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@nleite

MongoDB: Operational Big Data
Norberto Leite

Senior Solutions Architect, MongoDB
norberto@mongodb.com
Agenda
•  MongoDB Intro
•  Big Data
•  MongoDB Operation Big Data(base)
•  Use Cases
•  QA
Ola!
•  Norberto Leite
•  Solutions Architect
–  wingman
•  Barcelona/Brussels
MongoDB
MongoDB
The leading NoSQL database

General
Purpose

Document
Database

OpenSource
Global Community
5,000,000+

MongoDB Downloads

100,000+

Online Education Registrants

20,000+

MongoDB User Group Members

20,000+

MongoDB Days Attendees

20,000+

MongoDB Management Service (MMS) Users
MongoDB Overview

300+ employees

Offices in New York, Palo Alto, Washington DC,
London, Dublin, Barcelona and Sydney

600+ customers

Over $231 million in funding
MongoDB Overview

Agile

Scalable
MongoDB Vision
To provide the best database for how we build and
run apps today
Build

Run

–  New and complex data
–  Flexible
–  New languages
–  Faster development

–  Big Data scalability
–  Real-time
–  Commodity hardware
–  Cloud
Operational Database Landscape
Document Data Model
Relational

MongoDB
{ !
first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location:
[45.123,47.232],!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!
MongoDB is full featured
Rich Queries

•  Find Paul’s cars
•  Find everybody in London with a car built
between 1970 and 1980

Geospatial

•  Find all of the car owners within 5km of
Trafalgar Sq.

Text Search

•  Find all the cars described as having leather
seats

Aggregation

•  Calculate the average value of Paul’s car
collection

Map Reduce

•  What is the ownership pattern of colors by
geography over time? (is purple trending up
in China?)

MongoDB
{ !
first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location:
[45.123,47.232],!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!
Developers are more productive
Big Data
Best definition so far!
RDBMS Scale = Bigger Computers

“Clients can also opt to run zEC12 without a raised
datacenter floor -- a first for high-end IBM mainframes.”
IBM Press Release 28 Aug, 2012
Vertical Scalability
Mongo DB: Operational Big Data Database
2010 Search Index Size:
100,000,000 GB
New data added per day
100,000+ GB
Databases they could use
0

250,000+ MBP’s == 4.1 miles

This Was a Problem for Google

Source: http://googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html
And for Facebook
2010: 13,000,000 queries per second
And for Facebook
2010: 13,000,000 queries per second

TPC #1 DB: 504,161 tps
TPC Top Results
And for Facebook
2010: 13,000,000 queries per second

TPC #1 DB: 504,161 tps
TPC Top Results

Top 10 combined: 1,370,368 tps
Living in the Post-transactional Future
Order-processing systems largely “done” (RDBMS);
primary focus on better search and recommendations
or adapting prices on the fly (NoSQL)

Vast majority of its engineering is focused on
recommending better movies (NoSQL), not
processing monthly bills (RDBMS)

Easy part is processing the credit card (RDBMS).
Hard part is making it location aware, so it knows
where you are and what you’re buying (NoSQL)
Shift in What We’re Computing
How IT/Data Scientists Define Big Data

Source: Silicon Angle, 2012
MongoDB Operational Big
Data(base)
Consideration – Online vs. Offline
Online

•  Real-time
•  Low-latency
•  High availability

vs.

Offline

•  Long-running
•  High-Latency
•  Availability is lower priority
Consideration – Online vs. Offline
Online

vs.

Offline
MongoDB/NoSQL Is Good for…
360° View of the
Customer

Mobile & Social
Apps

Fraud Detection

User Data
Management

Content
Management &
Delivery

Reference Data

Product Catalogs

Machine to
Machine Apps

Data Hub
MongoDB and Enterprise IT Stack
CRM, ERP, Collaboration, Mobile, BI

Data Management
Online Data

Offline Data

RDBMS
RDBMS

Hadoop

EDW

Infrastructure
OS & Virtualization, Compute, Storage, Network

Security & Auditing

Management & Monitoring

Applications
Horizontal Scalability
Mongo DB: Operational Big Data Database
MongoDB Architecture
Use Cases
Leading Organizations Rely on MongoDB
Fortune 500 & Global 500
•  10 of the Top Financial Services Institutions
•  10 of the Top Electronics Companies
•  10 of the Top Media and Entertainment Companies
•  8 of the Top Retailers
•  6 of the Top Telcos
•  5 of the Top Technology Companies
•  4 of the Top Healthcare Companies
MongoDB Solutions
Big Data

Content Mgmt & Delivery

User Data Management

Mobile & Social

Data Hub
Customer example: Online Travel

Travel

Algorithms

MongoDB
Connector for
Hadoop
• 
• 
• 
• 

Flights, hotels and cars
Real-time offers
User profiles, reviews
User metadata (previous
purchases, clicks, views)

• 
• 
• 
• 

User segmentation
Offer recommendation engine
Ad serving engine
Bundling engine
Machine Learning

Ad-Serving

Algorithms

MongoDB
Connector for
Hadoop
• 
• 
• 
• 
• 

Catalogs and products
User profiles
Clicks
Views
Transactions

•  User segmentation
•  Recommendation engine
•  Prediction engine
Data Hub

Insurance

Churn Analysis

MongoDB
Connector for
Hadoop
• 
• 
• 
• 
• 

Insurance policies
Demographic data
Customer web data
Call center data
Real-time churn detection

