MongoDB Zürich event overview

www.mongodb.com
MongoDB Zürich
November 29th 2017
Overcoming Today's Data Challenges
with MongoDB
With the participation of:
&

• How the World has Changed Since Relational
Databases were Invented
• How to radically transform your IT environments with
MongoDB
• MongoDB with Blockchain
• MongoDB, the Database of choice for multiple Use
Cases
• Q&A and Conclusion
Agenda

Your Speakers today:
Rizan Flenner
Regional Vice President
Central Europe, MongoDB
Roman Gruhn
Director of Information Strategy
(EMEA), MongoDB
Dirk Slama
Vice President Bosch Software
Innovations
Benjamin Lorenz
Senior Solution Architect, MongoDB
Kamal Ved
Director of Business Development
BigChainDB

800+ employees
MongoDB Inc.
MongoDB DACH
Frankfurt, München,
Zürich, Wien
4,300+ customers
29 offices
worldwide
NASDAQ
“MDB”Adrian Oesman
adrian@mongodb.com

Customers MongoDB Office Support MongoDB User Groups
30+ Million Downloads

Rizan Flenner
Regional Vice President, Central Europe
Rizan.flenner@mongodb.com
How the World has Changed
Since Relational Databases were Invented

Digital Platforms Have Changed
The platforms your end users and customers use to engage with your applications and services have fundamentally
changed at an unprecedented speed over the past 5 years.
UPFRONT SUBSCRIBE
Business
YEARS / MONTHS WEEKS / DAYS
Applications
PC MOBILE / BYOD
Customers
ADS SOCIAL
Engagement
SERVERS CLOUD
Infrastructure

People Thinking of:
“ Competitive Advantage
… they think about technology …..
… which includes software and data
Every company today is becoming a software business
Companies Value Propositions today are either
Enabled,Defined or Delivered
Thru software solutions“
Dev Ittycheria

How can Software drive success?
Source: US Bureau of Economic Analysis
Manufacturing Retail Transportation Publishing,
Broadcast
Education,
Healthcare,
Social Assistance
Finance,
Insurance,
Real Estate
Arts,
Entertainment,
Food
$1.6T
$1.1T
$1.5T
$6.2T
$5.3T
$2.4T
$1.2T

Digital matters to your customers…
$4tn+ in
eCommerce sales
by 2020
75bn connected
devices
by 2025
61% see AI as an
“urgent” strategy
in 2017
$108bn AR/VR market
size
by 2021
59% global smartphone
penetration
by 2022
$6tn+ in cyber-crime
damage
by 2021

…..But Digital is Hard
Source: Forrester Digital Transformation Infographic: https://go.forrester.com/blogs/16-05-09-digital_transformation_2016_infographic/

MongoDB fuels giant ideas in DACH

• How the World has Changed Since Relational
Databases were Invented
• How to radically transform your IT environments with
MongoDB
• Bridging the gap – How BOSCH is addressing
enterprise/cloud integration
• MongoDB with Blockchain
• MongoDB, the Database of choice for multiple Use
Cases
• Q&A and Conclusion
Agenda

How to radically transform your IT environments with
MongoDB
Roman Gruhn
Director, Information Strategy, MongoDB
roman.gruhn@mongodb.com

Agenda
• Challenges & Opportunities
• Modern Information Management
• The New Operating Models in IT
• Customer Success Stories

The Dominance of Data
“Software is
eating the world”
“Software is king,
but data is queen”
Our Mission:
Be the data platform for innovators everywhere

The World Has Changed
Leverage Data & Technology to
Maximise Competitive Advantage
Accelerate
Time to Value
Dramatically
Lower TCO
Reduce Risk for
Mission-Critical
Deployments
Data Applications Commercials Risk
Our Value Drivers:
Volume
Velocity
Variety
Time to value
Architectures
Operating Models
Scalability
Opex vs Capex
TCO
24/7 availability
Global impact
Business criticality

