The document provides an overview of MongoDB and how it addresses the requirements of modern applications and enterprises. It discusses how traditional databases struggle with new demands around dynamic schemas, large volumes of data, and agile development. MongoDB supports these requirements through features like document data structures, horizontal scaling, and high performance. Case studies demonstrate how MongoDB has helped organizations build real-time views of customer data, virtualize legacy systems, and improve data distribution. The document concludes by discussing best practices for enterprise adoption of MongoDB.

1. An Enterprise Architect’s View of
MongoDB
Matt Kalan
Business Architect
matt.kalan@mongodb.com
@matthewkalan

2. Agenda
• Modern drivers of change on enterprises
• Requirements these create
• How traditional databases are handling changes
• New capabilities needed
• How MongoDB provides these capabilities
• Case studies
• Enterprise adoption
2

3. Modern Requirements

4. More Technologies and Requirements
Than Ever
Opportunity cost
NoSQL
Analytics
Globalization
JSON
Big Data Datawarehouse
Customer 360
Document Data Stores
Key-value
Hadoop
ODS
MongoDB
Graph
Wide-column
Cloud Computing
Cross-channel
New Revenue Streams
Faster Competition
Emerging markets
Agile Development
Regulation
Internet of Things
Gamification
More with less
Mobile
Social networking
Empowered customers
Consumerization
Lowering TCO
4

5. Questions for Enterprise Architects
• What current and future requirements does all
this raise?
• How to prepare my enterprise to handle these?
• Which technologies and products will help me?
• How to bring them into my enterprise
successfully?
• How does old and new technology work together?
• What does the future state architecture look like?
5

6. Modern Application Requirements
Data Types & OOP
Volume of Data
New Architectures
• Object-oriented
• Petabytes of data
• Horizontal scaling
• Variably structured
• Trillions of records
• Unstructured (not tabular)
• Millions of queries per
second
• Commodity
servers
• Cloud computing
Agile Development
Single Views
• Iterative
• Disparate data
• Short development
cycles
• Intraday
• Fast time-to-market
6
RDBMS
• Cross-channel/silo
• Global

7. Impact of New Requirements Handled
with 40-year old Technology
• Customfield1…100 or separate tables
• Caching & ORMs
• Expensive hardware and storage
• Schema migration project
• One canonical schema
• Application-specific partitioning
• Use files instead of databases
• Schema change takes 6 months
7
Slow time-to-market
Agility lost
High cost
Failed projects
Business frustrated

9. How Do I Prepare My Enterprise?

10. What Could a Modern Database Do
to Make This Easier
• Dynamic and variable schemas
• Richly-structured data
• Much faster performance
• Easy horizontal scaling
• Low TCO
• Plus still maintaining capabilities
– Rich querying
– Strongly consistently data
10

11. Documents Support Modern Data
Relational
Document Data Structure
{
first_name: „Paul‟,
surname: „Miller‟
city: „London‟,
location: [45.123,47.232],
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000,
picture: <binary>, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}
11

12. MongoDB Supports Modern
Requirements
1. Dynamic Document
Schema
Application
2. Native language drivers
db.customer.insert({…})
db.customer.find({
name: ”John Smith”})
{ name: “John Smith”,
date: “2013-08-01”),
address: “10 3rd St.”,
phone: [
{ home: 1234567890},
{ mobile: 1234568138} ]
}
Driver
Mongos
3. High availability
Shard 2
Shard N
Primary
Primary
Primary
Secondary
Secondary
Secondary
- Replica sets
Shard 1
Secondary
…
5. Horizontal scalability
12
- Sharding
Secondary
Secondary
4. High performance
- Data locality
- Rich Indexes
- RAM

13. Global Deployment with Local
Read/Writes
Primary:LON
Secondary:NYC
Primary:NYC
Secondary:SYD
Secondary:LON
Secondary:SYD
Primary:SYD
Secondary:LON
Secondary:NYC
13

