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An Enterprise Architect’s View of 
MongoDB 
Matt Kalan 
Business Architect 
matt.kalan@mongodb.com 
@matthewkalan
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
Modern Requirements
More Technologies and Requirements 
Than Ever 
NoSQL 
Datawarehouse 
Hadoop 
Internet of Things 
4 
Document Data Stores 
Big Data 
Key-value 
JSON 
Wide-column 
MongoDB 
Cloud Computing 
Mobile 
Gamification 
Social networking 
Graph 
Agile Development 
ODS 
Analytics 
Consumerization
More Technologies and Requirements 
Than Ever 
NoSQL 
Datawarehouse 
Hadoop 
Internet of Things 
5 
Document Data Stores 
Big Data 
Key-value 
JSON 
Wide-column 
MongoDB 
Cloud Computing 
Mobile 
Gamification 
Social networking 
Graph 
Agile Development 
ODS 
Analytics 
Consumerization 
Opportunity cost 
Globalization 
Customer 360 
Cross-channel 
New Revenue Streams 
Faster Competition 
Emerging markets 
Regulation 
Data Monetization 
More with less 
Common Services 
Empowered customers 
Lowering TCO
Questions for Enterprise Architects 
• What current and future requirements does all 
6 
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?
Modern Application Requirements 
Data Types & OOP 
• Object-oriented 
• Variably structured 
• Unstructured (not tabular) 
7 
Volume of Data 
• Petabytes of data 
• Trillions of records 
• Millions of queries per 
second 
Agile Development 
• Iterative 
• Short development 
cycles 
• Fast time-to-market 
New Architectures 
• Horizontal scaling 
• Commodity 
servers 
• Cloud computing 
RDBMS 
Single Views 
• Disparate data 
• Intraday 
• Cross-channel/silo 
• Global
Modern Application Requirements 
Data Types & OOP 
• Object-oriented 
• Variably structured 
• Unstructured (not tabular) 
8 
Volume of Data 
• Petabytes of data 
• Trillions of records 
• Millions of queries per 
second 
Agile Development 
• Iterative 
• Short development 
cycles 
• Fast time-to-market 
New Architectures 
• Horizontal scaling 
• Commodity 
servers 
• Cloud computing 
RDBMS 
Single Views 
• Disparate data 
• Intraday 
• Cross-channel/silo 
• Global
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 
9
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 
10 
Slow time-to-market 
Agility lost 
High cost 
Failed projects 
Business frustrated
How Do I Prepare My Enterprise 
for Modern Requirements?
What would we need to make it 
easier? 
13 
New capabilities 
• Dynamic and variable 
schemas 
• Richly-structured [object] 
data 
• Higher performance 
• Easy horizontal scaling 
• Low TCO
What would we need to make it 
easier? 
• Dynamic and variable 
schemas 
• Richly-structured [object] 
data 
14 
New capabilities 
• Higher performance 
• Easy horizontal scaling 
• Low TCO 
Traditional capabilities 
• Rich querying 
• Strongly consistently data 
• High availability 
• Security
Documents Support Modern 
Requirements 
15 
Relational Document Data Structure 
{ customer_id : 1, 
first_name : "Mark", 
last_name : "Smith", 
city : "San Francisco", 
location : [40.74, -73.97], 
image : <binary>, 
phones: [ { 
number : “1-212-777-1212”, 
dnc : true, 
type : “home” 
}, 
{ 
number : “1-212-777-1213”, 
type : “cell” 
}] 
}
Modern DB Architecture 
16 
Application 
Driver 
Query Router 
Shard 1 
Primary 
Secondary 
Secondary 
Shard 2 
Primary 
Secondary 
Secondary 
… 
Shard N 
Primary 
Secondary 
Secondary 
db.customer.insert({…}) 
db.customer.find({ 
name: ”John Smith”}) 
1.Dynamic Document 
Schema 
{ name: “John Smith”, 
date: “2013-08-01”), 
address: “10 3rd St.”, 
phone: [ 
{ home: 1234567890}, 
{ mobile: 1234568138} ] 
} 
2. Native language drivers 
4. High performance 
- Data locality 
- Rich Indexes 
- RAM 
3. High availability 
- Replica sets 
5. Horizontal scalability 
- Sharding
MongoDB Built Exactly For These 
Requirements 
17 
Application 
Driver 
Query Router 
Shard 1 
Primary 
Secondary 
Secondary 
Shard 2 
Primary 
Secondary 
Secondary 
… 
Shard N 
Primary 
Secondary 
Secondary 
db.customer.insert({…}) 
db.customer.find({ 
name: ”John Smith”}) 
1.Dynamic Document 
Schema 
{ name: “John Smith”, 
date: “2013-08-01”), 
address: “10 3rd St.”, 
phone: [ 
{ home: 1234567890}, 
{ mobile: 1234568138} ] 
} 
2. Native language drivers 
4. High performance 
- Data locality 
- Rich Indexes 
- RAM 
3. High availability 
- Replica sets 
5. Horizontal scalability 
- Sharding
Performance 
18 
Better Data 
Locality 
In-Memory 
Caching 
In-Place 
Updates
No SQL But Still Flexible Querying 
19 
MongoDB 
Rich Queries 
• Find anyone with phone # “212…” 
• Check if the person with number 
“555…” is on the “do not call” list 
Geospatial 
• Find the best offer for the customer at 
geo coordinates of 42nd St. and 6th Ave 
Text Search 
• Find all tweets that mention the firm 
within the last 2 days 
Aggregation 
• Count and sort number of customers by 
city 
Map Reduce 
• For customers in each zip code, what 
are the top 5 most common products 
{ customer_id : 1, 
first_name : "Mark", 
last_name : "Smith", 
city : ”New York", 
phones: [ { 
number : “1-212-777-1212”, 
dnc : true, 
type : “home” 
}, 
{ 
number : “1-212-777-1213”, 
type : “cell” 
}] 
}
Security Capabilities 
• Kerberos 
• LDAP 
• x.509 certificates 
20 
• User-Defined Roles 
• Field Level Security 
• Admin Actions 
• CRUD operations 
• Partner support 
• SSL support on wire 
• Disk encryption 
support by partners
Global Deployment with Local 
Read/Writes 
21 
Primary:NYC 
Primary:LON 
Secondary:NYC 
Primary:SYD 
Secondary:LON 
Secondary:NYC 
Secondary:SYD 
Secondary:LON 
Secondary:SYD
MongoDB Business Value 
22 
Faster Time to Market Lower TCO 
Enabling New Apps Faster Response Time 
& Scalability
When to Use MongoDB?
