Webinar: MongoDB and Hadoop - Essential Tools for Your 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

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  • OrSo not everyone would agree with the term offline big data
  • OrSo not everyone would agree with the term offline big data
  • Webinar: MongoDB and Hadoop - Essential Tools for Your Big Data Playbook

    1. 1. Big Data Playbook
    2. 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. 3 C-Level View of Big Data – Not “Big” from Big Data Executive Summary 64% - Ingest diverse, new data in real-time
    4. 4. 4 Consideration – Online vs. Offline • Long-running • High-Latency • Availability is lower priority • Real-time • Low-latency • High availability Online Offlinevs.
    5. 5. 5 Consideration – Online vs. Offline Online Offlinevs.
    6. 6. Use Cases and Production Systems
    7. 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. 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. 9 MongoDB and Hadoop are Complementary • “Data Lake” • In-depth analytics • Real-time systems • Light-weight analytical workloads
    10. 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. 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. 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. 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. 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

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