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Enabling Telco to Build and Run Modern Applications

Enabling Telco to Build and Run Modern Applications

See how new databases like MongoDB enable Telco Enterprises to Build and Run Modern Applications.

This presentations was delivered in Tel Aviv in Jan-2015 during a Telco round table organized by Matrix.

Tugdual Grall

January 06, 2015
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  1. Enabling Businesses to Build and Run Modern Applications
    Tugdual Grall
    Technical Evangelist
    [email protected]
    @tgrall

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  2. { “about” : “me” }
    Tugdual “Tug” Grall
    • MongoDB
    – Technical Evangelist
    • Couchbase
    – Technical Evangelist
    • eXo
    – CTO
    • Oracle
    – Developer/Product Manager
    – Mainly Java/SOA
    • Developer in consulting firms
    • Web
    – @tgrall
    – http://blog.grallandco.com
    – tgrall
    • NantesJUG co-founder
    • Pet Project
    – http://www.resultri.com
    [email protected]
    [email protected]

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  3. Agenda
    • The Need for a Next Generation Database
    • MongoDB Overview
    • The Company
    • The Technology
    • The Community
    • MongoDB in Telco

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  4. Agenda
    • The Need for a Next Generation Database
    • MongoDB Overview
    • The Company
    • The Technology
    • The Community
    • MongoDB in Telco

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  5. Your Industry Has Changed
    Upfront subscribe
    Business
    Years Months
    Applications
    PC Mobile
    Customers
    Ads social
    Engagement
    servers Cloud
    Infrastructure

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  6. Your Data Has Changed
    • 90% of the world’s data was
    created in the last two years
    • 80% of enterprise data is
    unstructured
    • Unstructured data growing
    2x faster than structured

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  7. You’re Not Alone
    What are the primary data issues driving you to consider Big Data?*
    *  From  Big  Data  Executive  Summary  of  50+  execs  from  F100,  gov  orgs
    “Of  Gartner's  "3Vs"  of  big  data  (volume,  velocity,  variety),  
    the  variety  of  data  sources  is  seen  by  our  clients  as  both  
    the  greatest  challenge  and  the  greatest  opportunity.”  
                      -­‐  Forrester,  2014
    Diverse, streaming or new data types
    Greater than 100TB
    Less than 100TB

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  8. Development – Methods are Changing
    Requirements
    Analysis
    Design
    Build
    Test
    Acceptance
    Business  Input
    Features

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  9. Development – Agile Development
    Feature Backlog Working Product
    Analysis
    Design
    Build
    Test
    2 - 4 Weeks Cycle

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  10. Software Has Changed
    • High up-front costs
    • High TCO
    • Low up-front costs
    • Low TCO

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  11. The Database is the
    last technology in
    the stack to be
    modernized

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  12. Analytics & BI Integration

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  13. Agenda
    • The Need for a Next Generation Database
    • MongoDB Overview
    • The Company
    • The Technology
    • The Community
    • MongoDB in Telco

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  14. MongoDB, Inc.
    400+ employees 1,000+ customers
    Over $231 million in funding
    13 offices around the world

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  15. MongoDB Partners (600+)
    Software & Services
    Cloud & Channel Hardware

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  16. Agenda
    • The Need for a Next Generation Database
    • MongoDB Overview
    • The Company
    • The Technology
    • The Community
    • MongoDB in Telco

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  17. Data Types  
    Unstructured data  
    Semi-structured data  
    Polymorphic data  
    Agile Development  
    Iterative  
    Short development cycles  
    New workloads
    Relational Database Challenges
    Volume of Data  
    Petabytes of data  
    Trillions of records  
    Millions of queries/sec  
    New Architectures  
    Horizontal scaling  
    Commodity servers  
    Cloud computing

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  18. Operational Database Landscape
    Scalability & Performance
    Depth of Functionality
    key/value stores
    wide column
    RDBMS
    MongoDB

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  19. Changing Mindsets
    Relational
    Centralized
    Document
    Distributed

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  20. Removing Unneeded Complexity
    {
    name: ‘John Doe’,
    id: ‘X2312-BC’,
    cell: ‘+447557505611’
    city: ‘London’,
    location: [45.123,47.232],
    plans: [
    { type : ‘mobile’
    label: ‘30G+’,
    price: 29.99,
    … },
    { type : ‘internet’
    label: ‘Cable’,
    price: 39.99,
    … }
    }
    }

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  21. 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, … }  
    }  
    }

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  22. No SQL But Still Flexible Querying
    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, … }  
    }  
    }
    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?)

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  23. MongoDB - Scalability
    • High Availability
    • Auto Sharding
    • Enterprise Monitoring
    • Grid file storage

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  24. Morphia
    MEAN  Stack
    Java Python Perl
    Ruby
    Support for the most popular languages and frameworks
    Drivers & Ecosystem

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  25. What We Sell
    MongoDB Enterprise Advanced  
    The best way to run MongoDB in your data center  
    MongoDB Management Service (MMS)  
    The easiest way to run MongoDB in the cloud.  
    Production Support  
    In production and under control  
    Development Support  
    Let’s get you running.  
    Consulting  
    We solve problems.  
    Training  
    Get your teams up to speed.

