Schnellere Digitalisierung mit einer cloudbasierten Datenstrategie

Faster Digitization with a
Cloud-Based Data Strategy
Düsseldorf , 5 July 2018

• Welcome and Introduction
• Cloud-Based Data Strategy
• Case Study: Consumer IoT at Vodafone
• Best Practices for Migrating to
Cloud-Native MongoDB
• Q&A
Agenda

Speakers
Tizian Bürger
Sr. Enterprise Account Executive
MongoDB
tizian.buerger@mongodb.com
Dr. Christian Kurze
Senior Solutions Architect
MongoDB
christian.kurze@mongodb.com
Viktor Kessler
MongoDB
viktor.kessler@mongodb.com
Ralf Bergs
Technical Lead, Consumer IoT,
Core Platform Vodafone

IPO 2017
About MongoDB
6600+ Customers
40 Mio
Downloads
+1000 Employees2007 / New York Growth 52% YoY
1M+ MongoDB
University
Registrations

Implement a Cloud-Based Data
Strategy for Faster Digitization
Dr. Christian Kurze
MongoDB
christian.kurze@mongodb.com

MongoDB 4.0 – Our Largest Release…
Just like relational transactions
• Multi-statement, familiar relational syntax
• Easy to add to any application
• Multiple documents in 1 or many collections and databases
ACID guarantees
• Snapshot isolation, all or nothing execution
• No performance impact for non-transactional operations
Schedule
• MongoDB 4.0: replica set
• MongoDB 4.2: extended to sharded clusters

Safe Harbour Statement
This presentation contains “forward-looking statements” within the meaning of Section 27A of the
Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as
amended. Such forward-looking statements are subject to a number of risks, uncertainties, assumptions
and other factors that could cause actual results and the timing of certain events to differ materially from
future results expressed or implied by the forward-looking statements. Factors that could cause or
contribute to such differences include, but are not limited to, those identified our filings with the
Securities and Exchange Commission. You should not rely upon forward-looking statements as
predictions of future events. Furthermore, such forward-looking statements speak only as of the date of
this presentation.
In particular, the development, release, and timing of any features or functionality described for
MongoDB products remains at MongoDB’s sole discretion. This information is merely intended to
outline our general product direction and it should not be relied on in making a purchasing decision nor
is this a commitment, promise or legal obligation to deliver any material, code, or functionality. Except
as required by law, we undertake no obligation to update any forward-looking statements to reflect
events or circumstances after the date of such statements.

MongoDB 4.0 – Our Largest Release…

Templates: Global Deployments in the Cloud

Why do I need to rethink my data layer…?
RDBMS Files
Mainframe
Application
Microservices / API Layer
Reads
Writes
RDBMS
Files
Mainframe
Typical Architecture
Complex & Fragile
Operational Data Platform
Organize, Use & Enrich Data
Application
Cache
RDBMS
RDBMS
Application Application
Non-standard data access Standardised Data Access
Near Real-
Time CDC
RDBMS
ApplicationApplicationApplication

How to move into the Cloud?
Phase I: Cloud-Ready Scaling of Backend Systems
Example: Operating Cost Savings
by reducing MIPS consumption
Extend Mainframe Applications:
• New digital applications to engage
customers on all channels
• Scale and grow as new services grow
Meeting Regulatory Demands, e.g.:
• PSD2, GDPR, no fees due to downtimes
Future-proof basis for the path into the cloudRDBMS Files
Application
Reads
Writes
Near Real-
Time CDC
RDBMS

Phase II: Enrich Data into a Single View
Incremental and low-risk development
of new functionality
360° Single View
• Improved customer experience
• Deeper insight into customer behavior and
better personalization
• Cross- and Up-Sell opportunities
Reusable and governed data assets for
multiple applications and services
RDBMS Files
Application
Reads
Writes
Near Real-
Time CDC
RDBMS

Phase III: Legacy Modernization
Future-Proof Architecture enables
Business Agility
• Short time-to-market by eliminating
”process waste”
• De-Risk Modernization: Step-wise
development
Cloud-Readiness
• Leverage cost benefits of modern
technologies
RDBMS Files
Application
Reads
Writes
Near Real-
Time CDC
RDBMS
X

API Access Layer
Operational Data
Customers
Products
Accounts
ML Models
Shared Physical Infrastructure
App1 App2 App3
1. Development agility
2. Corporate governance
3. Data re-use
Cloud Data Strategy
Standardized, on-demand database service
Cloud Portable
Any Cloud, Any Where

Best way to
work with data
Intelligently put data
where you need it
Freedom
to run anywhere
Intelligent Operational Data PlatformIntelligent Operational Data PlatformIntelligent Operational Data PlatformIntelligent Operational Data PlatformIntelligent Operational Data Platform
Why MongoDB?

