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(Iot) and Big Data 30.9.2016 – DOAG 2016 Big Data Days Internet of Things (IoT) and Big Data 30.9.2016 – DOAG 2016 Big Data Days Guido Schmutz BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENEVA HAMBURG COPENHAGEN LAUSANNE MUNICH STUTTGART VIENNA ZURICH Guido Schmutz Working for Trivadis for more than 19 years Oracle ACE Director for Fusion Middleware and SOA Co-Author of different books Consultant, Trainer, Software Architect for Java, SOA & Big Data / Fast Data Member of Trivadis Architecture Board Technology Manager @ Trivadis More than 25 years of software development experience Contact: [email protected] Blog: http://guidoschmutz.wordpress.com Slideshare: http://www.slideshare.net/gschmutz Twitter: gschmutz 2 Internet of Things (IoT) and Big Data Agenda 1. Introduction 2. Towards an IoT Architecture 3. IoT Refererence Architecture 4. Summary 3 Internet of Things (IoT) and Big Data Introduction 4 Internet of Things (IoT) and Big Data Internet of Things (IoT) Wave Internet of Things (IoT): Enabling communication between devices, people & processes to exchange useful information & knowledge that create value for humans Term was first proposed by Kevin Ashton in 1999 Source: Ericsson, June 2016 Source: The Economist 5 Internet of Things (IoT) and Big Data Reasons why IoT opportunity is occurring now ? Affordable hardware Availability of supporting tools • Costs of actuators & sensors have been • Big data tools & cloud based infrastructure cut in half over last 10 years have become widely available Smaller, more powerful hardware Mass market awareness • Form factors of hardware have shrunk to • IoT has surpassed a critical tipping point millimeter or even nanometer levels • Vision of a connected world has reached such a followership that companies have Ubiquitous & cheap mobility initiated IoT developments • Commitment is irreversible • Cost for mobile devices, bandwidth and data processing has declined over last 10 years 6 Internet of Things (IoT) and Big Data The Sensing-as-a-Service Model 7 Internet of Things (IoT) and Big Data Towards an IoT Architecture 15 Internet of Things (IoT) and Big Data Key Challenges for building an IoT application 1. Connect: How to collect data from intelligent devices? • Abstract complexity associated with device connectivity • Standardize integration of devices with enterprise 2. Analyze: How to analyze IoT data? • Reduce noise and detect business event at real-time • Enable historical big-data analysis 3. Integrate: How to integrate IoT data & events with enterprise infrastructure? • Make enterprise processes IoT friendly • Allow enterprise & mobile applications to control devices 18 Internet of Things (IoT) and Big Data Today) Existing Service-/API Architecture as a base REST External Cloud SOAP Service Providers BPM and SOA Mobile SOAP Platform REST / SOAP API Gateway Apps WS Processes SOA Suite D B Services Oracle Data Integrator Event BPM Suite REST / SOAP Rich (Web) Enterprise Apps REST / SOAP API Gateway Various 19Client Apps D WS Business B Logic/Rules Event DB SQL Business Intelligence Visualization Enterprise Service Bus (ESB) / Data Integration Business Activity Service Bus Analytics Monitoring 19 Internet of Things (IoT) and Big Data = one way = request/response IoT 1a) Reuse exiting Service-/API-based Architecture REST SOAP External Cloud Service Providers Mobile REST / SOAP Apps D BPM and SOA B SOAP Platform API Gateway WS Processes SOA Suite Rich (Web) Oracle Data Integrator Services BPM Suite REST / SOAP Event 20Client Apps D REST / SOAP JMS B Enterprise Apps API Gateway Various WS Business Logic/Rules Event REST JMS DB SQL REST Service Bus Business IoT Smart Intelligence Devices Visualization JMS / REST Enterprise Service Bus (ESB) / Data Integration WebSocket Business Activity Monitoring JMS Analytics JMS Weblogic 20 Internet of Things (IoT) and Big Data = one way = request/response IoT 1a) Challenges • Do IoT devices contain enough resources to communicate directly over the internet (HTTP or JMS) ? • Should the device only collect data (sense) or is there also the way back necessary (actuator) ? • Can JMS be used from external devices (firewalls allow traffic over JMS) ? • How many IoT devices are planned short and long term? How often do they send to the backend? Are the JMS server as well as the ESB capable to deal with the resulting message volume ? • What are the operations on IoT messages / events, only simple transformations, filter and routing operations? 