Database Landscape 
24 
2010 
RDBMS 
NoSQL 
Key-Value/ 
Wide-column 
Hadoop 
OLAP/DW 
2000 
RDBMS 
OLAP/DW 
1990 
RDBMS 
Operational 
Database 
Datawarehousing 
Document DB
Database Landscape 
25 
2010 
RDBMS 
NoSQL 
Key-Value/ 
Wide-column 
Hadoop 
OLAP/DW 
2000 
RDBMS 
OLAP/DW 
1990 
RDBMS 
Operational 
Database 
Datawarehousing 
Document DB
MongoDB Hadoop Connector 
26 
Operational 
Database 
• Low latency 
• Rich fast querying 
• Flexible indexing 
• Aggregations in database 
• Known data relationships 
• Great for any subset of data 
Analytics 
• Longer jobs 
• Batch analytics 
• Highly parallel processing 
• Unknown data relationships 
• Great for looking at all data 
MongoDB-Hadoop 
Connector
Operational Database Use Cases 
27 
RDBMSs
Operational Database Use Cases 
28 
RDBMSs 
Key/Value or 
Wide Column Stores
Operational Database Use Cases 
29 
RDBMSs 
MongoDB 
Key/Value or 
Wide Column Stores
MongoDB 5th Most Popular Database 
30
Leading Organizations Rely on MongoDB 
31
Usage Patterns & Case Studies
Architecture Patterns 
1. Operational Data Store (ODS) 
2. Enterprise Data Service 
3. Datamart/Cache 
4. Master Data Distribution 
5. Single Operational View 
33
Architecture Patterns 
1. Operational Data Store (ODS) 
2. Enterprise Data Service 
3. Datamart/Cache 
4. Master Data Distribution 
5. Single Operational View 
34 
System of Record 
System of Engagement
Architectural Pattern – 
Operational Data Store (ODS)
Challenge: Applications not agile nor 
scalable enough 
36 
Requirement changes 
Change
Solution: Match dynamic data model 
to the application 
37
Criteria for benefitting most from 
MongoDB instead of RDBMS 
Data 
 Variably or 
unstructured 
 Hierarchical 
 Geo-coordinates 
 Disparate sources 
 Schema changes 
often 
38 
Querying 
 Real-time analytics & 
aggregations 
 Location-based 
 Lowest latency 
 Performance affects 
user experience 
Requirements 
 Agile development & 
fastest time-to-market 
 Data will grow quickly 
 Best performance for 
request/response 
 Lowest TCO 
 Multiple sources 
aggregated 
 Challenges today with 
RDBMS
ADP’s Global Mobile Platform 
One of the world's largest providers of payments solutions 
constructs a completely reliable and robust mobile 
experience 
39 
Problem Why MongoDB Results 
• Needed a signature 
mobile app for customers 
• Must support millions of 
users 
• Needed to quickly change 
features & functionality 
• High availability was 
critically important 
• Built-in high availability 
architecture optimized for 
global, multi-data center 
distribution 
• Dynamic schema & rich 
querying – deep 
functionality from launch & 
new features easily added 
• Much lower TCO, 
especially with commodity 
hardware 
• iTunes App Store “Top 15” 
business app since 2012 
launch 
• Over 1 million active users, 17 
countries, 23 languages 
• Extremely high performance 
through predictive caching 
• Maintenance much easier => 
simple codebase, less 
hardware 
• New functionality easy and 
quick to add
Architectural Pattern – 
Enterprise Data Service
Challenge: Siloed operational 
applications 
41 
Silo 1 Data 
Silo 2 Data 
… 
Silo N Data 
Impact 
• Views are siloed 
• Duplicate management 
and data access layer 
• Need another layer to 
aggregate 
Silo 1 systems 
Silo 2 Systems 
… 
Silo N 
Systems 
Reporting Reporting Reporting
Solution: Unified data services 
42 
… 
Benefit 
• Each application can still 
save its own data 
• Data is already aggregated 
for cross-silo reporting 
• One cluster and data access 
layer to manage 
Silo 1 Systems 
Silo 2 Systems 
… 
Silo N Systems 
Reporting 
……
Case Study: Global Broker Dealer 
Trade Mart for all OTC Trades 
Distribute reference data globally in real-time for 
fast local accessing and querying 
43 
Problem Why MongoDB Results 
• 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 
• Dynamic schema: can 
save trade for all products 
in one data service 
• Easy scaling: can easily 
keep trades as long as 
required with high 
performance 
• Rich querying: can query 
on any fields each 
business requires 
• Fast time-to-market using 
the persistence framework 
• Store any structure of 
products/trades without 
changing a schema 
• One consolidated trade 
store for auditing and 
reporting
Architectural Pattern – 
Datamart/Cache
Challenge: Response From Data 
Warehouse or Other System is Slow 
45 
Cards 
Loans 
… 
Deposits 
Data 
Warehouse 
Issues 
