By every measure the two biggest technologies in Big Data are MongoDB and Hadoop. In this webinar, we define the Big Data landscape, including the two types of Big Data — Online and Offline — and explain how MongoDB and Hadoop support each of these use cases. We then explore how these two technologies complement each other to help companies derive business value — like improved customer experience and lower TCO — in real-time and retrospective contexts. We will highlight production systems that leverage MongoDB and Hadoop together. In this webinar you will learn: * How MongoDB and Hadoop work together * What capabilities are provided by the new *MongoDB+Hadoop Connector * How leading companies make the most of these two technologies

1. Big Data Playbook

2. 2
big data big 'dāt-ə noun
referring to technologies and initiatives that involve
data that is too diverse, fast-changing or massive
for conventional technologies, skills and
infrastructure to address efficiently.
Big Data Defined

3. 3
C-Level View of Big Data – Not “Big”
from Big Data Executive Summary
64% - Ingest
diverse, new data in
real-time

4. 4
Consideration – Online vs. Offline
• Long-running
• High-Latency
• Availability is lower priority
• Real-time
• Low-latency
• High availability
Online Offlinevs.

5. 5
Consideration – Online vs. Offline
Online Offlinevs.

6. Use Cases and
Production Systems

7. 7
Hadoop is good for…
Risk Modeling Churn Analysis
Recommendation
Engine
Ad Targeting
Transaction
Analysis
Trade
Surveillance
Network Failure
Prediction
Search Quality Data Lake

8. 8
MongoDB is good for…
360 Degree 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

9. 9
MongoDB and Hadoop are
Complementary
• “Data Lake”
• In-depth analytics
• Real-time systems
• Light-weight analytical
workloads

10. 10
Use MongoDB+Hadoop Together
E-Commerce
• Products & Inventory
• Real-time recommendations
• Customer profile
• Session management
• Customer clickstream
• Fraud detection
• Transaction history
• Clickstream history
• Recommendation model
• Fraud modeling
Analysis
MongoDB
Connector for
Hadoop

11. 11
Customer example – Global Travel
Firm
Travel
• 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
Algorithms
MongoDB
Connector for
Hadoop

12. 12
Customer example – MetLife
Insurance
• Insurance policies
• Demographic data
• Customer web data
• Call center data
• Real-time churn detection
• Customer action analysis
• Churn prediction
algorithms
Churn Analysis
MongoDB
Connector for
Hadoop

13. 13
Customer example – Criteo
Ad-Serving
• Catalogs and products
• User profiles
• Clicks
• Views
• Transactions
• User segmentation
• Recommendation engine
• Prediction engine
Algorithms
MongoDB
Connector for
Hadoop

14. 14
• Webinars
– Thriving with Big Data
– What’s New with MongoDB Hadoop Integration
• White Paper – Big Data: Examples and Guidelines
for the Enterprise Decision Maker
• Blog –A Tour of What’s New In MongoDB Connector
for Hadoop
• Documentation –
MongoDB Connector for Hadoop
Resources