Overcoming Today's Data Challenges with MongoDB
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The document outlines an agenda for an event on overcoming data challenges with MongoDB. The event will feature speakers from MongoDB and Bosch discussing how the world has changed since relational databases were invented, how to radically transform IT environments with MongoDB, MongoDB and blockchain, and MongoDB for multiple use cases. The agenda includes presentations on these topics as well as a Q&A session and conclusion.
Overcoming Today's Data Challenges with MongoDB
- 1. www.mongodb.com MongoDB Zürich November 29th 2017 Overcoming Today's Data Challenges with MongoDB With the participation of: &
- 2. • How the World has Changed Since Relational Databases were Invented • How to radically transform your IT environments with MongoDB • MongoDB with Blockchain • MongoDB, the Database of choice for multiple Use Cases • Q&A and Conclusion Agenda
- 3. Your Speakers today: Rizan Flenner Regional Vice President Central Europe, MongoDB Roman Gruhn Director of Information Strategy (EMEA), MongoDB Dirk Slama Vice President Bosch Software Innovations Benjamin Lorenz Senior Solution Architect, MongoDB Kamal Ved Director of Business Development BigChainDB
- 4. • How the World has Changed Since Relational Databases were Invented • How to radically transform your IT environments with MongoDB • MongoDB with Blockchain • MongoDB, the Database of choice for multiple Use Cases • Q&A and Conclusion Agenda
- 5. Who are we today…
- 6. 800+ employees MongoDB Inc. MongoDB DACH Frankfurt, München, Zürich, Wien 4,300+ customers 29 offices worldwide NASDAQ “MDB”Adrian Oesman [email protected]
- 7. Customers MongoDB Office Support MongoDB User Groups 30+ Million Downloads
- 8. Rizan Flenner Regional Vice President, Central Europe [email protected] How the World has Changed Since Relational Databases were Invented
- 9. Digital Platforms Have Changed The platforms your end users and customers use to engage with your applications and services have fundamentally changed at an unprecedented speed over the past 5 years. UPFRONT SUBSCRIBE Business YEARS / MONTHS WEEKS / DAYS Applications PC MOBILE / BYOD Customers ADS SOCIAL Engagement SERVERS CLOUD Infrastructure
- 10. People Thinking of: “ Competitive Advantage … they think about technology ….. … which includes software and data Every company today is becoming a software business Companies Value Propositions today are either Enabled,Defined or Delivered Thru software solutions“ Dev Ittycheria
- 11. How can Software drive success? Source: US Bureau of Economic Analysis Manufacturing Retail Transportation Publishing, Broadcast Education, Healthcare, Social Assistance Finance, Insurance, Real Estate Arts, Entertainment, Food $1.6T $1.1T $1.5T $6.2T $5.3T $2.4T $1.2T
- 12. Digital matters to your customers… $4tn+ in eCommerce sales by 2020 75bn connected devices by 2025 61% see AI as an “urgent” strategy in 2017 $108bn AR/VR market size by 2021 59% global smartphone penetration by 2022 $6tn+ in cyber-crime damage by 2021
- 13. Speed Matters…..