• Data stored normalized 
• Reports slow to generate 
• Data updated daily but user 
response must be fast 
Impact 
• Lost productivity 
• Dissatisfied users and 
business 
Reporting 
Cards 
Silo 1 
Loans 
Silo 2 
Deposits 
Silo 3
Solution: Optimize Data Structure as a 
Datamart In-memory or On-disk 
Cards 
Loans 
Deposits 
46 
… 
Data 
Warehouse 
Solution 
• Data stored in optimal 
structure for reports 
• Optionally in memory 
Impact 
• Response times is as fast 
as possible 
• Users and business 
satisfied 
Fast Reporting 
Cards 
Silo 1 
Loans 
Silo 2 
Deposits 
Silo 3 
Datamart/Cache 
…
Case Study: Global Bank - 
Personalized In-memory Datamart 
Needed fast reporting for finance on global 
banking transaction data (about 2 petabytes) 
47 
Problem Why MongoDB Results 
• Data warehouse was 
too slow for reporting 
• No visibility into how 
long reports took 
• Could not generate 
multiple ad hoc reports 
• Users included 
regulators so even 
more demanding 
• Dynamic schema: store 
data in optimal structure 
• Performance: storing 
report results optimally 
• In-memory caching of 
results 
• Rich querying: can query 
on any field 
• Easy scaling: results 
spread across shards to 
generate report in parallel 
• Create a personalized in-memory 
data mart 
• Reports configured and 
notified when results ready 
• Data all in memory so fast 
to manipulate 
• Data spread across shards 
for ultra-fast reporting
Architectural Pattern – 
Master Data Distribution
Challenge: Master data can be hard 
to change and distribute 
49 
Golden 
Copy 
Batch 
Batch 
Batch 
Batch 
Batch 
Batch 
Batch 
Batch 
Common issues 
• Hard to change schema 
of master data 
• Data copied everywhere 
and gets out of sync 
Impact 
• Process breaks from out 
of sync data 
• Business doesn’t have 
data it needs 
• Many copies creates 
more management
Solution: Persistent dynamic cache 
replicated globally 
50 
Real-time 
Real-time Real-time 
Real-time 
Real-time 
Real-time 
Real-time 
Real-time 
Solution: 
• Load into primary with 
any schema 
• Replicate to and read 
from secondaries 
Benefits 
• Easy & fast change at 
speed of business 
• Easy scale out for one 
stop shop for data 
• Low TCO
Case Study: Global bank 
Reference Data Distribution 
Distribute reference data globally in real-time for 
fast local accessing and querying 
51 
Problem Why MongoDB Results 
• 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 
• Dynamic schema: easy to 
load initially & over time 
• Auto-replication: data 
distributed in real-time, 
read locally 
• Both cache and database: 
cache always up-to-date 
• Simple data modeling & 
analysis: easy changes 
and understanding 
• Will save about 
$40,000,000 in costs and 
penalties over 5 years 
• Only charged once for data 
• Data in sync globally and 
read locally 
• Capacity to move to one 
global shared data service
Architectural Pattern – 
Single Operational View
Challenge: Aggregation of disparate 
data is difficult 
53 
Cards 
Loans 
… 
Deposits 
Data 
Warehouse 
Batch 
Cross-Silo 
applications 
Issues 
• Yesterday’s data 
• Details lost 
• Inflexible schema 
• Slow performance 
Datamar 
t 
Datamar 
t 
Datamar 
t 
Batch 
Impact 
• What happened today? 
• Worse customer 
satisfaction 
• Missed opportunities 
• Lost revenue 
Batch 
Batch 
Reporting 
Cards 
Silo 1 
Loans 
Silo 2 
Deposits 
Silo 3
Solution: Using dynamic schema and 
easy scaling 
54 
Operational Single View Benefits 
Data 
Warehouse 
Real-time or 
Batch 
… 
Customer-facing 
Applications 
Regulatory 
applications 
• Real-time 
• Complete details 
• Agile 
• Higher customer 
retention 
• Increase wallet share 
• Proactive exception 
handling 
Strategic 
Reporting 
Operational 
Reporting 
Cards 
Loans 
… 
Deposits 
Customer 
Accounts 
Cards 
Silo 1 
Loans 
Silo 2 
Deposits 
Silo N
Case Study 
Insurance leader generates coveted 360-degree view of 
customers in 90 days – “The Wall” 
55 
Problem Why MongoDB Results 
• No single view of 
customer 
• 145 yrs of policy data, 
70+ systems, 15+ apps 
• 2 years, $25M in failing 
to aggregate in RDBMS 
• Poor customer 
experience 
• Agility – prototype in 5 
days; production in 90 
days 
• Dynamic schema: 
Imperative to combine 
disparate data 
• Rich querying: necessary 
for match data across silos 
• Hot tech to attract top 
talent 
• 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
Single [Operational] View of …. 