14. MongoDB Business Value
Faster Time to Market
Enabling New Apps
14
Lower TCO
Response Time & Scalability

15. When Does MongoDB Help?

16. Database Landscape
2010
1990
2000
RDBMS
Operational
Database
NoSQL
RDBMS
Key-Value/
Wide-column
Document DB
RDBMS
Datawarehousing
OLAP/DW
Hadoop
OLAP/DW
16

17. MongoDB-Hadoop Connector
Operational
Database
Processing
& Storage
MongoDB-Hadoop
Connector
•
•
•
•
•
17
Low latency
Rich fast querying
Request/response
Aggregations in database
Known data relationships
•
•
•
•
Longer jobs
Batch analytics
Highly parallel processing
Unknown data relationships

18. Operational Database Use Cases
MongoDB
RDBMSs
Key/Value or
Wide Column Stores
18

19. MongoDB 5th Most Popular Database
19

20. Leading Organizations Rely on MongoDB
20

21. Criteria for benefitting most from
MongoDB instead of RDBMS
 You want to aggregate data from multiple sources
 You want agile development and/or fastest time-to-market
 You expect the schema to change often
 You have variably or unstructured data (records might have different fields)
 Your data is hierarchical (i.e. hard to model in RDBMS), e.g. JSON
 You expect the data to grow quickly and want ease of scaling out
 You want the best performance possible for real-time read/write
 You want the lowest TCO and resources including with replication and caching
 Performance of database directly impacts user experience
 You want real-time analytics and aggregations
 You want location-based querying (distance from locations, within regions, etc.)
 You have challenges today with building canonical models, scale, TCO, or agility
21

22. Case Studies of Architectural
Capabilities

23. Difficult Issues Today
1. Performance and agility issues with RDBMS
2. Building a single view across disparate systems
3. Legacy systems often not real-time enabled
4. Master data can be hard to change and distribute
5. Operational applications are siloed
23

24. Challenge: Performance and agility
issues with RDBMS
Code
DB Schema
Application
24
XML Config
Object Relational
Mapping
Relational
Database

25. Solution: Match Data to Application
and Optimize Disk IOPS
Code
XML Config
DB Schema
Application
Object Relational
Mapping
Relational
Database
Code
Text Search
Rich
Queries
Application
25
Geospatial
Aggregatio
n
Map Reduce

26. Case Study
Uses MongoDB to power enterprise social
networking platform
Problem
• Complex SQL queries,
highly normalized
schema not aligned with
new data types
• Poor performance
• Lack of horizontal
scalability
26
Why MongoDB
Results
• Dynamic schemas
using JSON
• Flexibility to roll out new
social features quickly
• Ability to handle
complex data while
maintaining high
performance
• Sped up reads from 30
seconds to tens of
milliseconds
• Social network analytics
with lightweight
MapReduce
• Dramatically increased
write performance

27. Challenge: Building a single view
across disparate systems
Batch
Datamar
t
Batch
Datamar
t
Customer
Accounts
Loans
Loans
Silo 2
Loans
Web
…
Deposits
Deposits
Silo 3
Cards
Mobile
27
Batch
Datamar
t
Batch
Data
Warehouse
Reporting
Cards
Cards
Silo 1
Banking
Store
Issues
• Yesterday’s data
• Details lost
• Inflexible schema
• Slow performance
Impact
• What happened today?
• Worse customer
satisfaction
• Missed opportunities
• Lost revenue

28. Solution: Using dynamic schema and
easy scaling
Operational Data Hub
Customer
Accounts
CSR Application
Real-time or
Batch
Customer Portal
Loans
Loans
Silo 2
…
…
Deposits
Deposits
Silo 3
28
Operational
Reporting
Data
Warehouse
Strategic
Reporting
Cards
Cards
Silo 1
Benefits
• Real-time
• Complete details
• Agile
• Higher customer
retention
• Increase wallet share
• Proactive exception
handling

