architecting iot for the cloud - a case study

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This presentation describes how FramTack architected their IoT product to send and receive data between IoT devices and cloud databases using an open source REST API platform called DreamFactory. This was presented at the Bay Area Open Source Software meetup at SAP on October 29th, 2014


  • 1. dreamfactoryBen Busse@benbussebenbusse@dreamfactory.comArchitecting IoT for the CloudA Case Study

2. About DreamFactoryOpen Source SoftwareApache licenseQ4 2013 - Version 1.0Q1 2015 - Version 2.0Strong developer and cloud vendor adoptionREST API PlatformRun-time server softwareAuto-generates APIs for SQL, NoSQL, file storageUse CasesREST APIs and server-side security for enterprise mobile appsREST APIs for IoT data 3. Development ProcessInstall Connect DevelopDreamFactoryprovides REST APIServices to your dataBuild apps for phone,tablet, desktop or IoTdevice+ =Install DreamFactoryon IaaS cloud, PaaScloud, or server 4. Unified REST InterfaceDreamFactory Fragmented APIsFiles NoSQL SQLSQLNoSQLFiles 5. FramTack IoT Case StudySoftware VendorSolution Family Product for IoTSolution Engine for processing IoT dataSolution Builder for configuring data collectors, rules, andstatisticsReduces cost and time required to build IoT engine yourselfBuilding Automation Use Case 6. Solution Family Suite6 7. EdgeSolutionEngineDataModelCloudsStorageAnalyticsAppliancesIoT Data Flow2. Analyze DataSolution Builder1. Get Data3. Send Data to/from Cloud4. Control the Appliance5. Build Dashboards 8. Building Automation ExamplePump Room Space TempsIntelGatewayTemperatures PressuresIntel Gateway + PLCSolutionEngineSteam RoomTemperatures PressuresIntel Gateway + PLCAPT1 LobbyElectric MeterAnalytics 9. From Sensor to End UserSolution BuilderSolution EngineMobile AppDashboardDreamFactory AdminConsoleService PlatformSolution FamilyProductsIOT Data to Cloud via RESTAlerts and Analytics via RESTDB Connection, Schema, Data 10. Dashboard Builder7/10/13 10 11. DiscussionData explosionWhat data is actually useful for end users? Transactional vs aggregated data Tolerance thresholds for alerts Learning from false positives and false negativesWhere does data processing occur (e.g. gateway vs cloud)? Complexity of analysis How transient is the data (e.g. one day vs one month)?IoT trade-offsBusiness Requirements e.g. what data matters, what frequency?Cost e.g. API calls, bandwidth, storageSpeed e.g. how real-time must the data be?Scalability related to data explosion considerations above 12. Thank You!