DRIVING INTELLIGENCE TO THE EDGE – FEATURING AN END-TO …

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DRIVING INTELLIGENCE TO THE EDGE – FEATURING AN END-TO-END OPEN ARCHITECTURE FOR IoT

Strata Data Conference, September 12, 2018 Dave Shuman, Cloudera, Industry Lead for Manufacturing and IoT Bryan Dean, Red Hat, Director Business Development for IoT Solutions

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Securely connect, authenticate and manage disparate connected devices that speak different protocols

Apply analytics at the edge with machine learning and business rules to enable local, low-latency decision making

Centralize IoT data processing, analytics and machine learning to enable deep business insights and actionable intelligence

Create and deliver cloud-native applications. Integrate business applications and processes.

Tools to enable end-to-end data security, compliance, authorization and authentication

KEY FUNCTIONALITY FOR AN END-TO-END IoT ARCHITECTURE

Device Management & Connectivity

Intelligent Edge Processing & Analytics

Advanced Analytics & Machine Learning

Business & Application Integration

End-to-End Security & Compliance

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WHY OPEN SOURCE FOR IoT

“We believe the best way to support this complex environment is to base our commercial IoT platform, the Bosch IoT Suite, on open source components and open standards. These projects establish a horizontal open technology for IoT and provide the technical breeding grounds for successful business ecosystems.” - Dr. Stefan Ferber, VP of Engineering, Bosch Software Innovations

2.4 30* 250+ 130K

million lines of code

projects developers monthly visitors

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ADDRESSING ENTERPRISE IoT NEEDS

Data Management & Analytics

●  Enterprise Data Mgmt.

●  Persistent Data Storage

●  Big Data Processing & Analytics

●  Real-Time Analytics

●  Machine Learning

●  Data Security & Compliance

Operational Technology (OT)

●  Device Management

●  Industrial protocols

●  OT Middleware

●  Intelligent gateways

●  MQTT co-inventors

●  OT security

Information Technology (IT)

●  Messaging & Integration

●  Business Rules & CEP

●  Open Hybrid Platform-as-a-Service

●  Enterprise Linux Platform

●  IT security

Enterprise IoT open source community

Operational Technology (OT) Information Technology (IT)

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public, private, hybrid cloud

DATAMANAGEMENT&ANALYTICSPLATFORM

IoTEDGE

CONNECTED“THINGS”

IoTINTEGRATIONHUB

OPEN END-TO-END IoT ARCHITECTURE

APPLICATIONDEVELOPMENT,DELIVERY,&INTEGRATION

Integrating IoT operating technology, data management, analytics, and applications

Cloud-native apps

Traditional apps

• Modular, secure, end-to-end architecture • Streaming analytics and machine learning • Open, interoperable on hybrid cloud • Modern application development and agile integration

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Sensors, Actuators,

Data Sources

Edge Processing & Analytics


IoTINTEGRATIONHUB


Data Integration, Routing, Device

Command/Control Application Development, Deployment, Integration


EDGE PROCESSING AND ANALYTICS

IoTEDGE

•  Device connectivity •  Data transformation •  Intelligent routing •  Edge applications •  Edge analytics & real-time

decisions

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Command/Control

TelemetryData

Application Development, Deployment, Integration


DATA INTEGRATION, ROUTING, DEVICE COMMAND/CONTROL

IoTINTEGRATIONHUB


Sensors, Actuators,

Data Sources


•  Device management, security, access control

•  Data aggregation •  Event processing •  Agile integration •  Container-based

application platform

IoTEDGE


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Command/Control Advanced Analytics & Machine Learning


•  Data ingest •  Stream / batch processing •  Secure data storage •  Machine learning and

real-time analytics

IoTEDGE

ADVANCED ANALYTICS AND MACHINE LEARNING

Machine Learning Model


Sensors, Actuators,

Data Sources


IoTINTEGRATIONHUB


Telemetry Data

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Application Data


APPLICATION DEVELOPMENT, DEPLOYMENT, INTEGRATION


Cloud-native apps

Traditional apps



IoTEDGE


Sensors, Actuators,

Data Sources

IoTINTEGRATIONHUB


Command/Control


•  Container-based application platform with Kubernetes orchestration

•  DevOps: automated build-test-deploy •  Agile integration •  Multi-cloud portability

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IoTEDGE


IoTINTEGRATIONHUB


Cloud-native apps

Traditional apps

OPEN END-TO-END IoT ARCHITECTURE

•  Device connectivity •  Data transformation •  Intelligent routing •  Edge applications •  Edge analytics & real-time

decisions

•  Data ingest •  Stream / batch processing •  Secure data storage •  Machine learning and

real-time analytics

•  Container-based application platform with Kubernetes orchestration

•  DevOps: automated build-test-deploy •  Agile integration •  Multi-cloud portability