- 14. …..But Digital is Hard Source: Forrester Digital Transformation Infographic: https://go.forrester.com/blogs/16-05-09-digital_transformation_2016_infographic/
- 15. MongoDB fuels giant ideas in DACH
- 16. • How the World has Changed Since Relational Databases were Invented • How to radically transform your IT environments with MongoDB • Bridging the gap – How BOSCH is addressing enterprise/cloud integration • MongoDB with Blockchain • MongoDB, the Database of choice for multiple Use Cases • Q&A and Conclusion Agenda
- 17. How to radically transform your IT environments with MongoDB Roman Gruhn Director, Information Strategy, MongoDB [email protected]
- 18. Agenda • Challenges & Opportunities • Modern Information Management • The New Operating Models in IT • Customer Success Stories
- 20. The Dominance of Data “Software is eating the world” “Software is king, but data is queen” Our Mission: Be the data platform for innovators everywhere
- 21. The World Has Changed Leverage Data & Technology to Maximise Competitive Advantage Accelerate Time to Value Dramatically Lower TCO Reduce Risk for Mission-Critical Deployments Data Applications Commercials Risk Our Value Drivers: Volume Velocity Variety Time to value Architectures Operating Models Scalability Opex vs Capex TCO 24/7 availability Global impact Business criticality
- 22. Key Topics Regulatory & Compliance Customer Expectations Startups (Social, Media, FinTech / etc.) Cloud TCO Talent
- 24. Key decision criteria What to think about when choosing a modern data platform Deployment Flexibility On-premise, Private, Public, or Hybrid without vendor lock-in Reducing Complexity Broad use case applicability to avoid additional complexity Agility Accelerate time to market and speed of change for the business Resiliency Engineered for high availability across distributed architectures Scalability Elastically grow with demand Cost Aligned to actual demand and value but with predictability Security Leverage best in class and appropriate security controls
- 25. Legacy Legacy systems are falling short RDBMS systems were not created for today’s requirements and consequently try to bolt-on features to compensate for the lack of capabilities. But this strategy can’t compete with data systems purpose-built to solve today’s problems. Rigid Schemas Resistant to change Throughput & Cost make Scale-Up Impractical Relational Model Scale-up Data changes constantly, which fits poorly with a relational model Scale-Up clusters were never meant to handle today’s volumes
- 26. MongoDB combines the best of Relational & NoSQL Scalability & Performance Always On, Global Deployments Flexibility Strong Consistency Enterprise Management & Integrations NoSQL Expressive Query Language & Secondary Indexes Relational
- 27. MongoDB – Multi-purpose operational data platform MongoDB is the most powerful, holistic data management platform in the market today, helping you to reduce system complexity, drastically lower TCO, increase productivity and minimise risk for critical operations. Multi-Model database – rich use cases require “more” than just relational queries (document, graph, search, etc) Multi-Workload support – combine operational and analytical workloads in a single, powerful data platform Multi-structured, polymorphic data – real-life data doesn’t fit into rows/columns and changes over time Maximum deployment optionality – from on-premise and VMs to hybrid/public cloud and Database-as-a-Service K-V SQLDOC Cloud / DBaaSOn-premise / self-managed +
- 28. Strategic SaaS, Mobile, Social Microservices / API Access / JSON Polymorph Data (structured, semi-structured, unstructured) Hadoop, Spark Commodity HW / Cloud Local Storage / Cloud Software-Defined Networks MongoDB and Enterprise IT Strategy Our technology can help you transform your IT organization and modernize the entire IT stack by enabling you leverage strategic solutions on every level to drive business transformation. Legacy Apps On-Premise Data Access Object-Relational Mapping / ODBC Access / SOAP Database Oracle / Microsoft Data Schemas Relational Data / Structured Offline Data Teradata Compute Scale-Up Server Storage SAN Network Routers and Switches MongoDB sits right at the centre of strategic IT and business / digital transformation, enabling full stack modernization. By removing layers we can: • Reduce complexity • Reduce cost • Increase business agility • Improve data & service quality • Facilitate innovation
- 29. The New Operating Model in IT
- 30. Technology Landscape Transitions Platform 1: Mainframes Platform 2: Client/Server Platform 3: Cloud/Mobile 1960s-1980s 1990s-2000s 2010-Beyond
- 31. Multi Cloud On Premises Desktop Cloud Self-Managed Fully Managed MongoDB - Develop and run anywhere
- 33. Let our team help you on your journey to efficiently leverage the capabilities of MongoDB, the database that allows innovators to unleash the power of software and data for giant ideas. Being successful with MongoDB We have worked with over 50% of the Fortune 500 companies. While the definition of success metrics look different for each one of them, 2 key factors are consistent across all of our engagements: 5xProductivity We help our customers to increase overall output, e.g. in terms of development or ops productivity. 80%Cost reduction We help our customers to dramatically lower their total cost of ownership for data storage and analytics by up to 80%.
- 34. Problem Why MongoDB Results Problem Solution Results • With the advent of mobile banking, Barclays has experienced a significant growth of traffic originating from mobile devices to Mainframe platforms that supports banking applications. Growth of traffic, which is expected to continue, has led to an increased cost of operations and decreased performance • Ability to provide high resiliency during mainframe outages • Existing ETL processes that load transaction data into Teradata on a daily basis are updated to additionally feed data to MongoDB paving the way for decommissioning of Teradata. In subsequent phases of the project, MongoDB will be updated in near real-time via a live transactions feed • De-normalized real time data store using MongoDB with the benefits to reduce growth • Stand-in capability to support Resiliency during planned and unplanned outages across mainframe system and other source systems • Reduced cost of operations • Reduced number of read only transactions to Mainframes , there by freeing up mainframe resources for additional growth Operational Data Source Data lake to store data from multiple sources for operations on the data. ODS is built to store and process read only customer transactions for business operations, analysis and reporting.
