MongoDB & on IBM Z / LinuxONE Agenda Introductions - Who am I?

45 minutes MongoDB Overview

Mainframe Modernization

Customer Successes

Q&A Who Am I? Who am I?

Currently: Director of Information Strategy at MongoDB Helping companies derive business value through Digital Transformation, using the capabilities offered by MongoDB’s technology offerings and the surrounding ecosystem ● Cloud Native applications and architectures ● Application Modernization ● Mainframe Modernization ● Internet of Things

Previously: IMS Systems Programmer and OfficeVision/MVS Consultant at IMI MongoDB Overview To free the genius within everyone by making data ridiculously easy to work with. Intelligent Operational Data Platform

Best way to work Intelligently put data Freedom to run with data where you want it anywhere Best way to work with data Easy Fast Flexible Versatile Need to Think About the Data Differently

{ "_id" : ObjectId("5ad88534e3632e1a35a58d00"), "name" : { "first" : "John", "last" : "Doe" }, "address" : [ { "location" : "work", "address" : { "street" : "16 Hatfields", "city" : "London", "postal_code" : "SE1 8DJ"}, "geo" : { "type" : "Point", "coord" : [ 51.5065752,-0.109081]}}, + {...} ], "phone" : [ { "location" : "work", "number" : "+44-1234567890"}, + {...} ], "dob" : ISODate("1977-04-01T05:00:00Z"), "retirement_fund" : NumberDecimal("1292815.75") } Tabular (Relational) Data Model Document Data Model Related data split across multiple records and tables Related data contained in a single, rich document

9 This becomes complex and rigid to change even for a single application

{ "_id" : ObjectId("5ad88534e3632e1a35a58d00"), "name" : { "first" : "John", "last" : "Doe" }, "address" : [ { "location" : "work", "address" : { "street" : "16 Hatfields", "city" : "London", "postal_code" : "SE1 8DJ"}, "geo" : { "type" : "Point", "coord" : [ 51.5065752,-0.109081]}}, + {...} ], "phone" : [ { "location" : "work", "number" : "+44-1234567890"}, + {...} ], "dob" : ISODate("1977-04-01T05:00:00Z"), "retirement_fund" : NumberDecimal("1292815.75") } Tabular (Relational) Data Model Document Data Model Related data split across multiple records and tables Related data contained in a single, rich document

10 Intelligently put data where you want it

Workload Availability Scalability Locality Isolation Native Distributed Architecture

Application

Driver

••• Mongos Mongos •••

Shard 1 Shard 2 Shard N High availability Primary Primary Primary - Replica sets Secondary Secondary ••• Secondary Secondary Secondary Secondary Horizontal scalability - Sharding 12 Freedom to run anywhere

On-premises Fully managed Local & Mainframe Private cloud Hybrid cloud Public cloud cloud service

• Database that runs the same everywhere • Global coverage • Leverage the benefits of a multi-cloud strategy • Avoid lock-in

Convenience: same codebase, same APIs, same tools, wherever you run

13 Mainframe Modernization 5 phases of Mainframe Modernization MongoDB can help you monetize mainframe data, and increase agility and capabilities for new use cases at the same time.

MongoDB serves as system of record for a multitude of System of applications, with deferred writes to the mainframe if necessary. Record Transforming the role of the mainframe Transactions are written first to MongoDB, which passes the data “MongoDB first” on to the mainframe system of record.

Writes are performed concurrently to the mainframe as well as Offloading Reads “Y-Loading” MongoDB (Y-Loading), e.g. via a service-driven architecture. & Writes

Business Benefits Business The Operational Data Layer (ODL) data is enriched with Enriched ODL additional sources to serve as operational intelligence platform Offloading for insights and analytics. Reads Records are copied via CDC/Delta Load mechanism from the Operational mainframe into MongoDB, which serves as Operational Data Data Layer (ODL) Layer (ODL), e.g. for frequent reads.

Scope Customer Successes Leading International Mainframe Modernization Hospitality Provider Core reservations application modernized with MongoDB layer building flexibility beyond their current application Partnered with IBM architecture

Problem WhySolution MongoDB ResultsResults

Increased load from a large merger Operational Data Layer on MongoDB on Eight figure cost avoidance due to added to organic growth in traffic from Linux on Z provides a cost effective decreased mainframe engine needs and existing and new travel sites were platform for easy access by third MIPS reductions. causing both spiraling costs and parties. customer dissatisfaction. Improved customer satisfaction due to Ability to run on Linux on Z avoided pain reduced occurrence of hotel room not Core reservations application on z/OS of application rewrite, since customer being available at booking time. was overloaded by growing look:book has no desire to move off z/OS. ratio Large International Bank

Flexibility Availability Speed Versatility Run Anywhere

MongoDB solved a Always-on Operational Data Rapid improvement of Multiple account types Application scalability and agility Store eliminates customer customer experience aggregated into single innovations are issue for their customer impact from System of through expanded ODS for population into protected from the Secure Document store. Record maintenance transactional history Web and Mobile operational platform, events or outages channels accelerating the adoption of cloud

TCO 70% 10x -29 DCs Uptime Transactions $10M+ ⇩ ⇩ ⇧ ⇧ ⇩