Key Topics
Regulatory &
Compliance
Customer Expectations Startups
(Social, Media, FinTech /
etc.)
Cloud
TCO Talent

Key decision criteria
What to think about when choosing a modern data platform
Deployment Flexibility
On-premise, Private, Public, or Hybrid
without vendor lock-in
Reducing Complexity
Broad use case applicability to avoid
additional complexity
Agility
Accelerate time to market and speed
of change for the business
Resiliency
Engineered for high availability
across distributed architectures
Scalability
Elastically grow with demand
Cost
Aligned to actual demand and value
but with predictability
Security
Leverage best in class and
appropriate security controls

Legacy
Legacy systems are falling short
RDBMS systems were not created for today’s requirements and consequently try to bolt-on features to compensate for the lack of
capabilities. But this strategy can’t compete with data systems purpose-built to solve today’s problems.
Rigid Schemas
Resistant to
change
Throughput & Cost
make Scale-Up
Impractical
Relational Model Scale-up
Data changes constantly, which fits
poorly with a relational model
Scale-Up clusters were never meant to
handle today’s volumes

MongoDB combines the best of Relational & NoSQL
Scalability
& Performance
Always On,
Global Deployments
Flexibility
Strong Consistency
Enterprise Management
& Integrations
NoSQL
Expressive Query Language
& Secondary Indexes
Relational

MongoDB – Multi-purpose operational data platform
MongoDB is the most powerful, holistic data management platform in the market today, helping you to reduce system complexity,
drastically lower TCO, increase productivity and minimise risk for critical operations.
Multi-Model database – rich use cases require “more”
than just relational queries (document, graph, search, etc)
Multi-Workload support – combine operational and
analytical workloads in a single, powerful data platform
Multi-structured, polymorphic data – real-life data
doesn’t fit into rows/columns and changes over time
Maximum deployment optionality – from on-premise and
VMs to hybrid/public cloud and Database-as-a-Service
K-V SQLDOC
Cloud / DBaaSOn-premise / self-managed
+

Strategic
SaaS, Mobile, Social
Microservices /
API Access / JSON
Polymorph Data (structured,
semi-structured, unstructured)
Hadoop, Spark
Commodity HW / Cloud
Local Storage / Cloud
Software-Defined Networks
MongoDB and Enterprise IT Strategy
Our technology can help you transform your IT organization and modernize the entire IT stack by enabling you leverage strategic solutions on
every level to drive business transformation.
Legacy
Apps On-Premise
Data Access
Object-Relational Mapping /
ODBC Access / SOAP
Database Oracle / Microsoft
Data Schemas Relational Data / Structured
Offline Data Teradata
Compute Scale-Up Server
Storage SAN
Network Routers and Switches
MongoDB sits right at the centre of
strategic IT and business / digital
transformation, enabling full stack
modernization.
By removing layers we can:
• Reduce complexity
• Reduce cost
• Increase business agility
• Improve data & service quality
• Facilitate innovation

Technology Landscape Transitions
Platform 1: Mainframes Platform 2: Client/Server Platform 3: Cloud/Mobile
1960s-1980s 1990s-2000s 2010-Beyond

Multi Cloud
On Premises
Desktop
Cloud
Self-Managed Fully Managed
MongoDB - Develop and run anywhere

Let our team help you on your journey to efficiently leverage the capabilities of MongoDB, the database that allows innovators to
unleash the power of software and data for giant ideas.
Being successful with MongoDB
We have worked with over 50% of the Fortune 500 companies. While the definition of success metrics
look different for each one of them, 2 key factors are consistent across all of our engagements:
5xProductivity
We help our customers to increase overall
output, e.g. in terms of development or ops
productivity.
80%Cost reduction
We help our customers to dramatically lower their total
cost of ownership for data storage and analytics by up
to 80%.