- 35. Problem Why MongoDB ResultsProblem Solution Results Massive variability in data structured ingested from customer systems: highly concurrent batch loads and continuous queries Relational databases didn’t provide schema flexibility or scalability Hadoop was too complex MongoDB Enterprise Advanced running on Azure Complex queries and aggregations to support ad-hoc, exploratory queries MongoDB Connector for BI to provide rich visualizations in Tableau MongoDB Cloud Manager for operational automation and disaster recovery First to market with unique management accounting services 50% faster development time than any other database 5x scale on same infrastructure footprint Cloud-Based Data Lake Industry-first “benchmarking” service for 70,000 French businesses, built on MongoDB & Azure France
- 36. Problem Why MongoDB Results Problem Solution Results High licensing costs from proprietary database and data grid technologies Complex architecture and duplicated processes across system stacks Data duplication across siloed systems with complex reconciliation controls Tech estate simplification initiative driven by cost efficiency programme High operational complexity impacting service availability & speed of application delivery Open source MongoDB, Kafka & RabbitMQ that underpins several core trading platforms Rationalised data layer behind a common API, implemented by a multi-tenant PaaS, tackling common data problems Flexible data model and secondary indexes to provide support for wide variety of data and queries. New solution is a self-service Large scale cost reduction: • £m license avoidance of incumbent technologies • Dramatic cost per TB storage saving Technology simplification: Roadmap to decommission hundreds of servers Increased Velocity: • Develop new apps in days • Self-service data service Data Fabric (Data-as-a-Service) Migration from Oracle & Microsoft to create a consolidated “data fabric” reduces $m in cost, speeds application development & simplifies operations
- 37. RBS FY 2016 Investor Report - Excerpt During their recent FY 2016 Investor Report, RBS CEO Ross McEwan highlighted their Data Fabric platform (built on MongoDB) as a key enabler to helping the Bank reduce cost significantly and dramatically increase the speed at which RBS can deploy new capabilities.
- 38. Single Platform for Financial Data Quantitative investment manager with over $11B in assets under management invests heavily in new database Problem Why MongoDB ResultsProblem 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
- 40. • How the World has Changed Since Relational Databases were Invented • How to radically transform your IT environments with MongoDB • MongoDB with Blockchain • MongoDB, the Database of choice for multiple Use Cases • Q&A and Conclusion Agenda
- 41. Are you on a Blockchain DIET? Trusted Data for Unstoppable Code Kamal Ved
- 42. Blockchain DIET Secure Value Exchange Decentralized / shared control Immutable data provenance & audit trail Tokenize value attribution Exchange assets Security & Trust Value & Attribution
- 43. Blockchain: “spreadsheet in the sky” Decentralized, immutable storage Append-only > versioned > audit trails Tokens, assets, value “world computer” Unstoppable code Smart contracts Legally binding execution?
- 44. Why you need a Blockchain DIET?
- 46. ISS Global Partnership for Space Advancement Source: https://www.nasa.gov/mission_pages/station/research/news/global_partnerships
- 47. Blockchains ♥ Data & AI
- 49. “Self Driving cars are the killer app for AI” -Madrona Venture Group Problem: Data is Expensive Sol’n: A Decentralized Data Exchange for Self-Driving Car Data
- 50. Mo’ data (and mo’ compute) Mo’ accuracy Mo’ $
- 52. To blockchain or not to blockchain? Are you the middleman? Can you be replaced by a smart contract? Do you have multiple sources of truth? Ecosystem – can you reconcile & settle faster?
