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Building a Scalable and Reliable
IoT Platform with HiveMQ and MongoDB
Dr. Christian Kurze
Principal Solutions Architect
MongoDB
Dominik Obermaier
CTO and Co-Founder
HiveMQ
How to optimize your Business with IoT?
Typical IoT Challenges
HiveMQ and MongoDB Solution
Fleet Management Demo
What you will
learn today
Q&A
Or: Why should you care?
How to optimize your Business with IoT?
Digital Services are the Key Differentiator in Global Competition
Intensifying Competition ... … Eroding Margins ...
… leading to the need to rethink
current business models
8 5
41
24
18
28
53
24
Asia-Pacific
Americas
Europe
Rest of World
1998 2019 General industrial machinery
Already commoditized
Future price
premium at stake
Special purpose machinery
Risk of
hardware
commoditization
McKinsey: IIoT platforms: The technology stack as value driver in industrial equipment and machinery
Services
Software
Machinery
1998 2019
Production of machinery
by region (%)
Indicative Value Pools
Digital Services are the Key Differentiator in Global Competition
Intensifying Competition ... … Eroding Margins ...
… leading to the need to rethink
current business models
Leaders gain >15%
positive impact on cost
and revenue via IoT
McKinsey: Best Practices Separating IoT Leaders from Laggards
Develop new IoT Products and Services
e.g. apps, fleet management
Business
Strategies
Drive Sales and Service Efficiency
e.g. monitoring, field services, maintenance, staffing
Optimize Business Operations
e.g. manufacturing, supply chain, R&D
Devices with Sensors and Actuators
e.g. cars, buildings, equipment, wearables
Device Enablement Platforms
e.g. obtaining, importing, and processing data using standard protocols
IT
Initiatives
Business Applications
e.g. customer- and/or device-facing functionality, dashboards, mobile apps
Cloud and Edge Computing
e.g. for new workloads and cost optimizations
A Digital Twin can represent
almost everything:
Machines, Processes, Places,
Buildings, Cities, even
Humans like You and Me.
Global leaders leverage IoT powered by MongoDB
Source of Industries: IHS Markit
What is the core of IoT & Digital Twins?
R&D
Production
Operation
Maintenance
Decommissioning
Describe
Predict
Recommend
Combines all data across the
whole lifecycle of a product
Outlives the physical product in
order to optimize business
processes and unlock new
revenue streams
Product Life-Cycle
Information Life-Cycle
Only ~30% of relevant IoT solutions are in
company-wide roll-out.
Delivering IoT at scale requires the ability to
extract, interpret, and harmonize data from
disparate systems that were not designed to
work together.
McKinsey: Best Practices Separating IoT Leaders from Laggards
Or: Why does not everyone benefit?
Typical IoT Challenges
People on
the Internet
Devices on
the Internet
Web Technology used today is
built for the
Internet of Humans,
NOT for the
Internet of Things
Many Different Types of Data
● Device data arrives in different
formats (JSON, AVRO, Protobuf,
custom binary formats)
● Often in time series data
● Data agnostic message brokers
used to distribute data into the
backend
● Relational databases are sometimes
not well suited for IoT data
Responsiveness of Systems
● Low latency is critical for many IoT
use cases
● End users expect responsive IoT
applications
● Unreliable cellular networks can
have a significant impact on
responsiveness
BMW Case Study: bit.ly/bmw-casestudy
Scalability
● IoT solutions need to
scale to accommodate
growth (100s - 1,000,000s
of devices)
● Scale-up and scale-down
to accommodate spikes
MQTT Broker Cluster
IoT Devices
Extension
Extension
Extension
Enterprise Integration
● IoT data needs to be
integrated into
enterprise systems
● Device to Cloud and
Cloud to Device data
integration
On-premise Self hosted Managed Service
Deployment Agnostic
Atlas
Many different types of data
Top IoT
Challenges
Responsiveness of systems
Enterprise integration
Deployment agnostic
Scalability to thousands & millions of devices
Or: How to tackle the typical challenges?