Problem Why MongoDB Results
Problem Solution Results
• With the advent of mobile banking,
Barclays has experienced a significant
growth of traffic originating from mobile
devices to Mainframe platforms that
supports banking applications. Growth
of traffic, which is expected to continue,
has led to an increased cost of
operations and decreased performance
• Ability to provide high resiliency during
mainframe outages
• Existing ETL processes that load
transaction data into Teradata on a daily
basis are updated to additionally feed
data to MongoDB paving the way for
decommissioning of Teradata. In
subsequent phases of the project,
MongoDB will be updated in near
real-time via a live transactions feed
• De-normalized real time data store using
MongoDB with the benefits to reduce
growth
• Stand-in capability to support Resiliency
during planned and unplanned outages
across mainframe system and other
source systems
• Reduced cost of operations
• Reduced number of read only
transactions to Mainframes , there by
freeing up mainframe resources for
additional growth
Operational Data Source
Data lake to store data from multiple sources for operations on the data. ODS
is built to store and process read only customer transactions for business
operations, analysis and reporting.

Problem Why MongoDB ResultsProblem Solution Results
Massive variability in data structured
ingested from customer systems:
highly concurrent batch loads and
continuous queries
Relational databases didn’t provide
schema flexibility or scalability
Hadoop was too complex
MongoDB Enterprise Advanced running on
Azure
Complex queries and aggregations to support
ad-hoc, exploratory queries
MongoDB Connector for BI to provide rich
visualizations in Tableau
MongoDB Cloud Manager for operational
automation and disaster recovery
First to market with unique
management accounting services
50% faster development time than
any other database
5x scale on same infrastructure
footprint
Cloud-Based Data Lake
Industry-first “benchmarking” service for 70,000 French businesses, built on
MongoDB & Azure
France

Problem Why MongoDB Results
Problem Solution Results
High licensing costs from proprietary
database and data grid technologies
Complex architecture and duplicated
processes across system stacks
Data duplication across siloed systems
with complex reconciliation controls
Tech estate simplification initiative
driven by cost efficiency programme
High operational complexity impacting
service availability & speed of
application delivery
Open source MongoDB, Kafka &
RabbitMQ that underpins several core
trading platforms
Rationalised data layer behind a
common API, implemented by a
multi-tenant PaaS, tackling common
data problems
Flexible data model and secondary
indexes to provide support for wide
variety of data and queries.
New solution is a self-service
Large scale cost reduction:
• £m license avoidance of incumbent
technologies
• Dramatic cost per TB storage saving
Technology simplification:
Roadmap to decommission hundreds
of servers
Increased Velocity:
• Develop new apps in days
• Self-service data service
Data Fabric (Data-as-a-Service)
Migration from Oracle & Microsoft to create a consolidated “data
fabric” reduces $m in cost, speeds application development &
simplifies operations

RBS FY 2016 Investor Report - Excerpt
During their recent FY 2016 Investor
Report, RBS CEO Ross McEwan
highlighted their Data Fabric platform
(built on MongoDB) as a key enabler to
helping the Bank reduce cost significantly
and dramatically increase the speed at
which RBS can deploy new capabilities.

Single Platform for Financial Data
Quantitative investment manager with over $11B in assets under
management invests heavily in new database
AHL needed new technologies to be
more agile and gain competitive
advantages in the systematic trading
space
Proprietary systems in financial services
tech, as well as relational databases,
were too expensive and/or rigid
Built single platform for all financial data
on MongoDB
Flexible data model and scalability were
core to ability to put all data in single
platform
Expressive query language, secondary
indexes and strong consistency were
core to ability to migrate core use cases
to new platform
100x faster to retrieve data
Tick Data: Quickly scaled to 250M ticks
per second, a 25x improvement in tick
throughput
Cut disk storage 60%, and realized 40%
cost savings by using commodity SSDs

Are you on a Blockchain DIET?
Trusted Data for Unstoppable Code
Kamal Ved

Blockchain DIET
Secure
Value
Exchange
Decentralized /
shared control
Immutable data
provenance &
audit trail
Tokenize
value attribution
Exchange
assets
Security & Trust
Value & Attribution

Blockchain:
“spreadsheet in the sky”
Decentralized, immutable storage
Append-only > versioned > audit trails
Tokens, assets, value
“world computer”
Unstoppable code
Smart contracts
Legally binding execution?

