Enabling Telco to Build and Run Modern Applications
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This document discusses how MongoDB can help enable businesses to build and run modern applications. It begins with an overview of Tugdual Grall and his background. It then discusses how industries and data have changed, driving the need for a next generation database. The rest of the document provides an overview of MongoDB, including the company, technology, and community. Examples are given of how MongoDB has helped companies in the telecommunications industry achieve a single customer view, improve product catalogs and personalization, and build mobile and open data APIs.
![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,
… }
}
}](https://image.slidesharecdn.com/mongodb-telco-tgrall-150111045526-conversion-gate02/85/Enabling-Telco-to-Build-and-Run-Modern-Applications-20-320.jpg)
![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, … }
}
}](https://image.slidesharecdn.com/mongodb-telco-tgrall-150111045526-conversion-gate02/85/Enabling-Telco-to-Build-and-Run-Modern-Applications-21-320.jpg)
![No SQLBut 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?)](https://image.slidesharecdn.com/mongodb-telco-tgrall-150111045526-conversion-gate02/85/Enabling-Telco-to-Build-and-Run-Modern-Applications-22-320.jpg)
Enabling Telco to Build and Run Modern Applications
- 1. Enabling Businesses to Build and Run Modern Applications Tugdual Grall Technical Evangelist [email protected] @tgrall
- 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]
- 3. Agenda • The Need for a Next Generation Database • MongoDB Overview • The Company • The Technology • The Community • MongoDB in Telco
- 4. Agenda • The Need for a Next Generation Database • MongoDB Overview • The Company • The Technology • The Community • MongoDB in Telco
- 5. Your Industry Has Changed Upfront subscribe Business Years Months Applications PC Mobile Customers Ads social Engagement servers Cloud Infrastructure
- 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
- 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
- 8. Development – Methods are Changing Requirements Analysis Design Build Test Acceptance Business Input Features
- 9. Development – Agile Development Feature Backlog Working Product Analysis Design Build Test 2 - 4 Weeks Cycle
- 10. Software Has Changed • High up-front costs • High TCO • Low up-front costs • Low TCO
- 11. The Database is the last technology in the stack to be modernized
- 12. Analytics & BI Integration
- 13. Agenda • The Need for a Next Generation Database • MongoDB Overview • The Company • The Technology • The Community • MongoDB in Telco
- 14. MongoDB, Inc. 400+ employees 1,000+ customers Over $231 million in funding13 offices around the world
- 15. MongoDB Partners (600+) Software & Services Cloud & Channel Hardware
- 16. Agenda • The Need for a Next Generation Database • MongoDB Overview • The Company • The Technology • The Community • MongoDB in Telco
- 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
- 18. Operational Database Landscape Scalability&Performance Depth of Functionality key/value stores wide column RDBMS MongoDB
- 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, … } } }
- 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, … } } }
- 22. No SQLBut 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?)
- 23. MongoDB - Scalability • High Availability • Auto Sharding • Enterprise Monitoring • Grid file storage
- 24. Morphia MEAN Stack Java Python PerlRuby Support for the most popular languages and frameworks Drivers & Ecosystem
- 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.
- 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
- 27. How MMS helps you Scale EasilyMeet SLAs Best Practices, Automated Cut Management Overhead
- 28. What MMS can do Provision Upgrade Scale Continuous Backup Point-‐in-‐Time Recovery Performance Alerts
- 29. Agenda • The Need for a Next Generation Database • MongoDB Overview • The Company • The Technology • The Community • MongoDB in Telco
- 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
- 31. Agenda • The Need for a Next Generation Database • MongoDB Overview • The Company • The Technology • The Community • MongoDB in Telco
- 32. Removing Impedance Mismatches Object Relational Mapping (ORM) Extraction Transformation and Loading (ETL) Change Management Features vs Complexity Platform Agility
- 33. MongoDB Use Cases Single View Internet of Things Mobile Real-Time Analytics Catalog Personalization Content Management
- 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
- 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 • …
- 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
- 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
- 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
- 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
- 40. Mobile / Open Data API PIM Database • Legacy Application • Product Information NoSQL • REST API • Product Data • Additional Metadata
- 41. And many more… Opening new possibles
- 42. Turning your Network into Insights for resellers
- 43. Smartsteps
- 44. Ideas?
- 45. Conclusion • World has changed • Time To Market • Cost Reduction • New Possibles
- 46. Enabling Businesses to Build and Run Modern Applications Tugdual Grall Technical Evangelist [email protected] @tgrall