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

{ “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] 

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

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

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

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 

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 

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

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

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

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

Analytics & BI Integration 

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

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

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

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

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 

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

Changing Mindsets Relational Centralized Document Distributed 

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, … } } } 

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, … }   }   } 

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?) 

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

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

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. 

‹#› 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 

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

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

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

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 

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

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

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

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 

.  .  .  .   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   • … 

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 

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 

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 

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 

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

And many more… Opening  new  possibles 

Turning your Network into Insights for resellers 

Smartsteps 

Ideas? 

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

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