HiveMQ & MongoDB Solution
IoT Simplified: Same Pattern for any Application
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Data Storage
Hot & Cold Data for
Real-Time & Batch Access
Dashboards
Visual Insights
Applications
User-Facing Applications &
Automations
Advanced Analytics &
Machine Learning
Gaining Insights into Data,
Predict & Act
HiveMQ’s MQTT Broker for Communication
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Data Storage
Hot & Cold Data for
Real-Time & Batch Access
Dashboards
Visual Insights
Applications
User-Facing Applications &
Automations
Advanced Analytics &
Machine Learning
Gaining Insights into Data,
Predict & Act
Publish/subscribe based architecture
Easy
(I)IoT Messaging Protocol
Minimal Overhead
Designed for reliable communication
over unreliable channels
Binary
Data agnostic
What is
MQTT?
Publish / Subscribe Pattern
Logistics
Tele-
communication
IoT Messaging
Middleware
MQTT Use Cases
Connected
Car
IIoT /
Industry 4.0
HiveMQ Enterprise MQTT Platform
Building new digital products
Improving customer experience
Creating more efficient operations
and insights
Avoiding data loss with efficient
system
Our Customers
…and more
...and more
Challenge: Responsiveness of Systems &
Deployment Agnostic
Challenge: Scalability to thousands & millions of devicesChallenge: Scalability to thousands & millions of devices
Challenge: Enterprise Integration
Tackling IoT Challenges
World-class scalable MQTT
● Masterless architecture, Auto healing, elastic
scaling
● Private or Public deployments
● K8s, OpenShift, AWS, Azure, GCP
● Ideal for multi-cloud
Reliable Cloud Native Architecture
● Visibility for operations team
● Live debug of individual clients
● Trace recording for playback on message
sequences
Real-time Monitoring Across Device Fleets
● Data integration with existing enterprise
systems
● Integration with other MQTT clients and
broker
● Off-the-shelf integrations and custom
extensions
Extension Framework and Marketplace
● Scales to 10 million connections and
more
● MQTT 5 fully supported
● Full hybrid support of 3.1.1 and 3.1
HiveMQ & MongoDB on-premises
IoT Devices
Extension
Extension
Extension
HiveMQ Cloud & MongoDB Atlas
Extension
Extension
Extension
IoT Devices
Atlas
MongoDB’s Data Platform for IoT
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Data Storage
Hot & Cold Data for
Real-Time & Batch Access
Dashboards
Visual Insights
Applications
User-Facing Applications &
Automations
Advanced Analytics &
Machine Learning
Gaining Insights into Data,
Predict & Act
MongoDB Atlas: End-2-End Data Platform for IoT
Primary Secondary Secondary
High Volume Real Time Operational Data
Analytical Analytical
Real Time Analytical Data
Data Lake, offline, queryable archive
Lucene based Text Search
Sharding and Replica Sets
Multi-Cloud Data Platform
OneQueryLanguage,API,andSQL
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Mobile Database
Edge to Cloud Sync
Native Visualizations
Applications & Microservices
Advanced Analytics
Triggers & Events
Reporting
MongoDB Atlas: End-2-End Data Platform for IoT
Primary Secondary Secondary
High Volume Real Time Operational Data
Analytical Analytical
Real Time Analytical Data
Data Lake, offline, queryable archive
Lucene based Text Search
Sharding and Replica Sets
Multi-Cloud Data Platform
OneQueryLanguage,API,andSQL
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Mobile Database
Edge to Cloud Sync
Native Visualizations
Applications & Microservices
Advanced Analytics
Triggers & Events
Reporting
MongoDB Atlas: End-2-End Data Platform for IoT
Primary Secondary Secondary
High Volume Real Time Operational Data
Analytical Analytical
Real Time Analytical Data
Data Lake, offline, queryable archive
Lucene based Text Search
Sharding and Replica Sets
Multi-Cloud Data Platform
OneQueryLanguage,API,andSQL
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Mobile Database
Edge to Cloud Sync
Native Visualizations
Applications & Microservices
Advanced Analytics
Triggers & Events
Reporting
MongoDB Atlas: End-2-End Data Platform for IoT
Primary Secondary Secondary
High Volume Real Time Operational Data
Analytical Analytical
Real Time Analytical Data
Data Lake, offline, queryable archive
Lucene based Text Search
Sharding and Replica Sets
Multi-Cloud Data Platform
OneQueryLanguage,API,andSQL
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Mobile Database