•  Device management, security, access control

•  Data aggregation •  Event processing •  Agile integration •  Container-based platform

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IoTEDGE


IoTINTEGRATIONHUB


Cloud-native apps

Traditional apps

OPEN END-TO-END IoT ARCHITECTURE: PRODUCTS


ENTERPRISEDATAHUB

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IoTEDGE

IoTINTEGRATIONHUB


Protocol Translation

IntelligentFiltering Aggregation Routing


Real-Time Analytics

Data Ingest Real-Time Processing

Data Storage

Machine Learning

Data Security

TelemetryData

Application Integration

Management

Data flow to derive deep business insights and actionable intelligence END-TO-END ANALYTICS

Telemetry Data

Deepdataanalysis&insights

Application Data

Cloud-native apps

Traditional apps


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Protocol Translation

IntelligentFiltering Aggregation Routing

Deepdataanalysis&insights

Real-Time Analytics

Data Ingest Real-Time Processing

Data Storage

Machine Learning

Data Security

Telemetry Data


Application Data

Data flow to derive deep business insights and actionable intelligence END-TO-END ANALYTICS

Machine Learning

MLModel

Actions

Prediction/Alert

MLModel

IoTINTEGRATIONHUB


Cloud-native apps

Traditional apps


IoTEDGE


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PUTTING IT INTO PRODUCTION

Data Mgt. and Analytics

/home/cdsw/trained-model.h5

IoT Edge

IoT Hub

Connected “Things”

Predict Locally

Capture Images

Download Model

Report Result

A trained model becomes a “downloadable brain”

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REAL WORLD DEPLOYMENTS

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END-TO-END IOT ARCHITECTURE - PoC

Application Data

Telemetry Data

MQTT over WiFi

Telemetry Data

Command & Control

Telemetry Data


Client XDK Data Mgt. and Analytics

IoT Edge

Network: Red Zone Network: Green Zone

Telemetry Data

IoT Integration Hub

Applications

Digital Twin

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IoT Integration Hub

Application Data

Telemetry Data

MQTT over WiFi

OT Middleware OT Middleware

Smart Services

Machine Learning

Business Logic

Device Management

Device Connectivity

Administration

Platform-as-a-Service

Ingest

Machine Learning

Store

Analyze

Process

Telemetry Data

Command & Control

Telemetry Data


Client XDK

Digital Twin

Data Mgt. and Analytics

BI IoT Edge

Network: Red Zone Network: Green Zone

Apps CDSW PPM

Telemetry Data

END-TO-END IOT ARCHITECTURE - PoC

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OPC-UA

Kafka Spark Kudu

Integrate Store

Spark

Impala

Analyze

Unified Services Layer

YARN Sentry

Data Science Workbench

TCP

/IP

Mqt

t / O

PC

-U

A

IoT Calibrators Edge IoT Site Hub

TCP/IP mqtt

•  Device connectivity •  Data transformation •  Intelligent routing •  Business logic •  Edge analytics & real-time

decisions

•  Device management, security, and access control

•  Data aggregation •  Event processing •  Integration services (API’s)

TCP/IP Kafka

Model Deployment

DATA PIPELINE & ARCHITECTURE

•  Data ingest •  Stream / batch processing •  Persistent data storage •  Machine learning and

real-time analytics

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Man

ifold

Pre

ssur

e

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Stacks for Machine Learning at the Core and Edge

CORE EDGE

Machine Learning Library

Machine Learning Platform

Operating System

Cloud / On-Prem Infrastructure

Model Training Application


Edge Software Library / Framework

Operating System

Physical Device

Model Serving Application

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So what about a Classifier?

CORE EDGE

DL4J

Cloudera Data Science Workbench

RHEL

AWS EC2 & S3

TrainClassifier.java

DL4J

Everyware Software Framework (Java OSGi)

Yocto Linux

Eurotech ReliaGATE 20-25

ServeClassifier.java



Operating System

Infrastructure


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Or…

CORE EDGE

Keras (w/ TensorFlow backend)

Cloudera Data Science Workbench

RHEL

Azure & ADLS

train_classifier.py

Tensorflow Lite (Java API)

Everyware Software Framework (Java OSGi)

Yocto Linux

HPE Moonshot

ServeClassifier.java



Operating System

Infrastructure


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TECHNICAL OVERVIEW: FUNCTIONALITY

IoT EDGE

CONNECTED “THINGS”