29. Case Study
Insurance leader generates coveted 360-degree view of
customers in 90 days – “The Wall”
Problem
•
No single view of
customer
•
145 yrs of policy data,
70+ systems, 15+ apps
Why MongoDB
• Agility – prototype in 5
days; production in 90
days
•
2 years, $25M in failing
to aggregate in RDBMS
• Dynamic schema & rich
querying – combine
disparate data into one
data store
•
Poor customer
experience
• Hot tech to attract top
talent
29
Results
• Unified customer view
available to all channels
• Increased call center
productivity
• Better customer
experience, reduced
churn, more upsell opps
• Dozens more projects
on same data platform

30. Challenge: Legacy systems often not
real-time enabled or too slow
Data
source 1
Batch copy
Application 1
Often not ready to expose as
enterprise services
• Mainframe
• Core systems
• Data Warehouses
• Not scalable system
Application 2
Data
source 2
…
Slow
request/response
30
Application 3
…
Data
source N
Batch copying of data many
times or requests are too slow
Application X
Changing source data affects X
systems
Impact
• Slow time to market
• Resource intensive
• Hard to change interfaces and
modernize system

31. Solution: Virtualize legacy systems
with a persistent caching service
Mainfram
e
Batch
Batch copy
API
Batch copy
Application 1
Application 2
EDW
…
…
Pub/sub
…
Core
system
Application 3
Application X
31
Benefits
• Faster time to market
• More agile in changing
sources
• Can modernize data sources
behind virtualization
• Infinite scale with low TCO

32. Case Study: Global Custodial Bank
Virtualize Enterprise Data Sources
Create a central data hub for accessing data across
the enterprise
Problem
• Found numerous pointto-point copies of data
• Change in one system
impacts multiple groups
• Response time on EDW
was too slow
• Wanted one central
data hub for most often
accessed data
32
Why MongoDB
Results
• Dynamic schema: can
• Data accessible by batch
normalize data as needed or REST layer in one place
and prioritized
• Customer portal response
• Performance: can handle times shrunk by 90%
all data in one logical DB
• Shorter development times
• Sharding: can add data
with more accessible hub
easily by scaling out
• Could modernize data
sources without changing
apps

33. Challenge: Master data can be hard
to change and distribute
Batch
Batch
Batch
Golden
Copy
Common issues
• Hard to change schema
of master data
• Data copied everywhere
and gets out of sync
Batch
Batch
Batch
Batch
Batch
Impact
• Process breaks from out
of sync data
• Business doesn’t have
data it needs
• Many copies creates
33
more management

34. Solution: Persistent dynamic cache
replicated globally
Real-time
Real-time
Real-time
Real-time
Real-time
Solution:
• Load into primary with
any schema
• Replicate to and read
from secondaries
Real-time
Real-time
Real-time
Benefits
• Easy & fast change at
speed of business
• Easy scale out for one
stop shop for data
• Low TCO
34

35. Case Study: Global bank
Reference Data Distribution
Distribute reference data globally in real-time for
fast local accessing and querying
Problem
• Delays up to 36 hours in
distributing data by batch
• Charged multiple times
globally for same data
• Incurring regulatory
penalties from missing
SLAs
• Had to manage 20
distributed systems with
same data
35
Why MongoDB
Results
• Dynamic schema: easy to • Will save about
load initially & over time
$40,000,000 in costs and
penalties over 5 years
• Auto-replication: data
distributed in real-time,
• Only charged once for data
read locally
• Data in sync globally and
• Both cache and database: read locally
cache always up-to-date
• Capacity to move to one
• Simple data modeling &
global shared data service
analysis: easy changes
and understanding

36. Reporting
Reporting
Silo 2
Transactions
Silo 2 Systems
Silo 3
Transactions
36
…
Silo 1 Systems
…
Silo 1
Transactions
Reporting
Challenge: Operational applications
are siloed
Silo 3 Systems
Impact
• Views are siloed
• Duplicate management
and data access layer
• Need another layer to
aggregate