Why you need a Blockchain DIET?

ISS Global Partnership for Space Advancement
Source: https://www.nasa.gov/mission_pages/station/research/news/global_partnerships

“Self Driving cars are the killer app for AI”
-Madrona Venture Group
Problem: Data is Expensive
Sol’n: A Decentralized Data Exchange for
Self-Driving Car Data

Mo’ data
(and mo’ compute)
Mo’ accuracy
Mo’ $

To blockchain or not to blockchain?
Are you the middleman?
Can you be replaced by a smart contract?
Do you have multiple sources of truth?
Ecosystem – can you reconcile & settle
faster?

The Innovator's Dilemma: When New Technologies Cause
Great Firms to Fail
Source: Clayton Christensen’s Popular Book - http://www.claytonchristensen.com/books/the-innovators-dilemma/

Retail / (e)Commerce / Other Disruptions

Ripple vs SWIFT: Payment (r)evolution
● Cross border payments slow, expensive and opaque
● Ripple offers sub-second efficiently priced payments
● Swift had to respond with Global Payments Initiative (GPII)
Source: https://www.linkedin.com/pulse/ripple-vs-swift-payment-revolution-david-blair

Status quo compute infrastructure
Modern apps use processing, files, data
FILE SYSTEM
e.g. S3, HDFS
APPLICATION
PROCESSING
e.g. EC2, Azure
DATABASE
e.g. MySQL, MongoDB
PLATFORM
e.g. AWS, Google App Engine, Heroku
CONNECTNETWORKS
e.g.TCP/IP

Towards a decentralized compute infrastructure
FILE SYSTEM
e.g. S3, HDFS
IPFS, SWARM
APPLICATION
PROCESSING
e.g. EC2, Azure, Ethereum, Hyperledger, Tendermint, Lisk, Corda, Tymlez
DATABASE
e.g. MySQL, MongoDB
BigchainDB, IPDB
PLATFORM
e.g. AWS, Google App Engine, Heroku, Eris/Monax, BlockApps, Tymlez
CONNECTNETWORKS
e.g.TCP/IP,InterledgerILP
e-Cash/e-Gold
Bitcoin, zCash, Ripple,
Blockstream, Multichain

130 GB
2-7 tx/s
What about planetary scale?

The first “Blue Ocean” DBs: Relational
DBs
Benefits: powerful structured querying
Winner: Oracle, 80s and 90s

The next “Blue Ocean” DB:
Website-ready DBs
New benefits: lightweight for
startups
Winner: MySQL, early 2000s

The next “Blue Ocean” DB: Distributed / NoSQL DBs
New benefits: “Big data” scale, flexible schemas
Winner: MongoDB, late 2000s-now

How do “big data” databases scale?
Answer: Distribute storage across many machines, i.e. sharding
A “consensus” algorithm keeps
distributed nodes in sync.

0 5
0
10
0
15
0
20
0
25
0
30
0
35
0
0
200,00
0
400,00
0
600,00
0
800,00
0
1,200,00
0
175,00
0
367,00
0
537,00
0
1,100,00
0
Nodes
Writes/s
How well do “big data” distributed DBs scale?
Example: Cassandra scaling.
More nodes = more throughput, more
capacity!