Edge to Cloud Sync
Native Visualizations
Applications & Microservices
Advanced Analytics
Triggers & Events
Reporting
MongoDB Atlas: End-2-End Data Platform for IoT
Primary Secondary Secondary
High Volume Real Time Operational Data
Analytical Analytical
Real Time Analytical Data
Data Lake, offline, queryable archive
Lucene based Text Search
Sharding and Replica Sets
Multi-Cloud Data Platform
OneQueryLanguage,API,andSQL
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Mobile Database
Edge to Cloud Sync
Native Visualizations
Applications & Microservices
Advanced Analytics
Triggers & Events
Reporting
MongoDB Atlas: End-2-End Data Platform for IoT
Primary Secondary Secondary
High Volume Real Time Operational Data
Analytical Analytical
Real Time Analytical Data
Data Lake, offline, queryable archive
Lucene based Text Search
Sharding and Replica Sets
Multi-Cloud Data Platform
OneQueryLanguage,API,andSQL
Sensors & Actuators
Wireless communication over
industry standard protocols
Edge Gateway
Offline Storage,
Local Processing
Streaming & Routing
Standard Protocols and Tools,
e.g. MQTT, Kafka
Mobile Database
Edge to Cloud Sync
Native Visualizations
Applications & Microservices
Advanced Analytics
Triggers & Events
Reporting
Benefits by Using One Single Data Platform
Fast Time-to-Market
One database query language for all
platforms, incl. the data lake
One codebase independent of
deployment strategy, Optimization
done once
Transfer of resources between
different teams
Lower TCO
Training (dev & ops) to be done once
Efficiency of scale for operational costs (one team vs.
platform-dedicated teams)
Time-to-Value for new features is the same on all
platforms
One agreement across all platforms,
same support team
High Security
Secure by default on different cloud providers
Reusable security concept on multiple
platforms
Encryption (At Rest, In Use, In Flight),
Authentication/Authorization, Auditing
Low Operational Effort
Same operations tooling and APIs
independent of platform
Same scaling approach and reusable
integrations across all platforms
Technical Support by the same Service
Engineers independent of platform
Modelling a Basic “Thing”
Unique Identifier
Title, Description
Creation, Modification Date
Base Thing
Relational Schema
Enriching a “Thing” Requires more Tables
Unique Identifier
Title, Description
Creation, Modification Date
Base Thing
Properties & Schema
Actions & Input / Output
Events & Schema / Subscription /
Cancellation
Relational Schema
Further Enrichment Creates Complex Schemas
Unique Identifier
Title, Description
Creation, Modification Date
Base Thing
Properties & Schema
Actions & Input / Output
Events & Schema / Subscription /
Cancellation
Translations
Security Schemes
Form Representation
User-Defined Data ? Relational Schema
Strict Schema vs. Flexible Data Model
id title description
12345-WoTLamp-1234 My Lamp A lamp in the room
67890-WoTLamp-1234 My Other Lamp Another lamp in the room
id thing_id title readOnly writeOnly
4711-p1 12345-WoTLamp-1234 status false false
id thing_id title safe idempodent
4711-a1 12345-WoTLamp-1234 toggle false false
id thing_id title readOnly writeOnly
4711-e1 12345-WoTLamp-1234 overheating false false
Thing
Property
Action
Event
{
"id": "123456-WoTLamp-1234",
"title": "My Lamp",
"description": "A lamp in the room",
"properties": {
"status": {
"type": "string",
"readOnly" : false,
"writeOnly" : false
}
},
"actions": {
"toggle": {
"safe": false,
"idempodent": false
}
},
"events": {
"overheating": {
"data": {
"type": "string",
"readOnly" : false,
"writeOnly" : false
}
}
}
}
Relational Example Flexible Document Model
Extensible at Runtime
{
"id": "123456-WoTLamp-1234",
"title": "My Lamp",
"description": "A lamp in the room",
+ "properties": { ... },
"actions": {
"toggle": {
"safe": false,
"idempotent": false
}
},
+ "events": { ... }
}
{
"id": "123456-WoTLamp-1234",
"title": "My Lamp",
"description": "A lamp in the room",
"securityDefinitions": {
"basic_sc": {
"scheme": "basic",
"in": "header"
}
},
+ "properties": { ... },
"actions": {
"toggle": {
"safe": false,
"idempodent": false,
"forms": [{
"op": "invokeaction",
"href": "https://mylamp.example.com/toggle",
"contentType": "application/json"
}]
}
},
+ "events": { ... }
}
Adding Security
and Action
Information
Schema-Free based on well-defined standards like
JSON-LD and W3C’s Web of Things Vocabulary
What about Timeseries?