Cards 
Silo 1 
Loans 
Silo 2 
56 
Operational 
Reporting 
Real-time 
or Batch 
… 
Single CSR 
Application 
Unified 
Customer Portal 
Operational Data Layer 
Cards 
Loans 
… 
Deposits 
Deposits 
Silo N 
Strategic 
Reporting 
… 
• Millisecond latency 
• Request/response 
• Easily scalable 
• Flexible schema 
• Low TCO 
• Rich querying 
• Globally distributed 
DW/Analytic Data Layer 
• Analytical/Offline processing 
• 10s seconds to hours latency 
• Also scalable, low TCO, and 
flexible 
• Pre-defined slices of data 
(few indexes) 
Analytics/Batch 
processing 
MongoDB 
Hadoop Connector 
…
Processing + Data Access Paradigm 
Processing 
model 
Data access 
model 
57 
Request/response 
Map-reduce 
Batch, ETL, etc. 
Analytical Jobs 
Latency important (e.g. 
user waiting) 
Milliseconds to seconds 
Small to large subsets 
of data 
Indexes valuable 
Multiple seconds to hours 
Processing all or large sets 
of data 
Indexes not used
Processing + Data Access Paradigm 
Processing 
model 
Data access 
model 
58 
Request/response 
Map-reduce 
Batch, ETL, etc. 
Analytical Jobs 
Latency important (e.g. 
user waiting) 
Milliseconds to seconds 
Small to large subsets 
of data 
Indexes valuable 
Multiple seconds to hours 
Processing all or large sets 
of data 
Indexes not used 
Typical MongoDB 
Use Case
Processing + Data Access Paradigm 
Processing 
model 
Data access 
model 
59 
Request/response 
Map-reduce 
Batch, ETL, etc. 
Analytical Jobs 
Latency important (e.g. 
user waiting) 
Milliseconds to seconds 
Small to large subsets 
of data 
Indexes valuable 
Multiple seconds to hours 
Processing all or large sets 
of data 
Indexes not used 
Typical MongoDB 
Use Case 
Typical Hadoop 
Use Case
Processing + Data Access Paradigm 
Processing 
model 
Data access 
model 
60 
Request/response 
Map-reduce 
Batch, ETL, etc. 
Analytical Jobs 
Latency important (e.g. 
user waiting) 
Milliseconds to seconds 
Small to large subsets 
of data 
Indexes valuable 
Multiple seconds to hours 
Processing all or large sets 
of data 
Indexes not used 
Typical MongoDB 
Use Case 
Typical Hadoop 
Use Case
Processing + Data Access Paradigm 
Processing 
model 
Data access 
model 
61 
Request/response 
Map-reduce 
Batch, ETL, etc. 
Analytical Jobs 
Latency important (e.g. 
user waiting) 
Milliseconds to seconds 
Small to large subsets 
of data 
Indexes valuable 
Multiple seconds to hours 
Processing all or large sets 
of data 
Indexes not used 
Typical MongoDB 
Use Case 
Typical Hadoop 
Use Case 
Data 
Discovery
Enterprise Adoption
Example Adoption Path 
Use of MongoDB 
63 
One Project 
MongoDB CoE 
A Few Projects 
Certified 
Widespread 
Adoption 
Operationally 
Supported 
Time 
Defined
Traditional Data Integrity Enforcement 
64 
RDBMS 
• Apps access DB directly 
• Data Integrity must be in the RDBMS 
• Schema implemented by a DBA 
Application 1 
Application 2 
Application 3
Modern Apps (SOA) - Data Access 
Layer Should Enforce Data Integrity 
Application 1 
65 
MongoDB Cluster 
Application 2 
• Data Integrity and validations done in 
• Implemented in code 
Data 
Access 
Layer 
… 
Application N 
… 
Data Access Layer 
REST/API/WS API on TCP/IP
Data Governance Benefits 
• Greater adoption from natural developer 
66 
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
Partner Ecosystem (500+) 
67
Factors to Consider in Adoption 
• SDLC and data governance for an application 
• Enterprise-wide data governance (inter-app) 
• Enterprise-wide security 
• 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 
68
Recommended Center of Excellence 
69 
Database Engineering & CoE 
Operational Database CoE 
Datawarehousing CoE
Recommended Center of Excellence 
70 
Database Engineering & CoE 
Database 
Advisory 
Services 
Operational Database CoE 
Datawarehousing CoE
Recommended Center of Excellence 
71 
RDBMS 
Engineering 
Database Engineering & CoE 
Database 
Advisory 
Services 
Operational Database CoE 
Datawarehousing CoE
Recommended Center of Excellence 
72 
RDBMS 
Engineering 
Database 
Advisory 
Services 
Operational Database CoE 
Database 
SMEs 
Database Engineering & CoE 
Datawarehousing CoE
Recommended Center of Excellence 
73 
Database 
Advisory 
Services 
MongoDB 
Engineering 
RDBMS 
Engineering 
Operational Database CoE 
Database 
SMEs 
Database Engineering & CoE 
Datawarehousing CoE
Recommended Center of Excellence 
74 
Database 
Advisory 
Services 
MongoDB 
Engineering 
RDBMS 
Engineering 
Operational Database CoE 
MongoDB 
Incubator 
(& cluster) 
Database 
SMEs 
Database Engineering & CoE 
Datawarehousing CoE
Recommended Center of Excellence 
75 
Database 
Advisory 
Services 
MongoDB 
Engineering 
RDBMS 
Engineering 
Operational Database CoE 
MongoDB 
Incubator 
(& cluster) 
Database 
SMEs 
Database Engineering & CoE 
RDBMS PaaS 
Engineering 
MongoDBaaS 
Engineering 
Datawarehousing CoE
Recommended Center of Excellence 
76 
MongoDB 
Engineering 
RDBMS 
Engineering 
Operational Database CoE 
MongoDB 
Incubator 
(& cluster) 
Database 
SMEs 
Database Engineering & CoE 
RDBMS PaaS 
Engineering 
MongoDBaaS 
Engineering 
DW Product 
Engineering 
Datawarehousing CoE 
Hadoop 
Incubator 
Clusters 
Database 
SMEs 
DW PaaS 
Engineering 
Hadoop PaaS 
Engineering 
Database 
Advisory 
Services
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, Inc. in to learn how to adopt it more widely 
77 
when appropriate 
• Firms using MongoDB benefit from 50% time-to-market, 
70% lower TCO, lower operating costs, and making the 
infeasible possible
We’re your partner 
78
For More Information 
79 
Resource Location 
Resource Location 
MongoDB Downloads mongodb.com/download 