- 53. The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail Source: Clayton Christensen’s Popular Book - http://www.claytonchristensen.com/books/the-innovators-dilemma/
- 54. Retail / (e)Commerce / Other Disruptions
- 55. Ripple vs SWIFT: Payment (r)evolution ● Cross border payments slow, expensive and opaque ● Ripple offers sub-second efficiently priced payments ● Swift had to respond with Global Payments Initiative (GPII) Source: https://www.linkedin.com/pulse/ripple-vs-swift-payment-revolution-david-blair
- 56. ICO vs Angel/Seed VC Funding
- 57. Status quo compute infrastructure Modern apps use processing, files, data FILE SYSTEM e.g. S3, HDFS APPLICATION PROCESSING e.g. EC2, Azure DATABASE e.g. MySQL, MongoDB PLATFORM e.g. AWS, Google App Engine, Heroku CONNECTNETWORKS e.g.TCP/IP
- 58. Towards a decentralized compute infrastructure FILE SYSTEM e.g. S3, HDFS IPFS, SWARM APPLICATION PROCESSING e.g. EC2, Azure, Ethereum, Hyperledger, Tendermint, Lisk, Corda, Tymlez DATABASE e.g. MySQL, MongoDB BigchainDB, IPDB PLATFORM e.g. AWS, Google App Engine, Heroku, Eris/Monax, BlockApps, Tymlez CONNECTNETWORKS e.g.TCP/IP,InterledgerILP e-Cash/e-Gold Bitcoin, zCash, Ripple, Blockstream, Multichain
- 59. Global networks
- 60. 130 GB 2-7 tx/s What about planetary scale?
- 61. The first “Blue Ocean” DBs: Relational DBs Benefits: powerful structured querying Winner: Oracle, 80s and 90s
- 62. The next “Blue Ocean” DB: Website-ready DBs New benefits: lightweight for startups Winner: MySQL, early 2000s
- 63. The next “Blue Ocean” DB: Distributed / NoSQL DBs New benefits: “Big data” scale, flexible schemas Winner: MongoDB, late 2000s-now
- 64. How do “big data” databases scale? Answer: Distribute storage across many machines, i.e. sharding A “consensus” algorithm keeps distributed nodes in sync.
- 65. 0 5 0 10 0 15 0 20 0 25 0 30 0 35 0 0 200,00 0 400,00 0 600,00 0 800,00 0 1,200,00 0 175,00 0 367,00 0 537,00 0 1,100,00 0 Nodes Writes/s How well do “big data” distributed DBs scale? Example: Cassandra scaling. More nodes = more throughput, more capacity!
- 66. The next blue ocean DB: blockchain database New benefits: decentralized, immutable, native assets Who: BigchainDB
- 67. How to build a scalable blockchain database (BigchainDB) 1. Start with an enterprise-grade distributed DB, e.g. MongoDB 2. Engineer in blockchain characteristics
- 68. Alice Bob Blockchain consensus Add tolerance to double-spends + other byzantine faults DB consensus Fault-tolerant consensus
- 69. “Big Data” + “Blockchain” - a decentralized database Immutability Decentralized Control Native Assets Scalable Queryability Operationalized Traditional Databases Traditional blockchains BigchainDB
- 70. Kamal Ved [email protected] https://www.linkedin.com/in/kamalved Thanks for listening
- 72. Vertical: Intellectual Property Digital art Value proposition: Compensate creators Claiming attribution Licensing
- 73. Vertical: Identity Value proposition: Sovereign personal data General Data Protection Reg. Right to be forgotten ©ITU/L.Berney, (CC BY 2.0)
- 74. Vertical: Identity Education Credentials for HR Value proposition: Reduce fraudulent degrees Lower HR friction
- 75. Blockchain: a trust network for supply chains Powered by IoT 75 Recordkeeping paper, sms, email, calls, … Inefficiency change records, own versions/formats, disagreements
- 76. Innovation as a Luxury 76
- 77. DATABASE IPDB CLIENT SIDE APP BROWSER/JS OR MOBILE APP A digital passport for Cars
- 78. Problem: Data Hoarding (2) Sol’n: Data Pooling For More Accurate Models Diamond price prediction for fraud detection: Warn if predicted price !≈ asking price
- 79. • How the World has Changed Since Relational Databases were Invented • How to radically transform your IT environments with MongoDB • MongoDB with Blockchain • MongoDB, the Database of choice for multiple Use Cases • Q&A and Conclusion Agenda
- 80. MongoDB, the DB of choice for multiple Use Cases Benjamin Lorenz Senior Solutions Architect, MongoDB [email protected]
- 81. Cases for Change MongoDB is a modern, operational database that supports a polyglot data strategy – on-premise and in the cloud. This allows us to drive several business critical topics with our customers. Cloud Data Strategy Leveraging the right data platforms as part of your overall cloud strategy helps to avoid vendor lock-in. Legacy Modernisation Current legacy technology stacks can’t cope with the range of new business requirements – we can help you modernise in a highly efficient and effective way. Mainframe Offloading Reduce cost and MIPS on legacy mainframes and enable data to be leveraged for new use cases. Operational Intelligence Solving the problem of deriving value from existing EDW or Hadoop-based data lake solutions in real-time. Single View Provide a holistic view of data entities (e.g. customer) across multiple underlying, disconnected source systems. Compliance & Regulation (e.g. PSD2) MongoDB is enabling scalable, highly available data platforms to banks who are forced to provide data in a more agile way to comply with the PSD2 regulations. Internet of Things (IoT) MongoDB can help you overcome Scalability & Performance issues that are not being met by many current IoT solutions