Freedom To Run Anywhere
Database that runs
the same everywhere
Coverage in any
geography
Leverage the benefits
of a multi-cloud
strategy
Avoid lock-in
Mainframe
Database as a service
ServerLaptop Self-managed in the cloud

MongoDB Atlas
Self-service and elastic
Global and highly
available
Secure by default
Comprehensive
monitoring
Managed backup Cloud agnostic

MongoDB Stitch: Serverless platform for MongoDB
Stitch
Integrated services and pipelines for
complex, multi-stage workflows
Native SDKs for Android, JS, and iOS
clients
Direct database access

19
MongoDB Stitch Serverless Platform – Services
Stitch QueryAnywhere
Brings MongoDB's rich
query language safely to
the edge
iOS, Android, Web, IoT
Stitch Functions
Integrate microservices +
server-side logic + cloud
services
Build full apps, or Data as a
Service through custom APIs
Stitch Mobile Sync
Automatically synchronizes
data between documents
held locally in MongoDB
Mobile and your backend
database
(coming soon)
Streamlines app development with simple, secure access to data and services from the client with thousands of lines less
code to write and no infrastructure to manage – getting your apps to market faster while reducing operational costs.
Stitch Triggers
Real-time notifications let your
application functions react in
response to database
changes, as they happen
(coming soon)

Code user authentication
Code data access controls
Provision backend server
Install runtime environment
Add code to make backend HA
Add code to scale backend
Monitor & manage backend infrastructure
Code REST API for frontend to use backend
Code backend application logic
Code application frontend
Code against each external service API
Continuously poll database for changes
Without Stitch
Simple JSON Config
Handled automatically by Stitch and Atlas
Code frontend using single SDK/API
With Stitch
Backend
Data Access
Frontend
Provide JS code for Stitch Functions

App Backend Infrastructure
Core Database Functionality
Storage
Service integrations, data access control
Code that moves the business forward
Managing OS, Scale, Security, Backups, etc.
MongoDB
Atlas
MongoDB
Stitch Fully managed
Elastic scale
Highly Available
Secure
You should focus here
Focus Your Energy Where You Can Make a Difference

MongoDB Mobile: E2E for Mobile & IoT
Geographically distributed backend services
MongoDB Atlas
Point and click global clusters
Effortless HA, DR, and low-latency access
MongoDB Stitch, Serverless Platform
Automated Scaling based on request volume
Stitch Query Anywhere
Stitch Functions
Stitch Triggers
Stitch Mobile Sync (coming soon)
Geographically distributed frontend services
MongoDB Mobile
IoT and Edge Devices
iOS and Android Devices
Supporting local offline storage
Stitch provides seamless bridge between them

Your Choice…
Digital Strategy
& Data
Challenges
Options
Limited ROI
Old Architecture
Limited advantage of cloud
Unattractive Economics
Lift & Shift
Migration
Select
Alternative
Relational DB
Small
Tactical Project
Modernize /
DBaaS
Use Next-Gen
DBMS
New Business
Opportunities
CXO Initiatives
Transformational solutions
Low risk Implementations
Cloud/Global
Tactical
Strategic

How to get started?
Spin up a cluster on the
Free Tier today:
cloud.mongodb.com

Consumer IoT
Cloud Strategy
Ralf Bergs, Technical
Lead
05 July 2018
C1 - Public

Who are we?
C1 - Public 26 13 July 2018

Vodafone Group
13 July 201827
• Vodafone second-largest Mobile Operator world-wide
• 26 local markets, +50 partner markets
• 533 m proportionate Customers
• Enterprise Clients in 150 countries
• Enterprise IoT World Market Leader:
– BMW, Audi, Volkswagen Group, Daimler
– Lufthansa, Airbus
– Bosch, RWE, Siemens
– Linde, Shell, Unilever…
C1 - Public