Schema Design Pattern: Bucketing
https://www.mongodb.com/collateral/time-series-best-practices
Telediagnostics: The Future of Mercedes Benz Services
Learn more about the project in Madalin Broscaru’s MongoDB.live presentation:
Telediagnostics@Mercedes Benz powered by MongoDB
… the Vehicle Data
Conditioning (VDC) where
these technical vehicle events
are processed …
CAC
Retail
Customer
… and the follow-up processes are
triggered with real-time
recommendations for actions.
Vehicles are transmitting regularly
status and health data into ...
Aggregated
Quality Analysis
Telediagnostics: The Future of Mercedes Benz Services
{
.............
"schema": "3.1.0"
"createdAt": {..},
.............
"vehicleIdentData": {
"chassisNumber": "WDD24708A5432J63",
"countryCode": "4f3490b14e238a5f",
"modelSeries": "f16ad22d42064811",
"modelType": "2196868af1c70d74",
"modelYear": "5e01ac15d73c3e4a",
"steering": "4a3424fe6411461c"
},
"basicData": {
"mileage": {..},
"batteries": [..],
"tanks": [..],
"tiresPressure": [..],
},
"controlUnits": [..],
"affectedFunctions": [..],
"vehicleClusterMessagesData": [..],
"maintenanceData": {...},
..............
}
"controlUnits": [
{
"ecuId": {..}
"name": "a927e49b0549f00f71",
"detailsHardware": {},
"detailsSoftware": {},
"dtc": [
{
"code": "B214F73",
...
"failureText": "81957650ea",
"environmentalData": {...}
},
...
]
},
...
]
Or: How does a solution look like? Show me the architecture and the code!
Fleet Management Demo
IoT is broad - let’s look into a specific example
Source of Industries: IHS Markit
Fleet Management
Covers fleets of commercial vehicles, fork lifts,
trains, goods, literally anything that forms a fleet
and needs to be monitored and actively managed.
Major Challenges
High distribution of fleet, unstable network
connections, multitude of device types and data
structures, scaling from 100’s to 1,000’s to 1,000,000s
Typical Benefits
Reduction of shipping costs, goods arrive in time, less
outages and damages, reaction times in minutes
instead of hours, CO2 reporting and reduction, higher
fleet utilization, compliance with legal requirements
Getting Started with MongoDB & HiveMQ
MongoDB Atlas
Free Tier Cluster
Retention ½ Day
HiveMQ
vehicles/trucks/truck-XXXXX
MQTT
Subscriber
Python
Trucks on tour
sending location,
speed, break time
MQTT
Subscriber
Python
Truck Simulator:
- >9000 Warehouses across Germany
- Trucks travel between random warehouses
- Every second, the current location, speed, speed
limit and break information is sent
Real-Time Position of Trucks:
- Subscription to MQTT topics
for live visualization
Analysis of Trucks:
- Visualization of historical
data based on truck routes
Getting Started with MongoDB & HiveMQ
MongoDB Atlas
Free Tier Cluster
Retention ½ Day
HiveMQ
vehicles/trucks/truck-XXXXX
MQTT
Subscriber
Python
Trucks on tour
sending location,
speed, break time
MQTT
Subscriber
Python
Truck Simulator:
- >9000 Warehouses across Germany
- Trucks travel between random warehouses
- Every second, the current location, speed, speed
limit and break information is sent
Real-Time Position of Trucks:
- Subscription to MQTT topics
for live visualization
Analysis of Trucks:
- Visualization of historical
data based on truck routes
Or: What about X, Y and Z?
Q&A
Get Started Today!