Free Online Training university.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 info@mongodb.com
An Enterprise Architect's View of MongoDB

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An Enterprise Architect's View 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
  • 4. More Technologies and Requirements Than Ever NoSQL Datawarehouse Hadoop Internet of Things 4 Document Data Stores Big Data Key-value JSON Wide-column MongoDB Cloud Computing Mobile Gamification Social networking Graph Agile Development ODS Analytics Consumerization
  • 5. More Technologies and Requirements Than Ever NoSQL Datawarehouse Hadoop Internet of Things 5 Document Data Stores Big Data Key-value JSON Wide-column MongoDB Cloud Computing Mobile Gamification Social networking Graph Agile Development ODS Analytics Consumerization Opportunity cost Globalization Customer 360 Cross-channel New Revenue Streams Faster Competition Emerging markets Regulation Data Monetization More with less Common Services Empowered customers Lowering TCO
  • 6. Questions for Enterprise Architects • What current and future requirements does all 6 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?
  • 7. Modern Application Requirements Data Types & OOP • Object-oriented • Variably structured • Unstructured (not tabular) 7 Volume of Data • Petabytes of data • Trillions of records • Millions of queries per second Agile Development • Iterative • Short development cycles • Fast time-to-market New Architectures • Horizontal scaling • Commodity servers • Cloud computing RDBMS Single Views • Disparate data • Intraday • Cross-channel/silo • Global
  • 8. Modern Application Requirements Data Types & OOP • Object-oriented • Variably structured • Unstructured (not tabular) 8 Volume of Data • Petabytes of data • Trillions of records • Millions of queries per second Agile Development • Iterative • Short development cycles • Fast time-to-market New Architectures • Horizontal scaling • Commodity servers • Cloud computing RDBMS Single Views • Disparate data • Intraday • Cross-channel/silo • Global
  • 9. 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 9
  • 10. 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 10 Slow time-to-market Agility lost High cost Failed projects Business frustrated
  • 11.
  • 12. How Do I Prepare My Enterprise for Modern Requirements?
  • 13. What would we need to make it easier? 13 New capabilities • Dynamic and variable schemas • Richly-structured [object] data • Higher performance • Easy horizontal scaling • Low TCO
  • 14. What would we need to make it easier? • Dynamic and variable schemas • Richly-structured [object] data 14 New capabilities • Higher performance • Easy horizontal scaling • Low TCO Traditional capabilities • Rich querying • Strongly consistently data • High availability • Security
  • 15. Documents Support Modern Requirements 15 Relational Document Data Structure { customer_id : 1, first_name : "Mark", last_name : "Smith", city : "San Francisco", location : [40.74, -73.97], image : <binary>, phones: [ { number : “1-212-777-1212”, dnc : true, type : “home” }, { number : “1-212-777-1213”, type : “cell” }] }
  • 16. Modern DB Architecture 16 Application Driver Query Router Shard 1 Primary Secondary Secondary Shard 2 Primary Secondary Secondary … Shard N Primary Secondary Secondary db.customer.insert({…}) db.customer.find({ name: ”John Smith”}) 1.Dynamic Document Schema { name: “John Smith”, date: “2013-08-01”), address: “10 3rd St.”, phone: [ { home: 1234567890}, { mobile: 1234568138} ] } 2. Native language drivers 4. High performance - Data locality - Rich Indexes - RAM 3. High availability - Replica sets 5. Horizontal scalability - Sharding
  • 17. MongoDB Built Exactly For These Requirements 17 Application Driver Query Router Shard 1 Primary Secondary Secondary Shard 2 Primary Secondary Secondary … Shard N Primary Secondary Secondary db.customer.insert({…}) db.customer.find({ name: ”John Smith”}) 1.Dynamic Document Schema { name: “John Smith”, date: “2013-08-01”), address: “10 3rd St.”, phone: [ { home: 1234567890}, { mobile: 1234568138} ] } 2. Native language drivers 4. High performance - Data locality - Rich Indexes - RAM 3. High availability - Replica sets 5. Horizontal scalability - Sharding
  • 18. Performance 18 Better Data Locality In-Memory Caching In-Place Updates
  • 19. No SQL But Still Flexible Querying 19 MongoDB Rich Queries • Find anyone with phone # “212…” • Check if the person with number “555…” is on the “do not call” list Geospatial • Find the best offer for the customer at geo coordinates of 42nd St. and 6th Ave Text Search • Find all tweets that mention the firm within the last 2 days Aggregation • Count and sort number of customers by city Map Reduce • For customers in each zip code, what are the top 5 most common products { customer_id : 1, first_name : "Mark", last_name : "Smith", city : ”New York", phones: [ { number : “1-212-777-1212”, dnc : true, type : “home” }, { number : “1-212-777-1213”, type : “cell” }] }
  • 20. Security Capabilities • Kerberos • LDAP • x.509 certificates 20 • User-Defined Roles • Field Level Security • Admin Actions • CRUD operations • Partner support • SSL support on wire • Disk encryption support by partners
  • 21. Global Deployment with Local Read/Writes 21 Primary:NYC Primary:LON Secondary:NYC Primary:SYD Secondary:LON Secondary:NYC Secondary:SYD Secondary:LON Secondary:SYD
  • 22. MongoDB Business Value 22 Faster Time to Market Lower TCO Enabling New Apps Faster Response Time & Scalability
  • 23. When to Use MongoDB?