- 82. Cases for Change MongoDB is a modern, operational database that supports a polyglot data strategy – on-premise and in the cloud. This allows us to drive several business critical topics with our customers. Cloud Data Strategy Leveraging the right data platforms as part of your overall cloud strategy helps to avoid vendor lock-in. Legacy Modernisation Current legacy technology stacks can’t cope with the range of new business requirements – we can help you modernise in a highly efficient and effective way. Mainframe Offloading Reduce cost and MIPS on legacy mainframes and enable data to be leveraged for new use cases. Operational Intelligence Solving the problem of deriving value from existing EDW or Hadoop-based data lake solutions in real-time. Single View Provide a holistic view of data entities (e.g. customer) across multiple underlying, disconnected source systems. Compliance & Regulation (e.g. PSD2) MongoDB is enabling scalable, highly available data platforms to banks who are forced to provide data in a more agile way to comply with the PSD2 regulations. Internet of Things (IoT) MongoDB can help you overcome Scalability & Performance issues that are not being met by many current IoT solutions
- 83. Cases for Change MongoDB is a modern, operational database that supports a polyglot data strategy – on-premise and in the cloud. This allows us to drive several business critical topics with our customers. Cloud Data Strategy Leveraging the right data platforms as part of your overall cloud strategy helps to avoid vendor lock-in. Legacy Modernisation Current legacy technology stacks can’t cope with the range of new business requirements – we can help you modernise in a highly efficient and effective way. Mainframe Offloading Reduce cost and MIPS on legacy mainframes and enable data to be leveraged for new use cases. Operational Intelligence Solving the problem of deriving value from existing EDW or Hadoop-based data lake solutions in real-time. Single View Provide a holistic view of data entities (e.g. customer) across multiple underlying, disconnected source systems. Compliance & Regulation (e.g. PSD2) MongoDB is enabling scalable, highly available data platforms to banks who are forced to provide data in a more agile way to comply with the PSD2 regulations. Internet of Things (IoT) MongoDB can help you overcome Scalability & Performance issues that are not being met by many current IoT solutions
- 84. Definition: Operational Data Layer (ODL) • An Operational Data Layer (ODL) is a database designed to allow disparate systems to persist their data in a common schema, regardless of structure and incorporate scale-out capabilities for resilience and scalability • An ODL is designed to span deployment environments, from Private to Public to Hybrid and Cross Cloud topologies. • An ODL must be able to natively interoperate with various application types • ODL is able to hold multi-structured data and if needed grow into the petabyte scale • Other properties of an ODL are the ability to interoperate with API integration frameworks, facilitating continuous integration
- 85. Problem / Solution Overview RDBMS Files Mainframe Application Microservices / API Layer ReadsWrites Key/Value Store Files Mainframe Application Typical Architecture Complex & Fragile Operational Data Layer (ODL) Simplified & Resilient Application Application Application In-Memory Cache RDBMS Wide-Column Store Application Application Non-standard data access Standardised Data Access Near Real-Time CDC Message Streaming/Proce ssing Graph Store Replication
- 86. Characteristics: Operational Data Layer (ODL) • Supports Structured, Semi-Structured and Un-Structured data with the same level of functionality • Native drivers connect applications to data without need for conversion (JSON) • Multi-tenancy through use of a common data model • Native support for all deployment types • On-premise/Bare Metal, Private, Public, Hybrid and Cross Clouds • Scale-out architecture supports all deployment types in mixed mode • Information Lifecycle Management easily managed by workload and geography Data Agnostic Deployment Agnostic&
- 87. The problem Operational Data Layers solve • Reliance on legacy systems (Mainframe, RDBMS) • High profile outages & downtime • Slow time to market • Complexity & risk of change • Regulatory requirement to open backend systems to public APIs Enterprises are forced to simplify their technology landscape
- 88. Why MongoDB for Operational Data Layer • Workload isolation • Expressive queries & secondary indexes • Data model flexibility with a dynamic schema • Real-time analytics • Performance, scale & always-on • Enterprise deployment model