MongoDB Atlas
https://cloud.mongodb.com
HiveMQ
https://hivemq.com/
We love to hear your feedback!
Dr. Christian Kurze | Principal Solutions Architect | christian.kurze@mongodb.com
Dominik Obermaier | CTO and Co-Founder | dominik.obermaier@hivemq.com

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  • 1. Building a Scalable and Reliable IoT Platform with HiveMQ and MongoDB Dr. Christian Kurze Principal Solutions Architect MongoDB Dominik Obermaier CTO and Co-Founder HiveMQ
  • 2. How to optimize your Business with IoT? Typical IoT Challenges HiveMQ and MongoDB Solution Fleet Management Demo What you will learn today Q&A
  • 3. Or: Why should you care? How to optimize your Business with IoT?
  • 4. Digital Services are the Key Differentiator in Global Competition Intensifying Competition ... … Eroding Margins ... … leading to the need to rethink current business models 8 5 41 24 18 28 53 24 Asia-Pacific Americas Europe Rest of World 1998 2019 General industrial machinery Already commoditized Future price premium at stake Special purpose machinery Risk of hardware commoditization McKinsey: IIoT platforms: The technology stack as value driver in industrial equipment and machinery Services Software Machinery 1998 2019 Production of machinery by region (%) Indicative Value Pools
  • 5. Digital Services are the Key Differentiator in Global Competition Intensifying Competition ... … Eroding Margins ... … leading to the need to rethink current business models
  • 6. Leaders gain >15% positive impact on cost and revenue via IoT McKinsey: Best Practices Separating IoT Leaders from Laggards
  • 7. Develop new IoT Products and Services e.g. apps, fleet management Business Strategies Drive Sales and Service Efficiency e.g. monitoring, field services, maintenance, staffing Optimize Business Operations e.g. manufacturing, supply chain, R&D
  • 8. Devices with Sensors and Actuators e.g. cars, buildings, equipment, wearables Device Enablement Platforms e.g. obtaining, importing, and processing data using standard protocols IT Initiatives Business Applications e.g. customer- and/or device-facing functionality, dashboards, mobile apps Cloud and Edge Computing e.g. for new workloads and cost optimizations
  • 9. A Digital Twin can represent almost everything: Machines, Processes, Places, Buildings, Cities, even Humans like You and Me.
  • 10. Global leaders leverage IoT powered by MongoDB Source of Industries: IHS Markit
  • 11. What is the core of IoT & Digital Twins? R&D Production Operation Maintenance Decommissioning Describe Predict Recommend Combines all data across the whole lifecycle of a product Outlives the physical product in order to optimize business processes and unlock new revenue streams Product Life-Cycle Information Life-Cycle
  • 12. Only ~30% of relevant IoT solutions are in company-wide roll-out. Delivering IoT at scale requires the ability to extract, interpret, and harmonize data from disparate systems that were not designed to work together. McKinsey: Best Practices Separating IoT Leaders from Laggards
  • 13. Or: Why does not everyone benefit? Typical IoT Challenges
  • 16. Web Technology used today is built for the Internet of Humans, NOT for the Internet of Things
  • 17. Many Different Types of Data ● Device data arrives in different formats (JSON, AVRO, Protobuf, custom binary formats) ● Often in time series data ● Data agnostic message brokers used to distribute data into the backend ● Relational databases are sometimes not well suited for IoT data
  • 18. Responsiveness of Systems ● Low latency is critical for many IoT use cases ● End users expect responsive IoT applications ● Unreliable cellular networks can have a significant impact on responsiveness BMW Case Study: bit.ly/bmw-casestudy
  • 19. Scalability ● IoT solutions need to scale to accommodate growth (100s - 1,000,000s of devices) ● Scale-up and scale-down to accommodate spikes
  • 20. MQTT Broker Cluster IoT Devices Extension Extension Extension Enterprise Integration ● IoT data needs to be integrated into enterprise systems ● Device to Cloud and Cloud to Device data integration
  • 21. On-premise Self hosted Managed Service Deployment Agnostic Atlas
  • 22. Many different types of data Top IoT Challenges Responsiveness of systems Enterprise integration Deployment agnostic Scalability to thousands & millions of devices
  • 23. Or: How to tackle the typical challenges? HiveMQ & MongoDB Solution
  • 24. IoT Simplified: Same Pattern for any Application Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Data Storage Hot & Cold Data for Real-Time & Batch Access Dashboards Visual Insights Applications User-Facing Applications & Automations Advanced Analytics & Machine Learning Gaining Insights into Data, Predict & Act
  • 25. HiveMQ’s MQTT Broker for Communication Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Data Storage Hot & Cold Data for Real-Time & Batch Access Dashboards Visual Insights Applications User-Facing Applications & Automations Advanced Analytics & Machine Learning Gaining Insights into Data, Predict & Act
  • 26. Publish/subscribe based architecture Easy (I)IoT Messaging Protocol Minimal Overhead Designed for reliable communication over unreliable channels Binary Data agnostic What is MQTT?
