MongoDB in the Big Data Landscape
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- MongoDB is a document database management system that is recognized as a leader by Gartner. It has over 520 employees, 2500+ customers, and offices globally. - MongoDB ranked 4th in database mindshare according to DB-Engines. It has seen 172% growth in the last 20 months. - Several companies such as a quantitative investment manager, an insurance company, a telecommunications company, and an ecommerce company migrated their systems to MongoDB and saw benefits like 100x faster data retrieval, 50% lower costs, and being able to build applications faster.
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MongoDB in the Big Data Landscape
- 2. MongoDB Inc, 520+ employees 2500+ customers Offices in NY, London & Palo Alto and across EMEA, and APAC World Class Advisory
- 4. Gartner, Inc. recognized MongoDB as a Leader in the 2015 Magic Quadrant for Operational Database Management Systems.* *Gartner, Inc., Magic Quadrant for Operational Database Management Systems by Donald Feinberg, Merv Adrian, Nick Heudecker, Adam M. Ronthal, and Terilyn Palanca, October 12, 2015. *Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from MongoDB, Inc.
- 5. MongoDB ranked 4 in Database Mindshare http://db-engines.com/en/ranking RANK% DBMS% %%%%MODEL% SCORE% GROWTH%(20%MO)% 1.# Oracle# Rela+onal#DBMS# 1,442# 55%# 2.# MySQL# Rela+onal#DBMS# 1,294# 2%# 3.# Microso?#SQL#Server# Rela+onal#DBMS# 1,131# 510%# 4.% MongoDB% Document%Store% 277% 172%% 5.# PostgreSQL# Rela+onal#DBMS# 273# 40%# 6.# DB2# Rela+onal#DBMS# 201# 11%# 7.# Microso?#Access# Rela+onal#DBMS# 146# 526%# 8.# Cassandra# Wide#Column# 107# 87%# 9.# SQLite# Rela+onal#DBMS# 105# 19%#
- 6. Information Management Has Changed UPFRONT SUBSCRIBE Business YEARS MONTHS Applications PC MOBILE Customers ADS SOCIAL Engagement SERVERS CLOUD Infrastructure
- 7. 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
- 8. Big Data Driving Factors “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.” 2014 * From Big Data Executive Summary of 50+ execs from F100, gov orgs What are the primary data issues driving you to consider Big Data?* Data Variety (68%) Data Volume (15%) Other Data (17%) Diverse, streaming or new data types Greater than 100TB Less than 100TB
- 10. Relational Expressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 11. The World Has Changed Data Risk Time Cost
- 12. NoSQL Scalability & Performance Always On, Global Deployments FlexibilityExpressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 13. Nexus Architecture Scalability & Performance Always On, Global Deployments FlexibilityExpressive Query Language & Secondary Indexes Strong Consistency Enterprise Management & Integrations
- 14. Proof Points
- 15. Single Platform for Financial Data Quantitative investment manager with over $11B in assets under management invests heavily in new database Problem% Why%MongoDB% Results%Problem Solution Results AHL needed new technologies to be more agile and gain competitive advantages in the systematic trading space Proprietary systems in financial services tech, as well as relational databases, were too expensive and/or rigid Built single platform for all financial data on MongoDB Flexible data model and scalability were core to ability to put all data in single platform Expressive query language, secondary indexes and strong consistency were core to ability to migrate core use cases to new platform 100x faster to retrieve data Tick Data: Quickly scaled to 250M ticks per second, a 25x improvement in tick throughput Cut disk storage 60%, and realized 40% cost savings by using commodity SSDs
- 16. Risk Mgt. for the Connected Home Delivering on customer protection mission with MongoDB Problem% Why%MongoDB% Results%Problem Solution Results Need to create innovative apps that support corporate mission to protect customers, improve brand perception, and reduce churn Poor view of customers’ behavior at home, which can be used to provide more compelling pricing and better products Unable to offer a new experience around domestic connected objects Build a scalable mobile app allowing customers to integrate domestic connected objects to detect and prevent risk Leverages flexible data model to support specific APIs with third parties: Philips Hue light bulbs, NEST smoke detection, MyFox cameras, and more in fast evolving market Prototype built in 2 months; deployed in production in 4 months Able to prevent domestic risks and assist customers in case of incidents. Proactive alerting to customers minimizes their risk, creates customer loyalty Great user experience improves perception of AXA brand
- 17. Reference Data Management Major app migrated to MongoDB, saving $40M over 5 years Problem% Why%MongoDB% Results%Problem Solution Results Globally distributed app reference data management app did not meet SLAs required in delivering data to traders, resulting in SEC fines and damages to the reputation of firm Complex infrastructure with many components (i.e., ETL, caching, proprietary storage) was expensive and difficult to maintain Replatformed on MongoDB for a simplified infrastructure Native replication made it easy to replicate to data centers on multiple continents, bringing it closer to stakeholders and reducing the effects of geographic latency $40M in savings over 5 years with simplified infrastructure and the use of commodity servers Application in compliance with strict SLAs
- 18. Content Management Migrates from MySQL to MongoDB on AWS, saving £2M and dramatically cutting project lead time Problem% Why%MongoDB% Results%Problem Solution Results Orange Digital web properties have 4.5M users on web and 2.3M users on mobile, across www.orange.co.uk, Orange World, and the Orange Business site, and other digital assets. MySQL reached scale ceiling Metadata management too challenging with relational model – targeting handsets, users, types of data, video, feeds, text and more Replatformed on MongoDB and migrated to AWS Flexible data model makes it substantially easier and more efficient to manage variety of metadata Sharding enables scalability and unrivaled performance 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 Eliminated 6B+ rows of attributes – instead creates single document per user / piece of content
- 19. Problem% Why%MongoDB% Results%Problem Solution Results Proprietary solution with rigid data model slowed rate of new service introductions, impacting competitiveness Unable to scale as subscriber and service portfolio expanded High TCO incurred from proprietary hardware and software Built new customer data management platform on MongoDB Flexible data model enables dynamic schema modification to support new service introductions Automatic sharding to scale database as the business grows MongoDB platform scales to serve 12M customers, with 50% reduced cost per subscriber Streamlined and simplified systems allowing faster innovation and higher agility Migration to MongoDB completed in just 6 months Customer Data Mgt. Telco leader unifies customer experience, driving 50% lower cost and reduced churn
- 20. Personalization Built personalization engine in 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