•  Customer action analysis
•  Churn prediction
algorithms
@nleite

Obrigado!
Norberto Leite

Senior Solutions Architect, MongoDB
norberto@mongodb.com
QA ?
Mongo DB: Operational Big Data Database

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Mongo DB: Operational Big Data Database

  • 1. @nleite MongoDB: Operational Big Data Norberto Leite Senior Solutions Architect, MongoDB norberto@mongodb.com
  • 2. Agenda •  MongoDB Intro •  Big Data •  MongoDB Operation Big Data(base) •  Use Cases •  QA
  • 3. Ola! •  Norberto Leite •  Solutions Architect –  wingman •  Barcelona/Brussels
  • 5. MongoDB The leading NoSQL database General Purpose Document Database OpenSource
  • 6. Global Community 5,000,000+ MongoDB Downloads 100,000+ Online Education Registrants 20,000+ MongoDB User Group Members 20,000+ MongoDB Days Attendees 20,000+ MongoDB Management Service (MMS) Users
  • 7. MongoDB Overview 300+ employees Offices in New York, Palo Alto, Washington DC, London, Dublin, Barcelona and Sydney 600+ customers Over $231 million in funding
  • 9. MongoDB Vision To provide the best database for how we build and run apps today Build Run –  New and complex data –  Flexible –  New languages –  Faster development –  Big Data scalability –  Real-time –  Commodity hardware –  Cloud
  • 11. Document Data Model Relational MongoDB { ! first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: [45.123,47.232],! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }!
  • 12. MongoDB is full featured Rich Queries •  Find Paul’s cars •  Find everybody in London with a car built between 1970 and 1980 Geospatial •  Find all of the car owners within 5km of Trafalgar Sq. Text Search •  Find all the cars described as having leather seats Aggregation •  Calculate the average value of Paul’s car collection Map Reduce •  What is the ownership pattern of colors by geography over time? (is purple trending up in China?) MongoDB { ! first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: [45.123,47.232],! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }!
  • 13. Developers are more productive
  • 16. RDBMS Scale = Bigger Computers “Clients can also opt to run zEC12 without a raised datacenter floor -- a first for high-end IBM mainframes.” IBM Press Release 28 Aug, 2012
  • 19. 2010 Search Index Size: 100,000,000 GB New data added per day 100,000+ GB Databases they could use 0 250,000+ MBP’s == 4.1 miles This Was a Problem for Google Source: http://googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html
  • 20. And for Facebook 2010: 13,000,000 queries per second
  • 21. And for Facebook 2010: 13,000,000 queries per second TPC #1 DB: 504,161 tps TPC Top Results
  • 22. And for Facebook 2010: 13,000,000 queries per second TPC #1 DB: 504,161 tps TPC Top Results Top 10 combined: 1,370,368 tps
  • 23. Living in the Post-transactional Future Order-processing systems largely “done” (RDBMS); primary focus on better search and recommendations or adapting prices on the fly (NoSQL) Vast majority of its engineering is focused on recommending better movies (NoSQL), not processing monthly bills (RDBMS) Easy part is processing the credit card (RDBMS). Hard part is making it location aware, so it knows where you are and what you’re buying (NoSQL)
  • 24. Shift in What We’re Computing
  • 25. How IT/Data Scientists Define Big Data Source: Silicon Angle, 2012
  • 27. Consideration – Online vs. Offline Online •  Real-time •  Low-latency •  High availability vs. Offline •  Long-running •  High-Latency •  Availability is lower priority
  • 28. Consideration – Online vs. Offline Online vs. Offline
  • 29. MongoDB/NoSQL Is Good for… 360° View of the Customer Mobile & Social Apps Fraud Detection User Data Management Content Management & Delivery Reference Data Product Catalogs Machine to Machine Apps Data Hub
  • 30. MongoDB and Enterprise IT Stack CRM, ERP, Collaboration, Mobile, BI Data Management Online Data Offline Data RDBMS RDBMS Hadoop EDW Infrastructure OS & Virtualization, Compute, Storage, Network Security & Auditing Management & Monitoring Applications
  • 36. Fortune 500 & Global 500 •  10 of the Top Financial Services Institutions •  10 of the Top Electronics Companies •  10 of the Top Media and Entertainment Companies •  8 of the Top Retailers •  6 of the Top Telcos •  5 of the Top Technology Companies •  4 of the Top Healthcare Companies
  • 37. MongoDB Solutions Big Data Content Mgmt & Delivery User Data Management Mobile & Social Data Hub
  • 38. Customer example: Online Travel Travel Algorithms MongoDB Connector for Hadoop •  •  •  •  Flights, hotels and cars Real-time offers User profiles, reviews User metadata (previous purchases, clicks, views) •  •  •  •  User segmentation Offer recommendation engine Ad serving engine Bundling engine
  • 39. Machine Learning Ad-Serving Algorithms MongoDB Connector for Hadoop •  •  •  •  •  Catalogs and products User profiles Clicks Views Transactions •  User segmentation •  Recommendation engine •  Prediction engine
  • 40. Data Hub Insurance Churn Analysis MongoDB Connector for Hadoop •  •  •  •  •  Insurance policies Demographic data Customer web data Call center data Real-time churn detection •  Customer action analysis •  Churn prediction algorithms
  • 41. @nleite Obrigado! Norberto Leite Senior Solutions Architect, MongoDB norberto@mongodb.com
  • 42. QA ?