The next blue ocean DB: blockchain database
New benefits: decentralized, immutable, native assets
Who: BigchainDB

How to build a scalable blockchain database
(BigchainDB)
1. Start with an enterprise-grade distributed DB, e.g.
MongoDB
2. Engineer in blockchain characteristics

Alice
Bob
Blockchain
consensus
Add tolerance to
double-spends +
other byzantine
faults
DB consensus
Fault-tolerant consensus

“Big Data” + “Blockchain”
- a decentralized database
Immutability
Decentralized Control
Native Assets
Scalable
Queryability
Operationalized
Traditional
Databases
Traditional
blockchains
BigchainDB

Kamal Ved
kamal@bigchaindb.com
https://www.linkedin.com/in/kamalved
Thanks for listening

Vertical:
Intellectual Property
Digital art
Value proposition:
Compensate creators
Claiming attribution
Licensing

Vertical:
Identity
Value proposition:
Sovereign personal data
General Data Protection Reg.
Right to be forgotten
©ITU/L.Berney, (CC BY 2.0)

Vertical:
Identity
Education Credentials for HR
Value proposition:
Reduce fraudulent degrees
Lower HR friction

Blockchain: a trust network for supply chains
Powered by IoT
75
Recordkeeping
paper, sms, email, calls, …
Inefficiency
change records, own versions/formats, disagreements

DATABASE
IPDB
CLIENT SIDE APP
BROWSER/JS OR MOBILE APP
A digital passport for Cars

Problem: Data Hoarding (2)
Sol’n: Data Pooling For More
Accurate Models
Diamond price prediction for
fraud detection:
Warn if predicted price !≈
asking price

MongoDB, the DB of choice for multiple Use Cases
Benjamin Lorenz
Senior Solutions Architect, MongoDB
benjamin.lorenz@mongodb.com

Cases for Change
MongoDB is a modern, operational database that supports a polyglot data strategy – on-premise and in the cloud. This allows us to drive
several business critical topics with our customers.
Cloud Data Strategy
Leveraging the right data platforms as part of your overall cloud
strategy helps to avoid vendor lock-in.
Legacy Modernisation
Current legacy technology stacks can’t cope with the range of
new business requirements – we can help you modernise in a
highly efficient and effective way.
Mainframe Offloading
Reduce cost and MIPS on legacy mainframes and enable data to
be leveraged for new use cases.
Operational Intelligence
Solving the problem of deriving value from existing EDW or
Hadoop-based data lake solutions in real-time.
Single View
Provide a holistic view of data entities (e.g. customer) across multiple
underlying, disconnected source systems.
Compliance & Regulation (e.g. PSD2)
MongoDB is enabling scalable, highly available data platforms to banks
who are forced to provide data in a more agile way to comply with the
PSD2 regulations.
Internet of Things (IoT)
MongoDB can help you overcome Scalability & Performance issues that
are not being met by many current IoT solutions

Definition: Operational Data Layer (ODL)
• An Operational Data Layer (ODL) is a database designed to allow disparate systems to
persist their data in a common schema, regardless of structure and incorporate scale-out
capabilities for resilience and scalability
• An ODL is designed to span deployment environments, from Private to Public to Hybrid and
Cross Cloud topologies.
• An ODL must be able to natively interoperate with various application types
• ODL is able to hold multi-structured data and if needed grow into the petabyte scale
• Other properties of an ODL are the ability to interoperate with API integration frameworks,
facilitating continuous integration

Problem / Solution Overview
RDBMS Files
Mainframe
Application
Microservices / API Layer
ReadsWrites
Key/Value Store
Files
Mainframe
Application
Typical Architecture
Complex & Fragile
Operational Data Layer (ODL)
Simplified & Resilient
Application Application Application
In-Memory Cache
RDBMS
Wide-Column
Store
Application Application
Non-standard data access Standardised Data Access
Near Real-Time
CDC
Message
Streaming/Proce
ssing
Graph Store
Replication

Characteristics: Operational Data Layer (ODL)
• Supports Structured, Semi-Structured
and Un-Structured data with the same
level of functionality
• Native drivers connect applications to
data without need for conversion
(JSON)
• Multi-tenancy through use of a common
data model
• Native support for all deployment types
• On-premise/Bare Metal, Private, Public, Hybrid
and Cross Clouds
• Scale-out architecture supports all
deployment types in mixed mode
• Information Lifecycle Management easily
managed by workload and geography
Data Agnostic Deployment Agnostic&