  • 24. Database Landscape 24 2010 RDBMS NoSQL Key-Value/ Wide-column Hadoop OLAP/DW 2000 RDBMS OLAP/DW 1990 RDBMS Operational Database Datawarehousing Document DB
  • 25. Database Landscape 25 2010 RDBMS NoSQL Key-Value/ Wide-column Hadoop OLAP/DW 2000 RDBMS OLAP/DW 1990 RDBMS Operational Database Datawarehousing Document DB
  • 26. MongoDB Hadoop Connector 26 Operational Database • Low latency • Rich fast querying • Flexible indexing • Aggregations in database • Known data relationships • Great for any subset of data Analytics • Longer jobs • Batch analytics • Highly parallel processing • Unknown data relationships • Great for looking at all data MongoDB-Hadoop Connector
  • 27. Operational Database Use Cases 27 RDBMSs
  • 28. Operational Database Use Cases 28 RDBMSs Key/Value or Wide Column Stores
  • 29. Operational Database Use Cases 29 RDBMSs MongoDB Key/Value or Wide Column Stores
  • 30. MongoDB 5th Most Popular Database 30
  • 31. Leading Organizations Rely on MongoDB 31
  • 32. Usage Patterns & Case Studies
  • 33. Architecture Patterns 1. Operational Data Store (ODS) 2. Enterprise Data Service 3. Datamart/Cache 4. Master Data Distribution 5. Single Operational View 33
  • 34. Architecture Patterns 1. Operational Data Store (ODS) 2. Enterprise Data Service 3. Datamart/Cache 4. Master Data Distribution 5. Single Operational View 34 System of Record System of Engagement
  • 35. Architectural Pattern – Operational Data Store (ODS)
  • 36. Challenge: Applications not agile nor scalable enough 36 Requirement changes Change
  • 37. Solution: Match dynamic data model to the application 37
  • 38. Criteria for benefitting most from MongoDB instead of RDBMS Data  Variably or unstructured  Hierarchical  Geo-coordinates  Disparate sources  Schema changes often 38 Querying  Real-time analytics & aggregations  Location-based  Lowest latency  Performance affects user experience Requirements  Agile development & fastest time-to-market  Data will grow quickly  Best performance for request/response  Lowest TCO  Multiple sources aggregated  Challenges today with RDBMS
  • 39. ADP’s Global Mobile Platform One of the world's largest providers of payments solutions constructs a completely reliable and robust mobile experience 39 Problem Why MongoDB Results • Needed a signature mobile app for customers • Must support millions of users • Needed to quickly change features & functionality • High availability was critically important • Built-in high availability architecture optimized for global, multi-data center distribution • Dynamic schema & rich querying – deep functionality from launch & new features easily added • Much lower TCO, especially with commodity hardware • iTunes App Store “Top 15” business app since 2012 launch • Over 1 million active users, 17 countries, 23 languages • Extremely high performance through predictive caching • Maintenance much easier => simple codebase, less hardware • New functionality easy and quick to add
  • 40. Architectural Pattern – Enterprise Data Service
  • 41. Challenge: Siloed operational applications 41 Silo 1 Data Silo 2 Data … Silo N Data Impact • Views are siloed • Duplicate management and data access layer • Need another layer to aggregate Silo 1 systems Silo 2 Systems … Silo N Systems Reporting Reporting Reporting
  • 42. Solution: Unified data services 42 … Benefit • Each application can still save its own data • Data is already aggregated for cross-silo reporting • One cluster and data access layer to manage Silo 1 Systems Silo 2 Systems … Silo N Systems Reporting ……
  • 43. Case Study: Global Broker Dealer Trade Mart for all OTC Trades Distribute reference data globally in real-time for fast local accessing and querying 43 Problem Why MongoDB Results • 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 • Dynamic schema: can save trade for all products in one data service • Easy scaling: can easily keep trades as long as required with high performance • Rich querying: can query on any fields each business requires • Fast time-to-market using the persistence framework • Store any structure of products/trades without changing a schema • One consolidated trade store for auditing and reporting
  • 44. Architectural Pattern – Datamart/Cache
  • 45. Challenge: Response From Data Warehouse or Other System is Slow 45 Cards Loans … Deposits Data Warehouse Issues • Data stored normalized • Reports slow to generate • Data updated daily but user response must be fast Impact • Lost productivity • Dissatisfied users and business Reporting Cards Silo 1 Loans Silo 2 Deposits Silo 3
  • 46. Solution: Optimize Data Structure as a Datamart In-memory or On-disk Cards Loans Deposits 46 … Data Warehouse Solution • Data stored in optimal structure for reports • Optionally in memory Impact • Response times is as fast as possible • Users and business satisfied Fast Reporting Cards Silo 1 Loans Silo 2 Deposits Silo 3 Datamart/Cache …