37. Solution: Unified data services
Silo 2 Systems
…
…
…
…
Common persistence framework
Reporting
Silo 1 Systems
Silo 3 Systems
37
Benefit
• Each application can
still save its own data
• Data is already
aggregated for crosssilo reporting
• One cluster and data
access layer to manage

38. Case Study: Global Broker Dealer
Trade Mart for all OTC Trades
Distribute reference data globally in real-time for
fast local accessing and querying
Problem
• Each application had its
own persistence and
audit trail
• Wanted one unified
framework and
persistence for all
trades and products
• Needed to handle many
variable structures
across all securities
38
Why MongoDB
Results
• Dynamic schema: can
• Fast time-to-market using
save trade for all products the persistence framework
in one data service
• Store any structure of
• Easy scaling: can easily
products/trades without
keep trades as long as
changing a schema
required with high
• One consolidated trade
performance
store for auditing and
reporting

39. Enterprise Adoption

40. Example Adoption Path
Use of MongoDB
Widespread
Adoption
Operationally
Supported
Certified
MongoDB Practice
Defined
A Few Projects
One Project
Time
40

41. Traditional Data Integrity Enforcement
Application 1
Application 2
Application 3
41
RDBMS
•
•
•
Apps access DB directly
Data Integrity must be in the RDBMS
Schema implemented by a DBA

42. Modern Apps (SOA) - Data Access
Layer Should Enforce Data Integrity
•
•
Data Integrity and validations done in
Data Access Layer
Implemented in code
MongoDB Cluster
Application 1
Application 2
…
Application N
42
Data
Access
Layer
API on TCP/IP
…
REST/API/WS

43. Data Governance Benefits
• Greater adoption from natural developer
framework on common data models
• Easier for master data or upstream changes to
flow into MongoDB-backed apps
• MongoDB useful for distributing master data
• ETL providers support MongoDB most in NoSQL
43

44. MongoDB Partners (360+) &
Integration
Software & Services
Cloud & Channel
44
Hardware

45. Factors to Consider in Adoption
• SDLC and data governance for an application
• Enterprise-wide data governance (inter-app)
• Roles and responsibilities
• Training requirements
• Operations/production support
• Center of Excellence (COE)
• Process for choosing which DB to use
• How to work with other technologies in-house
45

46. Recommended Center of Excellence
Database Engineering & CoE
Database
Advisory
Services
Operational Database CoE
RDBMS
Engineering
Database
SMEs
MongoDB
Engineering
MongoDB
Incubator
(& cluster)
RDBMS PaaS
Engineering
MongoDBaaS
Engineering
Datawarehousing CoE
DW Product
Engineering
46
Database
SMEs
Hadoop
Incubator
Clusters
DW PaaS
Engineering
Hadoop PaaS
Engineering

47. Summary
• Enormous technology and business change today
• Old technologies not suited for many of them
• MongoDB is purpose built for today and future applications
• And can help solve common architectural challenges
• Bring MongoDB in to learn how to adopt it more widely when
appropriate
• Firms using MongoDB benefit from 50% time-to-market,
70% lower TCO, and making the infeasible possible
47

48. MongoDB Products and Services
Subscriptions
MongoDB Enterprise, Monitoring, Support, Commercial License
Consulting
Expert Resources for All Phases of MongoDB Implementations
Training
Online and In-Person for Developers and Administrators
MongoDB Monitoring Service
Free, Cloud-Based Service for Monitoring and Alerts
MongoDB Backup Service
Cloud-based service for backing up and restoring MongoDB
48

49. For More Information
Resource
MongoDB Downloads
mongodb.com/download
Free Online Training
education.mongodb.com
Webinars and Events
mongodb.com/events
White Papers
mongodb.com/white-papers
Case Studies
mongodb.com/customers
Presentations
mongodb.com/presentations
Documentation
docs.mongodb.org
Additional Info
49
Location
info@mongodb.com