Consumer IoT – “V by Vodafone”
13 July 201828
• Bring Internet-of-Things to Consumers
• “V by Vodafone” proposition launched Nov. 2017
• Markets live: DE, ES, IT, PT, UK
• Products:
– V-Bag, V-Pet – Tracking Devices
– V-Auto – OBD2-Plug for Car Telematics and Logbook
– V-Camera – Rechargeable Battery-Driven LTE Surveillance Camera
– V-SIM – SIM Product tailored towards use in Consumer IoT Cellular Products
– V-Home – Home Security and Automation Platform
• https://v.vodafone.com/
C1 - Public

Commercial for “V by Vodafone” Launch
13 July 201830
Thanks to
Martin Freeman
(known as “Bilbo” from “The Hobbit” trilogy)
C1 - Public

Consumer IoT Core Platform Functionality
13 July 201831
• Registration
• Price Plan Selection
• Provisioning of Devices and SIMs
• Integration of Vendor Platforms
• Billing/Charging
• Value-added Services:
– Device Sharing Capabilities
– Alarming Functions (e. g. Call-outs to trusted Contacts)
• Customer Service Functionality
• Reporting
Consumer IoT
Core Platform
Vodafone Enablers
Billing M2M Global Data
Services Platform
Vodafone Automotive
Platform
Supplier
Platforms
Customer
C1 - Public

Our Cloud Strategy

Decision for Hosting Environment
13 July 201833
• 07/2016: PoC to be “industrialized”
– Hosting Environment? Choice between
– Traditional Data Center –Public Cloud (AWS)
C1 - Public

Hosting Architecture Evolution (1/3)
13 July 201834
– Initial Hosting Architecture:
– Derived from traditional 3-Tier
– Well-known, mature Design
– Approved by Security
– Easy Migration back to Data Center
(if required)
– Only native Services:
– EC2 (virtual instances) + Docker Containers
– Elastic Load Balancers
– Setup Time: one Day
C1 - Public

13 July 201835
Cloud
Nativeness
Time
EC2
ELB
Atlas RDS
Auto-
Healing
Auto-
Scaling
Docker
Swarm
Kubernetes
EKS
Lambda
Cloud-
Front/S3
ElastiCache
now
TerraForm
Ansibe
C1 - Public

13 July 201836
– Self-hosted MongoDB Database
Clusters replaced by Atlas
– CloudFront + S3 for static Content
– Auto-Scaling/Auto-Healing in many
places
– Classic ELBs replaced by Appl.
ELBs
– Docker Swarm
– Kubernetes being rolled out, to be
replaced by AWS EKS
– Lambda for Serverless Computing
C1 - Public

Reasoning for Hosting Environment and
Architecture
13 July 201837
• Challenges:
– Time-to-Market: challenging Timelines
– Conflict between “Deliver Features” and “Sustaining”
– “Total Cost of Ownership”:
– Off-Shore Team in India vs.
– External Consultants on-Shore
– Expected Usage and Growth?! Sizing?
• Solution:
– Amazon Web Services (AWS) Cloud
– Software-as-a-Service (Atlas)
C1 - Public

Our Benefits
13 July 201838
• Agile Setup of Infrastructure
• Flexible Sizing:
– Vertical or horizontal Scaling
– Additional (Development) Environments
– On-Demand, even temporary Components
• Automatic HA and Scaling with many
native Components
• Strong Security Features built-in
• Pricing(!)
“Peace of Mind”
C1 - Public

Atlas Benefits

Easy Setup
13 July 201840C1 - Public

Scalability
13 July 201841
• CPU/Memory Sizing with no Service Interruption
C1 - Public

Scalability
13 July 201842
• Storage Resizing:
C1 - Public

Different Storage Versions
13 July 201843
• Storage Engine Version:
C1 - Public

Performance Advisor
13 July 201844
• Ineffective Queries automatically detected, Index Creation suggested
C1 - Public

Monitoring
13 July 201845C1 - Public

Backup/Disaster Recovery
13 July 201846
• Fully-managed Backup Service
• Flexible Snapshot Frequency and Retention Time
• Additional Backup to second Region
• Query(!) Snapshots
• Restore Snapshot to new Cluster
C1 - Public

