webinar: mongodb and hadoop - essential tools for your big data playbook
DESCRIPTION
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 technologiesTRANSCRIPT
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