- 89. Cases for Change MongoDB is a modern, operational database that supports a polyglot data strategy – on-premise and in the cloud. This allows us to drive several business critical topics with our customers. Cloud Data Strategy Leveraging the right data platforms as part of your overall cloud strategy helps to avoid vendor lock-in. Legacy Modernisation Current legacy technology stacks can’t cope with the range of new business requirements – we can help you modernise in a highly efficient and effective way. Mainframe Offloading Reduce cost and MIPS on legacy mainframes and enable data to be leveraged for new use cases. Operational Intelligence Solving the problem of deriving value from existing EDW or Hadoop-based data lake solutions in real-time. Single View Provide a holistic view of data entities (e.g. customer) across multiple underlying, disconnected source systems. Compliance & Regulation (e.g. PSD2) MongoDB is enabling scalable, highly available data platforms to banks who are forced to provide data in a more agile way to comply with the PSD2 regulations. Internet of Things (IoT) MongoDB can help you overcome Scalability & Performance issues that are not being met by many current IoT solutions
- 90. Mainframe Client-Server Web Control & Efficiency Agility & Innovation Cloud & Mobile Distributed, NoSQL Databases 1980s Mid 90s – 2000s 2015>1960s-70s The Cloud Era: Platform Transformation
- 91. Seismic Shifts — Organizational
- 92. Seismic Shifts — Application Architecture
- 93. API Access Layer Operational Data Customers Products Accounts ML Models Shared Physical Infrastructure App1 App2 App3 Development agility – UI for self-service provisioning & scaling Data Re-use – Each service’s data is physically isolated into its own database instance – Federated across services with appropriate permissioning Corporate Governance – Logically managed as one service Cloud Data Strategy Standardized, On-Demand DB Service Cloud Agnostic Any Cloud, Anywhere
- 94. #MDBE16 Cloud ManagerOps Manager MongoDB Atlas Private DBaaS: On-Prem Eliminating Lock-In Hybrid DBaaS Public DBaaS: Fully Managed Same Code Base, Same API, Same Management UI
- 95. Why MongoDB for Cloud Data Strategy? • Freedom of choice • On premise and as managed service • Same code base everywhere
- 96. Why MongoDB Atlas? • Ready for developers and DevOps • Scalable back-end for your application on-demand • Secure by default • High available, even while scaling • Path maintenance performed for you • Your own MongoDB cluster in the cloud (multitenant)
- 97. IoT App Running on MongoDB Atlas Biotechnology giant uses MongoDB Atlas to allow their customers to track experiments from any mobile device Problem Why MongoDB ResultsProblem Solution Results Thermo Fisher is developing Thermo Fisher Cloud, one of the largest cloud platforms for the scientific community on AWS For scientific IoT applications, internal developers need a database that could easily handle a wide variety of fast-changing data Each experiment produces millions of “rows” of data, which led to suboptimal performance with incumbent database Thermo Fisher customers need to be able to slice and dice their data in many different ways MS instrument Connect allows Thermo Fisher customers to see live experiment results from any mobile device or browser MongoDB’s expressive query language and rich secondary indexes provide flexibility to support both ad-hoc and predefined queries to support customers’ scientific experiments Deployed MongoDB using MongoDB Atlas, a hosted DB service running on Amazon EC2 Thermo Fisher customers now can obtain real-time insights from mass spectrometry experiments from any mobile device or browser; not possible before Improved developer productivity with 40x less code in testing with MongoDB when compared to incumbent databases Improved performance by 6x Easy migration process & zero downtime. Testing to production in under 2 months
- 98. Key decision criteria What to think about when choosing a cloud data platform Deployment Flexibility On-premise, Private, Public, or Hybrid without vendor lock-in Reducing Complexity Broad use case applicability to avoid additional complexity Agility Accelerate time to market and speed of change for the business Resiliency Engineered for high availability across distributed architectures Scalability Elastically grow with demand Cost Aligned to actual demand and value but with predictability Security Leverage best in class and appropriate security controls
- 99. Wrapping Up
- 100. Conclusion 1 MongoDB is reshaping the DB Management Landscape 2 Time to Market, Developer Productivity and TCO are driving this Change 3 Engage with your local MongoDB Team
- 101. Resources to Get Started Spin up a cluster on the Free Tier today Download the Whitepaper