Insert Confidentiality Level in slide footer 47
Glückwunsch!
Du hast die Mission
„MongoDB: Schnellere
Digitalisierung“
erfolgreich bestanden.
Jetzt Punkte
einsammeln:
Noch nicht an Bord? Jetzt anmelden
unter:
missiondigital.vodafone.de/registration
vod.af/mndb75

A Path to the Cloud:
Best Practices for Migrating Relational
Databases to Cloud-native MongoDB
Viktor Kessler
MongoDB
viktor.kessler@mongodb.com

50
Why to migrate
● Time to Market
● Cloud native approach by application development
● Use of serverless paradigm and FaaS
● Enabling of shared nothing organisation
● 24/7 availability of application with no downtime
● Horizontal scalability on commodity hardware
● Loosely coupling and collocation with new AWS Services (SageMaker)
● Cost saving
● ... What are your reasons? viktor@monogdb.com

51
2 choices as you move to the cloud: Self-Managed or DBaaS
1. Provision instances and storage
2. Configure HA
3. Configure security
4. Configure backup/restore
5. Monitoring & alerting
6. Ongoing upgrades & maintenance
Choose instance, hit deploy,
available in a couple of
minutes
Database as
a Service
Self-Managed
Aka “Lift and Shift”
Cloud Migration
or Cloud First
Fork in
the road

52
DEMO
https://cloud.mongodb.com/

53
DATA MODELS DIFFERENCES
Relational Database
Related data split across multiple records and tables.
Multi-record transactions essential
Document Database
Related data contained in a single, rich document.
Transaction scoped to the document
123 kWh
234134
tb:METER
PK Nmi
4711 6001140731
Meter
234134
Register
E1
Date
2013.09.21
tb:INTERVALS
PK start_t
100 00:00
end_t
00:30
Value
0.019
Unit_of_M
KWH
FK
4711
100 01:00 null 0.013 KWH4711
tb:ALERTS
PK type
a1 schwankung
value
255
Unit_of_M
V
FK
100
Best way to
work with
data

54
DATA MODELS CONSEQUENCE
Meter ObjectRelationalMappingLayer
Meter
Intervals
ARR
Meter MDM
Contact
Roles
Intervals
Alerts
Alerts_hist
Intervals_ch
eck
Meter Detail
Alerts

55
DATA MODELS CONSEQUENCE
Meter ObjectRelationalMappingLayer
Meter
Intervals
ARR
Meter MDM
Contact
Roles
Intervals
Alerts
Alerts_hist
Intervals_ch
eck
Meter Detail
Alerts
Meter
Intervals
Alerts

56
MIGRATION
migration roadmap
Meter
ObjectRelationalMappingLayer
Meter
Intervals
ARR
Meter
MDM
Contact
Roles
Intervals
Alerts
Alerts_hist
Intervals_
check
Meter
Detail
Alerts
Meter
Intervals
Alerts

57
Best Practice: Migration Roadmap
https://www.mongodb.com/collateral/rdbms-mongodb-
migration-guide

58
Schema Design Migration Example

59
How to migrate: { big-bang; lazy : [db-level, app-level] }
big-bang
lazy.db-level
APPLICATION
RDBMS
SNAPSHOT BATCH MIGRATION
Continuous Sync Migration
Extract Transform Load
Extract
Transform
lazy.app-level
CRUD CRUD
Application managed migration
Load

60
Decomposing monolith
RDBMS
Backend
www
REST CRUD
1

61
RDBMS
Backend
www
REST CRUD
1
RDBMS
www
REST CRUD
2
microservice
Backend

62
RDBMS
Backend
www
REST CRUD
1
RDBMS
www
REST CRUD
2
www
REST CRUD
3
microservice
Backend
Intelligently
put data
where you
want it

63
RDBMS
Backend
www
REST CRUD
1
RDBMS
www
REST CRUD
2
www
REST CRUD
3
www
REST CRUD
4
Atlas
Stitch
microservice
Backend
Freedom
to run
anywhere

Integrated services and
functions for complex,
multi-stage workflows
Native SDKs for
Android, JS, and iOS
apps
Direct Database
Access
MongoDB Stitch https://www.mongodb.com/cloud/stitch

65
Summary
 Choose migration strategy
 Identify your queries
 Deduce schema from queries
 Keep in mind: “data accessed together, is stored together”
 Schema is not rigid and not made by concrete
 Focus not only on application, but on data as well

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