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MongoDB company and case studies - john hong

MongoDB company and case studies
MongoDB Forum Seoul 2018 session 4
MongoDB john hong

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MongoDB company and case studies - john hong

  1. 1. Introduction to MongoDB and Customer Case Studies John Hong, Senior Director Solutions Architecture @ MongoDB
  2. 2. For more than a decade, organizations have been pursuing the promise of Digital Transformation
  3. 3. Mobile Expanded Use Cases New IT Models Smart Objects Legacy Modernization
  4. 4. Everycompany is becoming a software company
  5. 5. Survey Results: Every business is a software business Source: Stripe, “The Developer Coefficient”
  6. 6. It’s now about being a Great software business Source: Stripe, “The Developer Coefficient”
  7. 7. 7 Maximizing developer velocity is key Access to developers is a bigger constraint on growth than access to capital, according to survey of thousands of C-level executives. The Developer Coefficient, Stripe Developers spend 42% of their work week on maintenance issues and fixing bad code.
  8. 8. 88% CIOs believe they have yet to benefit from their digital strategy Source: Harvey Nash / KPMG CIO Survey 2017
  9. 9. The reason? DATA.S I L O E D | C O M P L E X | T R A P P E D
  10. 10. Why customers choose MongoDB Best way to work with data Intelligently put data where you need it Freedom to run anywhere
  11. 11. Mobile Commerce Mobile Banking Real-Time Travel Search Predictive Messaging Customer Marketing & Personalization Background Checks as a Service Shopping Cart Mobile App for Patient Data Connected Car Mass Spectrometer Instrumentation Ticket E-Commerce Online Publishing Content Mgt. & Collaboration Design Collaboration Swap Equities Management Trade Data Intelligence System Streaming Financial Data Accounting Suite Property Appraisal Online Booking Single View of Patient Genome Sequencing Online Banking Smart Grid Cryptocurrency Trading Order Capture Single View of City Logistics Modernization Social Security Benefits Program Product Catalog Gaming Platform Video Streaming Hourly Work Platform Log Metadata Store E-Commerce Platform Social Media Management
  12. 12. Massive partner ecosystem
  13. 13. How MongoDB Can Help
  14. 14. Easy Fast Flexible Versatile Best way to work with data
  15. 15. The Relational (tabular) data model
  16. 16. The Relational (tabular) data model
  17. 17. Tabular (Relational) Data Model Related data split across multiple records and tables Document Data Model Related data contained in a single, rich document { "_id" : ObjectId("5ad88534e3632e1a35a58d00"), "name" : { "first" : "John", "last" : "Doe" }, "address" : [ { "location" : "work", "address" : { "street" : "16 Hatfields", "city" : "London", "postal_code" : "SE1 8DJ"}, "geo" : { "type" : "Point", "coord" : [ 51.5065752,-0.109081]}}, + {...} ], "phone" : [ { "location" : "work", "number" : "+44-1234567890"}, + {...} ], "dob" : ISODate("1977-04-01T05:00:00Z"), "retirement_fund" : NumberDecimal("1292815.75") } Contrasting data models
  18. 18. • Naturally maps to objects in code • Represent data of any structure • Strongly typed for ease of processing – Over 20 binary encoded JSON data types • Access by idiomatic drivers in all major programming language { "_id" : ObjectId("5ad88534e3632e1a35a58d00"), "name" : { "first" : "John", "last" : "Doe" }, "address" : [ { "location" : "work", "address" : { "street" : "16 Hatfields", "city" : "London", "postal_code" : "SE1 8DJ"}, "geo" : { "type" : "Point", "coord" : [ 51.5065752,-0.109081]}}, + {...} ], "phone" : [ { "location" : "work", "number" : "+44-1234567890"}, + {...} ], "dob" : ISODate("1977-04-01T05:00:00Z"), "retirement_fund" : NumberDecimal("1292815.75") } The beauty of the Document model
  19. 19. Intelligently put data where you want it Availability Scalability Workload Isolation Locality
  20. 20. Freedom to run anywhere Runs the same everywhere Coverage in any geo Leverage multi-cloud strategy Avoid lock-in
