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MongoDB: Agile Combustion Engine
The document discusses MongoDB as a general-purpose document database, emphasizing its open-source nature and robust features such as flexible data models and scalability, which enable faster development and deployment. It highlights various use cases and success stories where organizations improved efficiency and reduced costs by leveraging MongoDB to address challenges in data handling and analytics. Additionally, it outlines the importance of combining agile methodologies with supportive infrastructure, technical tools, and business needs for successful project outcomes.
MongoDB: Agile Combustion Engine
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- 4. 4 Agenda MongoDB Introduction Agile Development EnablingFactors Outcomes
- 5. 5 Ola! Norberto Leite Technical Evangelist Madrid,Spain http://www.mongodb.com/norberto @nleite norberto@mongodb.com
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- 7. 7 MongoDB GENERAL PURPOSE DOCUMENTDATABASE OPEN-SOURCE
- 8. Have you everthought of building your geospatial system, out of your code!?! #GOODLUCK!
- 9. MongoDB is FullyFeatured
- 10. Over 10,000,000 downloads 300,000 Students for MongoDB University 35,000 attendeesto MongoDB events annually Over 1,000 Partners Over 2,000! Paying Customers
- 11. 11 Reduce Risk forMission-Critical Deployments Lower TCO MongoDB Business Value Leverage Data & Tech. to Maximize Competitive Advantage Faster Time to Value
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- 15. 15 Agile Development –Driving Forces • Development Practices • Natural Working Conditions • Process • Results
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- 20. 20 Product Backlog Sprint Backlog DemoDays Standup Meetings …
- 21. 21 Our support teamon their daily standup
- 22. 22 Our consultants at customeragile board
- 23. 23 Our own ContinuousIntegration Tool
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- 25. 25 Enabling Factors Business TechnicalOrganiza0onal Infrastructure
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- 30. 30 Business Needs h"ps://themonoorganisa0on.files.wordpress.com/2013/08/picture-data-vs-voice.jpg • Weneed flexible tools • Business are faster paced • Lots of transformational and disruption • Constant iteration
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- 37. 37 Technical - MongoDB h"p://socialscotland.com/wp-content/uploads/2014/07/US-Robo0cs-56k-modem.jpg • Flexible Database – You build faster • Drivers for all main programing languages • Integrates well with other tools • Build for the modern Infrastructure – Scalability and Cloud
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- 40. 40 Personalization Built personalization enginein 25% the time with 50% the team Problem Why MongoDB Results Problem Solution Results Needed personalization server that acts as the master storage for customer data. Originally built on Oracle (over 14 months) but it performed below expectations, did not scale, and cost too much New requirements made Oracle unusable – 40% more data, must reload entire data warehouse (22M customers) daily in small window – could not be met with Oracle Implemented on MongoDB, using flexible data model to easily bring in data from disparate customer data source systems Expressive query language made it possible to access customer records using any field Consulting and support significantly reduced upfront development and deployment costs New version of personalization engine was built on MongoDB in 25% the time with 50% the team Led to performance boosts of more than a magnitude Storage requirements decreased by 66%, lowering infrastructure costs
- 41. 41 Mobile Inbox Mail appstartup scales to 1M users in weeks Problem Why MongoDB Results Problem Solution Results Startup reimagines mobile inbox Massive demand for its new mobile email app Needed ability to iterate quickly based on early user feedback, stored variety of data and metadata Built app on MongoDB, leveraging flexible data model to store variety of inbox data and iterate quickly Auto-sharding allowed the team to add capacity in line with business growth Secondary indexes allow for fast access to data Scaled business from 0 to over 1M users within weeks, over 100M messages/day Delivered 3 major releases in 3 months Acquired by Dropbox
- 42. 42 Case Study Stores billionsof posts in myriad formats with MongoDB Problem Why MongoDB Results Problem Why MongoDB Results 1.5M posts per day, different structures Inflexible MySQL, lengthy delays for making changes Data piling up in production database Poor performance Flexible document-based model Horizontal scalability built in Easy to use Interface in familiar language Initial deployment held over 5B documents and 10TB of data Automated failover provides high availability Schema changes are quick and easy
- 43. 43 Single View ofCustomer Insurance leader generates coveted single view of customers in 90 days – “The Wall” Problem Why MongoDB Results Problem Solution Results No single view of customer, leading to poor customer experience and churn 145 years of policy data, 70+ systems, 24 800 numbers, 15+ front-end apps that are not integrated Spent 2 years, $25M trying build single view with Oracle – failed Built “The Wall,” pulling in disparate data and serving single view to customer service reps in real time Flexible data model to aggregate disparate data into single data store Expressive query language and secondary indexes to serve any field in real time Prototyped in 2 weeks Deployed to production in 90 days Decreased churn and improved ability to upsell/cross-sell
- 44. 44 Real-Time Geospatial Platform forInnovation Using MongoDB to create a smarter and safer city Problem Why MongoDB Results Problem Solution Results Siloed data across city departments made it difficult for the City of Chicago to intelligently analyze situations deliver services to its citizens City needed a system that could not only handle 7 million pieces of data / day from different departments, but also run analytics across it to deliver insight Used MongoDB’s flexible schema to build the WindyGrid, a unified view of the city’s operations that brings together disparate datasets from 30 departments Leveraged MongoDB’s rich analytics features (aggregation framework, geospatial indexes, etc) to create maps that deliver real-time insight Horizonal scalability with automatic sharding across commodity servers ensures the city can continue to cost effectively deliver real-time results A single view of the city’s operations on a map of Chicago is now available to all managers to help them better analyze and respond to incidents in real-time New predictive analytics system is planned that will help prevent crimes before they happen 450 sets of data have been published to the public, sparking even further innovation, e.g, an app that alerts citizens when street sweepers are coming
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- 46. 46 Agile Combustion Engine • Agile methodologies do not guarantee project success • It's a combined effort of – Infrastructure – Technical tools – Organizational – Business needs • MongoDB helps on all these fronts
- 47. How a DatabaseCan Make Your Organization Faster, Better, Leaner https://www.mongodb.com/collateral/how-database-can- make-your-organization-faster-better-leaner
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