  • 28. Logistics Tele- communication IoT Messaging Middleware MQTT Use Cases Connected Car IIoT / Industry 4.0
  • 30. Building new digital products Improving customer experience Creating more efficient operations and insights Avoiding data loss with efficient system Our Customers …and more ...and more
  • 31. Challenge: Responsiveness of Systems & Deployment Agnostic Challenge: Scalability to thousands & millions of devicesChallenge: Scalability to thousands & millions of devices Challenge: Enterprise Integration Tackling IoT Challenges World-class scalable MQTT ● Masterless architecture, Auto healing, elastic scaling ● Private or Public deployments ● K8s, OpenShift, AWS, Azure, GCP ● Ideal for multi-cloud Reliable Cloud Native Architecture ● Visibility for operations team ● Live debug of individual clients ● Trace recording for playback on message sequences Real-time Monitoring Across Device Fleets ● Data integration with existing enterprise systems ● Integration with other MQTT clients and broker ● Off-the-shelf integrations and custom extensions Extension Framework and Marketplace ● Scales to 10 million connections and more ● MQTT 5 fully supported ● Full hybrid support of 3.1.1 and 3.1
  • 32. HiveMQ & MongoDB on-premises IoT Devices Extension Extension Extension
  • 33. HiveMQ Cloud & MongoDB Atlas Extension Extension Extension IoT Devices Atlas
  • 34. MongoDB’s Data Platform for IoT Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Data Storage Hot & Cold Data for Real-Time & Batch Access Dashboards Visual Insights Applications User-Facing Applications & Automations Advanced Analytics & Machine Learning Gaining Insights into Data, Predict & Act
  • 35. MongoDB Atlas: End-2-End Data Platform for IoT Primary Secondary Secondary High Volume Real Time Operational Data Analytical Analytical Real Time Analytical Data Data Lake, offline, queryable archive Lucene based Text Search Sharding and Replica Sets Multi-Cloud Data Platform OneQueryLanguage,API,andSQL Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Mobile Database Edge to Cloud Sync Native Visualizations Applications & Microservices Advanced Analytics Triggers & Events Reporting
  • 36. MongoDB Atlas: End-2-End Data Platform for IoT Primary Secondary Secondary High Volume Real Time Operational Data Analytical Analytical Real Time Analytical Data Data Lake, offline, queryable archive Lucene based Text Search Sharding and Replica Sets Multi-Cloud Data Platform OneQueryLanguage,API,andSQL Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Mobile Database Edge to Cloud Sync Native Visualizations Applications & Microservices Advanced Analytics Triggers & Events Reporting
  • 37. MongoDB Atlas: End-2-End Data Platform for IoT Primary Secondary Secondary High Volume Real Time Operational Data Analytical Analytical Real Time Analytical Data Data Lake, offline, queryable archive Lucene based Text Search Sharding and Replica Sets Multi-Cloud Data Platform OneQueryLanguage,API,andSQL Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Mobile Database Edge to Cloud Sync Native Visualizations Applications & Microservices Advanced Analytics Triggers & Events Reporting
  • 38. MongoDB Atlas: End-2-End Data Platform for IoT Primary Secondary Secondary High Volume Real Time Operational Data Analytical Analytical Real Time Analytical Data Data Lake, offline, queryable archive Lucene based Text Search Sharding and Replica Sets Multi-Cloud Data Platform OneQueryLanguage,API,andSQL Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Mobile Database Edge to Cloud Sync Native Visualizations Applications & Microservices Advanced Analytics Triggers & Events Reporting
