LDAP Authentication ● Auditing ○ Unified Authenticator ● OpsCenter Security ○ Proxy Authentication © DataStax, All Rights Reserved.
12. Transformative banking applications have these qualities CONTEXTUAL ALWAYS-ON REAL-TIME DISTRIBUTED SCALABLE © 2017 DataStax, All Rights Reserved. Company Confidential
13. Where the operational data layer lives New Web Application Applications ? Bank App Open API ? 3rd Party 24/7/365 Expectations Embedded Search Scalable Low Latency Secure Data Exposure © DataStax, All Rights Reserved.
14. Don’t just add an API gateway and a search engine Separate New Search Web Application Applications Bank App Application Integration Open API Service 3rd Party 24/7/365 Expectations Embedded Search Batch/Mostly Available More Complexity Scalable Low Latency Secure Data Exposure High Cost Scale Up Front Directly Exposed Data © DataStax, All Rights Reserved.
15. Add a simple integrated operational data layer New Web Operational Application Applications Data Layer Bank App Dataset per Customer Customer Open API Embedded 3rd Party Search 24/7/365 Expectations Embedded Search 100% Always On Integrated Search Scalable Low Latency Secure Data Exposure Scale On Demand Isolated Datasets © DataStax, All Rights Reserved.
16. Reuse it inside the business New [Micro-] Web Operational Application Services Applications Data Layer Employee Bank App App Dataset per Service Employee Customer Internal Open APIAPI Embedded Search 3rd Party Peer Org 24/7/365 Expectations Embedded Search 100% Always On Integrated Search Scalable Low Latency Secure Data Exposure Scale On Demand Isolated Datasets © DataStax, All Rights Reserved.
17. The full-scale architecture with analytics New Web Application Operational Applications Data Layer Batch Fast-path Analytics New Analytics [Micro-] Application Services Multi-model Event API Embedded Search 24/7/365 expectations Contextual/Personal 100% Always On Combine Session and History Mega writes / s Real-time/Responsive Scalable write Online Stream Analytics © DataStax, All Rights Reserved.
18. Unify your data House Mortgage Bank Account Life Cover Insurance © DataStax, All Rights Reserved.
19. DataStax Enterprise Always On, Multi-model, Mixed Workload, Scalable Data Layer Linear Scalability Geographically Distributed Continuously Available Instantaneously Responsive Integrated Search Integrated Operational Analytics © DataStax, All Rights Reserved.