Telemetry

Management

IoT INTEGRATION HUB

Machine learning model

APPLICATION DEVELOPMENT, DELIVERY, & INTEGRATION

CONTAINER-BASED APPLICATION PLATFORM

Traditional applications Cloud-native applications

ERP

CRM

DBs

DATA MANAGEMENT & ANALYTICS

Data engineering

Data science

Data warehouse

Operational database

Security & governance

Machine learning

Storage Services

ENTERPRISE DATA HUB

Telemetry

Management

App integration


IOT EDGE FRAMEWORK

DevOps

Integration &

API mgmt Orchestration

Developer services

Edge analytics

Machine learning

Field connectivity

Cloud connectivity

Edge applications

Telemetry

IOT INTEGRATION FRAMEWORK

Device management

Device connectivity

Data integration


INDUSTRY 4.0 DEMO

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TO LEARN MORE…

Visit the Cloudera Booth

Industry 4.0 Demo

Featuring the End-to-end architecture and solution

Join us for a Webinar on Oct 3rd

to hear about the end-to-end Solution

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THANK YOU…

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BACKUP SLIDES


PREDOMINANT END-TO-END IoT ALTERNATIVES

Alternative Strength Weakness

Full-Stack Public Cloud Providers

•  Quick to get started •  Low initial investment •  Offload in-house expertise •  Single vendor source/support

•  Loss of data control •  Proprietary lock-in, limited portability •  Rigid architecture •  Limitations at edge for many use cases •  Cost model at scale

Proprietary IoT Platforms

•  Single vendor packaging •  Medium-quick to get started •  Optional vertical use-case packages

•  Proprietary lock-in, poor portability •  Black-box architecture •  Rigid architecture •  Requires vendor-specific in-house knowledge •  Limited interoperability

Do It Yourself •  Perceived low initial software cost •  Customized to use case •  No vendor middleman

•  Vast in-house expertise required •  Slow time to value; high development and ongoing

costs •  Support and indemnification •  Complexity, maintainability

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TECHNICAL OVERVIEW: FUNCTIONALITY

IoT EDGE


Telemetry

Management

IoT INTEGRATION HUB




Traditional applications Cloud-native applications

ERP

CRM

DBs


Data engineering

Data science

Data warehouse

Operational database

Security & governance

Machine learning

Storage Services

ENTERPRISE DATA HUB

Telemetry

Management

App integration


IOT EDGE FRAMEWORK

DevOps

Integration &

API mgmt Orchestration

Developer services

Edge analytics

Machine learning

Field connectivity

Cloud connectivity

Edge applications

Telemetry

IOT INTEGRATION FRAMEWORK

Device management

Device connectivity

Data integration

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TECHNICAL OVERVIEW: PRODUCTS

IoT EDGE


Telemetry

Management

IoT INTEGRATION HUB



Telemetry

Management

App integration

Telemetry

Edge analytics

Machine learning


ENTERPRISEDATAHUB

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TECHNICAL OVERVIEW: PROJECTS

IoT EDGE


Telemetry

Management

IoT INTEGRATION HUB



DATA MANAGEMENT & ANALYTICS Telemetry

Management

App integration

Edge analytics

Machine learning

Telemetry

CLOUDERA’SDISTRIBUTIONINCLUDINGHADOOP(CDH)

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IoT Gateways

IoT Integration Hub

REMOTE MAINTENANCE & SUPPORT

ON-SITE DATA ANALYTICS

MACHINE LEARNING & ADVANCED ANALYTICS

Data Mgmt, Analytics & ML

Manufacturing Equipment

●  Data ingest ●  Stream / batch

processing ●  Secure data storage ●  Machine learning and

real-time analytics

●  Device connectivity ●  Data transformation ●  Intelligent routing ●  Business logic ●  Edge analytics &

real-time decisions

●  Device management, security, and access control

●  Data aggregation ●  Event processing ●  Integration services

INDUSTRY 4.0 DEMO ARCHITECTURE

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VALUE PROPOSITION

Open and interoperable Future-proof open source architecture | open standards | deployment flexibility

Modular Avoid lock-in | capitalize on existing investments

End-to-end analytics Analytics at the edge | advanced analytics and machine learning | ML model execution at the edge

Reduce risk and complexity

Simplify development, deployment, and integration tasks | save costs

End-to-end security Security across devices, access, authentication, and applications as well as data in motion and at rest

Control your data Privacy | security | regulatory

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Device connectivity Open standards – MQTT, AMQP, OPC-UA, CoAP, HTTP(s)

Flexible deployment

Data management & analytics Based on Apache open source ecosystem libraries for machine learning and advanced analytics

Open application interfaces

Enterprise visibility | real-time anomaly detection | future-proof

Community innovation Collaboration driven by some of the leading enterprises in the IoT space

Any of the leading cloud providers or your data center or hybrid cloud

No vendor lock-in No rigid architectures or proprietary formats and components

OPEN SOURCE, OPEN STANDARDS, FLEXIBLE DEPLOYMENT

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DRIVING INTELLIGENCE TO THE EDGE – FEATURING AN END-TO …