  • 47. Case Study: Global Bank - Personalized In-memory Datamart Needed fast reporting for finance on global banking transaction data (about 2 petabytes) 47 Problem Why MongoDB Results • Data warehouse was too slow for reporting • No visibility into how long reports took • Could not generate multiple ad hoc reports • Users included regulators so even more demanding • Dynamic schema: store data in optimal structure • Performance: storing report results optimally • In-memory caching of results • Rich querying: can query on any field • Easy scaling: results spread across shards to generate report in parallel • Create a personalized in-memory data mart • Reports configured and notified when results ready • Data all in memory so fast to manipulate • Data spread across shards for ultra-fast reporting
  • 48. Architectural Pattern – Master Data Distribution
  • 49. Challenge: Master data can be hard to change and distribute 49 Golden Copy Batch Batch Batch Batch Batch Batch Batch Batch Common issues • Hard to change schema of master data • Data copied everywhere and gets out of sync Impact • Process breaks from out of sync data • Business doesn’t have data it needs • Many copies creates more management
  • 50. Solution: Persistent dynamic cache replicated globally 50 Real-time Real-time Real-time Real-time Real-time Real-time Real-time Real-time Solution: • Load into primary with any schema • Replicate to and read from secondaries Benefits • Easy & fast change at speed of business • Easy scale out for one stop shop for data • Low TCO
  • 51. Case Study: Global bank Reference Data Distribution Distribute reference data globally in real-time for fast local accessing and querying 51 Problem Why MongoDB Results • 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 • Dynamic schema: easy to load initially & over time • Auto-replication: data distributed in real-time, read locally • Both cache and database: cache always up-to-date • Simple data modeling & analysis: easy changes and understanding • Will save about $40,000,000 in costs and penalties over 5 years • Only charged once for data • Data in sync globally and read locally • Capacity to move to one global shared data service
  • 52. Architectural Pattern – Single Operational View
  • 53. Challenge: Aggregation of disparate data is difficult 53 Cards Loans … Deposits Data Warehouse Batch Cross-Silo applications Issues • Yesterday’s data • Details lost • Inflexible schema • Slow performance Datamar t Datamar t Datamar t Batch Impact • What happened today? • Worse customer satisfaction • Missed opportunities • Lost revenue Batch Batch Reporting Cards Silo 1 Loans Silo 2 Deposits Silo 3
  • 54. Solution: Using dynamic schema and easy scaling 54 Operational Single View Benefits Data Warehouse Real-time or Batch … Customer-facing Applications Regulatory applications • Real-time • Complete details • Agile • Higher customer retention • Increase wallet share • Proactive exception handling Strategic Reporting Operational Reporting Cards Loans … Deposits Customer Accounts Cards Silo 1 Loans Silo 2 Deposits Silo N
  • 55. Case Study Insurance leader generates coveted 360-degree view of customers in 90 days – “The Wall” 55 Problem Why MongoDB Results • No single view of customer • 145 yrs of policy data, 70+ systems, 15+ apps • 2 years, $25M in failing to aggregate in RDBMS • Poor customer experience • Agility – prototype in 5 days; production in 90 days • Dynamic schema: Imperative to combine disparate data • Rich querying: necessary for match data across silos • Hot tech to attract top talent • 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
  • 56. Single [Operational] View of …. Cards Silo 1 Loans Silo 2 56 Operational Reporting Real-time or Batch … Single CSR Application Unified Customer Portal Operational Data Layer Cards Loans … Deposits Deposits Silo N Strategic Reporting … • Millisecond latency • Request/response • Easily scalable • Flexible schema • Low TCO • Rich querying • Globally distributed DW/Analytic Data Layer • Analytical/Offline processing • 10s seconds to hours latency • Also scalable, low TCO, and flexible • Pre-defined slices of data (few indexes) Analytics/Batch processing MongoDB Hadoop Connector …
  • 57. Processing + Data Access Paradigm Processing model Data access model 57 Request/response Map-reduce Batch, ETL, etc. Analytical Jobs Latency important (e.g. user waiting) Milliseconds to seconds Small to large subsets of data Indexes valuable Multiple seconds to hours Processing all or large sets of data Indexes not used
  • 58. Processing + Data Access Paradigm Processing model Data access model 58 Request/response Map-reduce Batch, ETL, etc. Analytical Jobs Latency important (e.g. user waiting) Milliseconds to seconds Small to large subsets of data Indexes valuable Multiple seconds to hours Processing all or large sets of data Indexes not used Typical MongoDB Use Case
  • 59. Processing + Data Access Paradigm Processing model Data access model 59 Request/response Map-reduce Batch, ETL, etc. Analytical Jobs Latency important (e.g. user waiting) Milliseconds to seconds Small to large subsets of data Indexes valuable Multiple seconds to hours Processing all or large sets of data Indexes not used Typical MongoDB Use Case Typical Hadoop Use Case
