Webinar: MongoDB and Hadoop - Essential Tools for Your Big Data Playbook
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Big Data Playbook
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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
Transcript of Webinar: MongoDB and Hadoop - Essential Tools for Your Big Data Playbook
Big Data Playbook
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big data \big 'dat-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
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C-Level View of Big Data – Not “Big”
from Big Data Executive Summary
64% - Ingest diverse, new data in real-time
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Consideration – Online vs. Offline
• Long-running• High-Latency• Availability is lower
priority
• Real-time• Low-latency• High availability
Online Offlinevs.
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Consideration – Online vs. Offline
Online Offlinevs.
Use Cases and Production Systems
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Hadoop is good for…
Risk Modeling Churn Analysis Recommendation Engine
Ad Targeting Transaction Analysis
Trade Surveillance
Network Failure
PredictionSearch Quality Data Lake
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MongoDB is good for…
360 Degree View of the Customer
Mobile & Social Apps
Fraud Detection
User Data Management
Content Management
& DeliveryReference
Data
Product Catalogs
Machine to Machine Apps Data Hub
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MongoDB and Hadoop are Complementary
• “Data Lake”• In-depth analytics
• Real-time systems• Light-weight analytical
workloads
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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
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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
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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
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Customer example – Criteo
Ad-Serving
• Catalogs and products
• User profiles• Clicks• Views• Transactions
• User segmentation• Recommendation
engine• Prediction engine
Algorithms
MongoDB Connector for
Hadoop
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• Webinars – Thriving with Big Data– What’s New with MongoDB Hadoop Integra
tion• 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