The problem Operational Data Layers solve
• Reliance on legacy systems (Mainframe, RDBMS)
• High profile outages & downtime
• Slow time to market
• Complexity & risk of change
• Regulatory requirement to open backend systems to public APIs
Enterprises are forced to simplify their technology landscape

Why MongoDB for Operational Data Layer
• Workload isolation
• Expressive queries & secondary indexes
• Data model flexibility with a dynamic schema
• Real-time analytics
• Performance, scale & always-on
• Enterprise deployment model

Mainframe Client-Server Web
Control & Efficiency Agility & Innovation
Cloud & Mobile
Distributed,
NoSQL Databases
1980s Mid 90s – 2000s 2015>1960s-70s
The Cloud Era: Platform Transformation

Seismic Shifts — Organizational

Seismic Shifts — Application Architecture

API Access Layer
Operational Data
Customers
Products
Accounts
ML Models
Shared Physical Infrastructure
App1 App2 App3
Development agility
– UI for self-service provisioning & scaling
Data Re-use
– Each service’s data is physically isolated into its own
database instance
– Federated across services with appropriate
permissioning
Corporate Governance
– Logically managed as one service
Cloud Data Strategy
Standardized, On-Demand DB Service
Cloud Agnostic
Any Cloud, Anywhere

#MDBE16
Cloud ManagerOps Manager MongoDB Atlas
Private DBaaS:
On-Prem
Eliminating Lock-In
Hybrid DBaaS Public DBaaS: Fully
Managed
Same Code Base, Same API, Same Management UI

Why MongoDB for Cloud Data Strategy?
• Freedom of choice
• On premise and as managed service
• Same code base everywhere

Why MongoDB Atlas?
• Ready for developers and DevOps
• Scalable back-end for your application on-demand
• Secure by default
• High available, even while scaling
• Path maintenance performed for you
• Your own MongoDB cluster in the cloud (multitenant)

IoT App Running on MongoDB Atlas
Biotechnology giant uses MongoDB Atlas to allow their customers to track
experiments from any mobile device
Thermo Fisher is developing Thermo Fisher
Cloud, one of the largest cloud platforms for the
scientific community on AWS
For scientific IoT applications, internal developers
need a database that could easily handle a wide
variety of fast-changing data
Each experiment produces millions of “rows” of
data, which led to suboptimal performance with
incumbent database
Thermo Fisher customers need to be able to slice
and dice their data in many different ways
MS instrument Connect allows Thermo
Fisher customers to see live experiment
results from any mobile device or browser
MongoDB’s expressive query language and
rich secondary indexes provide flexibility
to support both ad-hoc and predefined
queries to support customers’ scientific
experiments
Deployed MongoDB using MongoDB Atlas,
a hosted DB service running on Amazon
EC2
Thermo Fisher customers now can obtain
real-time insights from mass spectrometry
experiments from any mobile device or
browser; not possible before
Improved developer productivity with 40x
less code in testing with MongoDB when
compared to incumbent databases
Improved performance by 6x
Easy migration process & zero downtime.
Testing to production in under 2 months

Key decision criteria
What to think about when choosing a cloud data platform
Deployment Flexibility
On-premise, Private, Public, or Hybrid
without vendor lock-in
Reducing Complexity
Broad use case applicability to avoid
additional complexity
Agility
Accelerate time to market and speed
of change for the business
Resiliency
Engineered for high availability
across distributed architectures
Scalability
Elastically grow with demand
Cost
Aligned to actual demand and value
but with predictability
Security
Leverage best in class and
appropriate security controls

Conclusion
1 MongoDB is reshaping the DB
Management Landscape
2 Time to Market, Developer
Productivity and TCO are driving
this Change
3 Engage with your local MongoDB
Team

Resources to Get Started
Spin up a cluster on the
Free Tier today
Download the Whitepaper

Thank you.Thank You.
www.mongodb.com

MongoDB Zürich event overview

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