  21. 21. The evolution of MongoDB 3.0 3.2 Document Validation $lookup Fast Failover Simpler Scalability Aggregation ++ Encryption At Rest In-Memory Storage Engine BI Connector MongoDB Compass APM Integration Profiler Visualization Auto Index Builds Backups to File System Doc-Level Concurrency Compression Storage Engine API ≤50 replicas Auditing ++ Ops Manager Linearizable reads Intra-cluster compression Views Log Redaction Graph Processing Decimal Collations Faceted Navigation Zones ++ Aggregation ++ Auto-balancing ++ ARM, Power, zSeries BI & Spark Connectors ++ Compass ++ Hardware Monitoring Server Pool LDAP Authorization Encrypted Backups Cloud Foundry Integration 3.4 3.6 Change Streams Retryable Writes Expressive Array Updates Query Expressivity Causal Consistency Consistent Sharded Sec. Reads Compass Community Ops Manager ++ Query Advisor Schema Validation End to End Compression IP Whitelisting Default Bind to Localhost Sessions WiredTiger 1m+ Collections MongoDB BI Connector ++ Expressive $lookUp R Driver Atlas Cross Region Replication Atlas Auto Storage Scaling 4.0 Multi-Document ACID Transactions Atlas Global Clusters Atlas HIPAA Atlas LDAP Atlas Audit Atlas Encrypted Storage Engine Atlas AWS Backup Snapshots Atlas Full CRUD Agg Pipeline Type Conversions 40% Faster Shard Migrations Snapshot Reads Non-Blocking Secondary Reads SHA-2 TLS 1.1+ Compass Agg Pipeline Builder Compass Export to Code Charts Beta Free Monitoring Cloud Service Ops Manager K8s & OpenShift MongoDB Stitch GA MongoDB Mobile Beta
  22. 22. MongoDB’s Database as a Service
  23. 23. Atlas unlocks agility and reduces cost Self-service and elastic Global and highly available Secure by default Comprehensive monitoring Managed backup Cloud agnostic
  24. 24. Customer Case Studies
  25. 25. Single View of Customer Insurance leader generates coveted single view of customers in 90 days – “The Wall” Problem Why MongoDB ResultsProblem Solution Results No single view of customer, leading to poor customer experience and churn 145 years of policy data, 70+ systems, 24 different 1-800 numbers, 15+ front- end apps that are not integrated Spent 2 years, $25M trying build single view with DB2 – 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
  26. 26. Real-Time Geospatial Platform for Innovation Using MongoDB to create a smarter and safer city Problem Why MongoDB ResultsProblem Solution Results Siloed data across city departments made it difficult for the City of Chicago to intelligently analyze situations and deliver services to its citizens City needed a system that could not only handle 7 million pieces of data / day from 30+ departments, but also run analytics across it to deliver insight Used MongoDB’s flexible data model 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 data sets have been published to the public, sparking even further innovation, e.g., an app that alerts citizens when street sweepers are coming
  27. 27. Agricultural IoT Farmsight connected tractors let farmers use data to increase output by 8% in initial trials Problem Why MongoDB ResultsProblem Solution Results Agricultural output must double by 2050 to meet population growth – Deere wanted to help farmers use data to get more out of each acre IBM DB2 rigid schema made it hard to store variety of data from tractors, adapt to new business requirements DB2 could not scale as data collection grew faster than expected Built Farmsight on MongoDB, using flexible data model to ingest variety of sensor data and iterate on app quickly Secondary indexes (incl. geospatial) allow for fast access to data; aggregation framework for in-place analysis Auto-sharding allowed Deere to add capacity in line with business growth AgDecision Support – data collected in MongoDB allows grower to increase output by 8% in initial trials Prototyping accelerated 6x, from 3 months --> 2 weeks New apps drive revenue, increase customer sat. and differentiate in stale industrial market
  28. 28. Coinbase with over 20M users, $150B assets traded and $20B assets stored, partners with MongoDB to scale reliably in the cloud to meet the explosion of cryptocurrency demand Coinbase’s mission is to create an open financial system for the world. MongoDB’s technology is enabling us to scale globally and we’re looking forward to partnering with the company along our journey to become the most compliant, reliable and trusted crypto-trading platform in the world. –Niall O’Higgins, Engineering Manager, Coinbase “10x platform resilience improvement 80x API RPM capacity improvement in ~6 months 12x improvement in speed of scale 70%+ faster app development release time
  29. 29. Ease and flexibility of the document model led to massive early adoption. We are the world’s most popular modern database. Uniquely positioned as the only company who can credibly offer a modern “general purpose” database. First database company to go public in over 20 years. We’ve raised over a half a billion dollars to invest in our business. Why bet on us?

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  • SangHyunLee45

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    Nov. 7, 2018

MongoDB company and case studies MongoDB Forum Seoul 2018 session 4 MongoDB john hong

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