  • 60. Processing + Data Access Paradigm Processing model Data access model 60 Request/response Map-reduce Batch, ETL, etc. Analytical Jobs Latency important (e.g. user waiting) Milliseconds to seconds Small to large subsets of data Indexes valuable Multiple seconds to hours Processing all or large sets of data Indexes not used Typical MongoDB Use Case Typical Hadoop Use Case
  • 61. Processing + Data Access Paradigm Processing model Data access model 61 Request/response Map-reduce Batch, ETL, etc. Analytical Jobs Latency important (e.g. user waiting) Milliseconds to seconds Small to large subsets of data Indexes valuable Multiple seconds to hours Processing all or large sets of data Indexes not used Typical MongoDB Use Case Typical Hadoop Use Case Data Discovery
  • 63. Example Adoption Path Use of MongoDB 63 One Project MongoDB CoE A Few Projects Certified Widespread Adoption Operationally Supported Time Defined
  • 64. Traditional Data Integrity Enforcement 64 RDBMS • Apps access DB directly • Data Integrity must be in the RDBMS • Schema implemented by a DBA Application 1 Application 2 Application 3
  • 65. Modern Apps (SOA) - Data Access Layer Should Enforce Data Integrity Application 1 65 MongoDB Cluster Application 2 • Data Integrity and validations done in • Implemented in code Data Access Layer … Application N … Data Access Layer REST/API/WS API on TCP/IP
  • 66. Data Governance Benefits • Greater adoption from natural developer 66 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
  • 68. Factors to Consider in Adoption • SDLC and data governance for an application • Enterprise-wide data governance (inter-app) • Enterprise-wide security • 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 68
  • 69. Recommended Center of Excellence 69 Database Engineering & CoE Operational Database CoE Datawarehousing CoE
  • 70. Recommended Center of Excellence 70 Database Engineering & CoE Database Advisory Services Operational Database CoE Datawarehousing CoE
  • 71. Recommended Center of Excellence 71 RDBMS Engineering Database Engineering & CoE Database Advisory Services Operational Database CoE Datawarehousing CoE
  • 72. Recommended Center of Excellence 72 RDBMS Engineering Database Advisory Services Operational Database CoE Database SMEs Database Engineering & CoE Datawarehousing CoE
  • 73. Recommended Center of Excellence 73 Database Advisory Services MongoDB Engineering RDBMS Engineering Operational Database CoE Database SMEs Database Engineering & CoE Datawarehousing CoE
  • 74. Recommended Center of Excellence 74 Database Advisory Services MongoDB Engineering RDBMS Engineering Operational Database CoE MongoDB Incubator (& cluster) Database SMEs Database Engineering & CoE Datawarehousing CoE
  • 75. Recommended Center of Excellence 75 Database Advisory Services MongoDB Engineering RDBMS Engineering Operational Database CoE MongoDB Incubator (& cluster) Database SMEs Database Engineering & CoE RDBMS PaaS Engineering MongoDBaaS Engineering Datawarehousing CoE
  • 76. Recommended Center of Excellence 76 MongoDB Engineering RDBMS Engineering Operational Database CoE MongoDB Incubator (& cluster) Database SMEs Database Engineering & CoE RDBMS PaaS Engineering MongoDBaaS Engineering DW Product Engineering Datawarehousing CoE Hadoop Incubator Clusters Database SMEs DW PaaS Engineering Hadoop PaaS Engineering Database Advisory Services
  • 77. 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, Inc. in to learn how to adopt it more widely 77 when appropriate • Firms using MongoDB benefit from 50% time-to-market, 70% lower TCO, lower operating costs, and making the infeasible possible
  • 79. For More Information 79 Resource Location Resource Location MongoDB Downloads mongodb.com/download Free Online Training university.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 info@mongodb.com

Editor's Notes

  1. Here’s a relational model for an application. It has hundreds of tables. If you are the new developer who just joined the team, congratulations!! Here’s a map of the database, now go figure out how to add your new feature (or fix a bug). Good luck!
  2. Point out what other NoSQL databases have (not rich querying and strong consistency)
  3. Point out what other NoSQL databases have (not rich querying and strong consistency)
  4. One of the main reasons is the data model. Documents are just easier. If my app tracks car collections, I don’t need to know dozens of tables – all the data for an individual and their collection is in one document. (Walk through this example) Dynamic schema
  5. Single view of a customer
  6. Single view of a customer
  7. Compared to distributed cache - $ and fixed schema
  8. Single view of a customer
  9. Can store all accounts in one table Have performance capacity and easy scaling to to do real-time, not just batch
  10. Can store all accounts in one table Have performance capacity and easy scaling to to do real-time, not just batch
  11. Single view of a customer
  12. In terms of reporting, A number of Business Intelligence (BI) vendors have developed connectors to integrate MongoDB as a data source with their suites, alongside traditional relational dbs. This integration provides reporting, visualizations, dash-boarding of MongoDB data