An Operational Data Layer is Critical for Transformative Banking Applications

An Operational Data Layer is Critical for

Transformative Banking Applications

Richard Henderson

Solution Engineer

DataStax

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

Some of our customers

In a rapidly evolving world…

accelerates expectations

© 2017 DataStax, All Rights Reserved. Company Confidential

You must respond

User

Expectations

And Behaviours

Regulatory change

Non-traditional Players

(Fintechs,

Challenger Banks)

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.

Macquarie

THE CHALLENGE: Drive digital transformation initiatives to

enhance customer experience.

7

• 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

ING Focuses on Customer Experience and Micro-Services

• Focusing on customer experience ING has moved to a

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

active-active and having an always-on architecture.

THE CHALLENGE: Availability.

© DataStax, All Rights Reserved.

Banking Transformation with an

Operational Data Layer

Requirement Zero:

Be Demonstrably Secure

DSE advanced security features


● At-rest Transparent Data Encryption

○ Local Key

○ External Key Manager via KMIP

○ Configuration Value Encryption

○ System Info Encryption

● Authentication

○ Internal or Password Authentication

○ Kerberos Authentication

○ LDAP Authentication

○ Unified Authenticator

○ Proxy Authentication

● Authorization

○ Role Based Access Control

○ Internal Role Management

○ External Role Management

○ Row Level Access Control

● In-flight Encryption

○ Inter-node SSL

○ client-to-node SSL

● Auditing

● OpsCenter Security

CONTEXTUAL

Transformative banking applications have these qualities

ALWAYS-ON DISTRIBUTED SCALABLEREAL-TIME

© 2017 DataStax, All Rights Reserved. Company Confidential

Where the operational data layer lives


New

Application

Bank App

3rd Party

Web

Applications

Open API

24/7/365 Expectations

Scalable Low Latency

Embedded Search

Secure Data Exposure

??

Don’t just add an API gateway and a search engine


High Cost Scale Up Front

Batch/Mostly Available

Separate

Search

More Complexity

Directly Exposed Data

Application

Integration

Service

New

Application

Bank App

3rd Party

Web

Applications

Open API



Embedded Search


Add a simple integrated operational data layer


Operational

Data Layer

Scale On Demand

100% Always On Integrated Search

Embedded

Search

Isolated Datasets



Embedded Search


New

Application

Dataset per

Customer

Bank App

3rd Party

Web

Applications

Open APICustomer

Bank App

3rd Party

Customer

New

ApplicationWeb

Applications

Open API

Reuse it inside the business


Scale On Demand

100% Always On Integrated Search

Isolated Datasets



Embedded Search


Operational

Data Layer

Embedded

Search

New

Application

Dataset per

Service

Employee

App

Peer Org

[Micro-]

Services

Internal APIEmployee

The full-scale architecture with analytics

24/7/365 expectations

Mega writes / s

Contextual/Personal

Real-time/Responsive

Batch

Analytics

Operational

Data Layer

Fast-path

Analytics

Multi-model

Scalable write

100% Always On Combine Session and History

Online Stream Analytics

New

Application[Micro-]

Services

Event API Embedded

Search

New

ApplicationWeb

Applications


Unify your data


Mortgage Bank AccountHouse

InsuranceLife Cover

DataStax Enterprise


Linear Scalability

Geographically Distributed

Continuously Available

Instantaneously Responsive

Integrated Search

Integrated Operational Analytics

Always On, Multi-model,Mixed Workload,Scalable Data Layer

An Operational Data Layer is Critical for Transformative Banking Applications

Business

Transcript of An Operational Data Layer is Critical for Transformative Banking Applications