21 Internet of Things (IoT) and Big Data IoT 1b) Reuse existing Service-/API-based Architecture REST SOAP External Cloud Service Providers Mobile Apps REST / SOAP D BPM and SOA B SOAP Platform API Gateway WS Processes SOA Suite Rich (Web) Oracle Data Integrator Event Services BPM Suite REST / SOAP 22Client Apps D REST / SOAP JMS B Enterprise Apps API Gateway Various WS Business REST Logic/Rules Event JMS DB HTTP REST SQL REST HTTP Service Bus Business IoT Smart Intelligence Devices JMS Visualization Enterprise Service Bus (ESB) / Data Integration Business Activity WebSocket Analytics Monitoring JMS JMS Weblogic 22 Internet of Things (IoT) and Big Data = one way = request/response IoT 2) Adding Event Hub and optional IoT Gateway REST External Cloud SOAP Service Providers Mobile REST / SOAP Apps Service Bus D BPM and SOA B SOAP Platform API Gateway WS Processes SOA Suite Rich (Web) Oracle Data Integrator Services REST / SOAP Event BPM Suite 23Client Apps D REST / SOAP JMS B ESB / Data Integration Enterprise Apps API Gateway Various WS Business REST REST Logic/Rules Event JMS DB REST Kafka IoT Smart REST Devices SQL Business Kafka / MQTT / REST REST Intelligence Visualization Business Activity IoT IoT REST MQTT WebSocket Monitoring Devices Gateways Analytics Event Hub Kafka REST Kura Kafka / MQTT / REST MQTT Kafka MQTT 23 Internet of Things (IoT) and Big Data = one way = request/response How to implement an Event Hub? Apache Kafka to the rescue Producer Producer Producer Distributed publish-subscribe messaging system Kafka Cluster Designed for processing of high-volume, real Consumer Consumer Consumer time activity stream data (logs, metrics collections, social media streams, …) Topic Semantic does not implement JMS standard Initially developed at LinkedIn, now part of Apache 24 Internet of Things (IoT) and Big Data Oracle’s Service Bus as a consumer of Kafka Web Apps Service Bus 12c Business Cloud API Cloud Service Cloud Apps Mobile Apps Proxy Pipeline Routing Kafka Service Sensor / IoT Kafka Business Backend REST Service REST Apps Database DB CDC Proxy Pipeline Business Backend Routing Apps WSDL Kafka Service Service WSDL Stream Processing 25 Internet of Things (IoT) and Big Data IoT 2) Solutions & Challenges Solutions • Event Hub solves the potential scalability issue of JMS • IoT Gateway makes sure that lightweight sensors can connect to the internet / send their data Challenges • Where to do complex analytics on the events? Is it scalable? • Can we really send all data down to backend? Network bandwidth? 26 Internet of Things (IoT) and Big Data IoT 3) Adding Stream Processing / Analytics in Backend REST SOAP External Cloud Service Providers Mobile Apps REST / SOAP Service Bus D BPM and SOA B API Gateway SOAP Platform WS Processes SOA Suite Rich (Web) Oracle Data Integrator Event Services BPM Suite Client Apps REST / SOAP REST / SOAP 27 D JMS B API Gateway Enterprise Apps Various WS Business REST REST Logic/Rules (ESB) / Data Integration Event JMS DB IoT Smart Kafka / MQTT / REST Devices SQL Stream Processing Kafka Business Kafka / DB REST Kafka REST Intelligence MQTT / DB Visualization Business Activity IoT MQTT IoT REST Kafka WebSocket ESP / CEP Monitoring Event Hub Analytics Devices MQTT Gateways Kafka Stream Analytics Kafka REST Kura REST MQTT Event Hub Kafka Kafka 27 Internet of Things (IoT) and Big Data = one way = request/response IoT 3) Solutions & Challenges Solutions • Stream Processing handles complex analytics on events in a scalable manner before sending events to ESB / backend systems Challenges • Can we really send all data down to backend? Network bandwidth? 28 Internet of Things (IoT) and Big Data Oracle’s Stream Analytics as consumer of Kafka/MQTT Web Apps Mobile Apps Kafka Kafka Stream Analytics Kafka Kafka Sensor / IoT MQTT Oracle Stream Analytics Machine Data DB CDC 29 Internet of Things (IoT) and Big Data IoT 4) Adding Industry 4.0 Data Sources (machine data) REST SOAP External Cloud Service Providers Mobile Apps REST / SOAP Service Bus D BPM and SOA B API Gateway SOAP Platform WS Processes SOA Suite Rich (Web) Oracle Data Integrator Event Services BPM Suite Client Apps REST / SOAP REST / SOAP 30 D JMS B API Gateway Enterprise Apps Various WS Business REST REST Logic/Rules (ESB) / Data Integration Event JMS DB IoT Smart Kafka / MQTT / REST Devices SQL Stream Processing Kafka Business Kafka / Kafka DB REST Kafka REST Intelligence MQTT / DB Visualization MQTT Business Activity IoT IoT REST WebSocket Monitoring ESP / CEP Analytics Devices MQTT Gateways Kafka Stream Analytics Kafka REST Kura REST Event Hub Event Hub Kafka Kafka I 4.0 Kafka / MQTT / REST Machine MQTT DB CDC GoldenGate 30 Internet of Things (IoT) and Big Data = one way = request/response Oracle’s GoldenGate for Change Data Capture of existing database Machine Data HDFS DB Pump Capture Delivery Oracle GoldenGate JMS Kafka Machine Data GoldenGate Gateway Pump HBase Capture Delivery DB Oracle GoldenGate 31 Internet of Things (IoT) and Big Data IoT 5) Adding Stream Processing / Analytics at Edge REST SOAP External Cloud Service Providers Mobile Apps REST / SOAP
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