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  26. ‹#›
    DO YOU NEED: YES NO
    Advanced security? ✓
    Disaster Recovery? ✓
    Monitoring for system performance and availability? ✓
    Automated lifecycle management? ✓
    Guaranteed response time? ✓
    Platform certification ✓
    Enterprise Decision Checklist

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  27. How MMS helps you
    Scale  Easily
    Meet  SLAs
    Best  Practices,  
    Automated
    Cut  Management  
    Overhead

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  28. What MMS can do
    Provision
    Upgrade
    Scale
    Continuous  Backup
    Point-­‐in-­‐Time  Recovery
    Performance  Alerts

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  29. Agenda
    • The Need for a Next Generation Database
    • MongoDB Overview
    • The Company
    • The Technology
    • The Community
    • MongoDB in Telco

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  30. THE LARGEST ECOSYSTEM
    9,000,000+

    MongoDB Downloads
    250,000+

    Online Education Registrants
    35,000+

    MongoDB User Group Members
    40,000+

    MongoDB Management Service (MMS) Users
    750+

    Technology and Services Partners
    2,000+

    Customers Across All Industries

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  31. Agenda
    • The Need for a Next Generation Database
    • MongoDB Overview
    • The Company
    • The Technology
    • The Community
    • MongoDB in Telco

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  32. Removing Impedance Mismatches
    Object Relational
    Mapping (ORM)
    Extraction Transformation and
    Loading (ETL)
    Change
    Management
    Features vs
    Complexity
    Platform Agility

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  33. MongoDB Use Cases
    Single View Internet of Things Mobile Real-Time Analytics
    Catalog Personalization Content Management

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  34. Challenge: Achieve Cross Asset View
    Batch
    Batch
    Batch
    Issues  
    •Yesterday’s  data  
    •Details  lost  
    •Inflexible  schema  
    •Slow  performance
    Batch
    Impact  
    •What  happened  today?  
    •Worse  customer  satisfaction
    •Missed  opportunities  
    •Lost  revenue  
    Batch
    Batch
    Reporting
    Customers
    Payments
    Products
    Data  
    Mart
    Data  
    Mart
    Data  
    Mart
    Datawarehouse

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  35. .  .  .  .  
    Solution: Use New Database
    Customers
    Payments
    Products
    .  .  .  .  
    Operational  
    Data  Layer
    Customers  
    Service
    Operational  
    Reporting
    Open  Data  API
    Datawarehouse
    Strategic  
    Reporting
    Benefits  
    • Real-­‐time  
    • Complete  details  
    • Agile  
    • Higher  customer  retention
    • New  products  
    • …

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  36. Single View of Customer
    Insurance leader generates coveted 360-degree view of
    customers in 90 days – “The Wall”
    Problem Why MongoDB Results
    • No single view of
    customer
    • 145 yrs of policy data,
    70+ systems, 15+ apps
    • 2 years, $25M in failing
    to aggregate in RDBMS
    • Poor customer
    experience
    • Agility – prototype in 9
    days;
    • Dynamic schema & rich
    querying – combine
    disparate data into one
    data store
    • Hot tech to attract top
    talent
    • Production in 90 days with 70
    feeders
    • Unified customer view
    available to all channels
    • Increased call center
    productivity
    • Better customer experience,
    reduced churn, more upsell
    opps
    • Dozens more projects on
    same data platform

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  37. Single View of Customer
    Adding Flexibility and Scalability to Bouygues Telecom
    Problem Why MongoDB Results
    • No single view of
    customer
    • Perfomance and
    complexity
    • 2 years delay
    • Poor customer
    experience
    • Agility
    • Scalability
    • Dynamic schema & rich
    querying – combine
    disparate data into one
    data store
    • Easy data integration
    • Developed in 6 months
    • Unified customer view
    available to all channels
    • Increased call center
    productivity
    • New projects
    • Devops

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  38. Product Catalog
    Serves variety of content and user services on
    multiple platforms to 7M web and mobile users
    Problem Why MongoDB Results
    • MySQL reached scale
    ceiling – could not cope
    with performance and
    scalability demands
    • Metadata management
    too challenging with
    relational model
    • Hard to integrate
    external data sources
    • Unrivaled performance
    • Simple scalability and
    high availability
    • Intuitive mapping
    • Eliminated 6B+ rows of
    attributes – instead
    creates single document
    per user / piece of content
    • Supports 115,000+
    queries per second
    • Saved £2M+ over 3 yrs.
    • “Lead time for new
    implementations is cut
    massively”
    • MongoDB is default
    choice for all new
    projects

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  39. Personnalisation Server
    Accelerate Time To Market
    Problem Why MongoDB Results
    • Expensive Oracle Based
    Solution
    • 20 people, 16 months
    • Performance issues
    • 3 iterations
    • Cannot take new
    requirements
    • Mature Technology
    • Dynamic Schema
    • Fault Tolerance
    • Performance
    • 4 Developers
    • 4 months
    • Add new features
    • Faster
    • Smaller
    • Easier

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  40. Mobile / Open Data API
    PIM Database
    • Legacy Application
    • Product Information
    NoSQL
    • REST API
    • Product Data
    • Additional Metadata

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  41. And many more…
    Opening  new  possibles

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  42. Turning your Network into
    Insights for resellers

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  43. Smartsteps

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  44. Ideas?

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  45. Conclusion
    • World has changed
    • Time To Market
    • Cost Reduction
    • New Possibles

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  46. Enabling Businesses to Build and Run Modern Applications
    Tugdual Grall
    Technical Evangelist
    [email protected]
    @tgrall

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