  • 39. MongoDB Atlas: End-2-End Data Platform for IoT Primary Secondary Secondary High Volume Real Time Operational Data Analytical Analytical Real Time Analytical Data Data Lake, offline, queryable archive Lucene based Text Search Sharding and Replica Sets Multi-Cloud Data Platform OneQueryLanguage,API,andSQL Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Mobile Database Edge to Cloud Sync Native Visualizations Applications & Microservices Advanced Analytics Triggers & Events Reporting
  • 40. MongoDB Atlas: End-2-End Data Platform for IoT Primary Secondary Secondary High Volume Real Time Operational Data Analytical Analytical Real Time Analytical Data Data Lake, offline, queryable archive Lucene based Text Search Sharding and Replica Sets Multi-Cloud Data Platform OneQueryLanguage,API,andSQL Sensors & Actuators Wireless communication over industry standard protocols Edge Gateway Offline Storage, Local Processing Streaming & Routing Standard Protocols and Tools, e.g. MQTT, Kafka Mobile Database Edge to Cloud Sync Native Visualizations Applications & Microservices Advanced Analytics Triggers & Events Reporting
  • 41. Benefits by Using One Single Data Platform Fast Time-to-Market One database query language for all platforms, incl. the data lake One codebase independent of deployment strategy, Optimization done once Transfer of resources between different teams Lower TCO Training (dev & ops) to be done once Efficiency of scale for operational costs (one team vs. platform-dedicated teams) Time-to-Value for new features is the same on all platforms One agreement across all platforms, same support team High Security Secure by default on different cloud providers Reusable security concept on multiple platforms Encryption (At Rest, In Use, In Flight), Authentication/Authorization, Auditing Low Operational Effort Same operations tooling and APIs independent of platform Same scaling approach and reusable integrations across all platforms Technical Support by the same Service Engineers independent of platform
  • 42. Modelling a Basic “Thing” Unique Identifier Title, Description Creation, Modification Date Base Thing Relational Schema
  • 43. Enriching a “Thing” Requires more Tables Unique Identifier Title, Description Creation, Modification Date Base Thing Properties & Schema Actions & Input / Output Events & Schema / Subscription / Cancellation Relational Schema
  • 44. Further Enrichment Creates Complex Schemas Unique Identifier Title, Description Creation, Modification Date Base Thing Properties & Schema Actions & Input / Output Events & Schema / Subscription / Cancellation Translations Security Schemes Form Representation User-Defined Data ? Relational Schema
  • 45. Strict Schema vs. Flexible Data Model id title description 12345-WoTLamp-1234 My Lamp A lamp in the room 67890-WoTLamp-1234 My Other Lamp Another lamp in the room id thing_id title readOnly writeOnly 4711-p1 12345-WoTLamp-1234 status false false id thing_id title safe idempodent 4711-a1 12345-WoTLamp-1234 toggle false false id thing_id title readOnly writeOnly 4711-e1 12345-WoTLamp-1234 overheating false false Thing Property Action Event { "id": "123456-WoTLamp-1234", "title": "My Lamp", "description": "A lamp in the room", "properties": { "status": { "type": "string", "readOnly" : false, "writeOnly" : false } }, "actions": { "toggle": { "safe": false, "idempodent": false } }, "events": { "overheating": { "data": { "type": "string", "readOnly" : false, "writeOnly" : false } } } } Relational Example Flexible Document Model
  • 46. Extensible at Runtime { "id": "123456-WoTLamp-1234", "title": "My Lamp", "description": "A lamp in the room", + "properties": { ... }, "actions": { "toggle": { "safe": false, "idempotent": false } }, + "events": { ... } } { "id": "123456-WoTLamp-1234", "title": "My Lamp", "description": "A lamp in the room", "securityDefinitions": { "basic_sc": { "scheme": "basic", "in": "header" } }, + "properties": { ... }, "actions": { "toggle": { "safe": false, "idempodent": false, "forms": [{ "op": "invokeaction", "href": "https://mylamp.example.com/toggle", "contentType": "application/json" }] } }, + "events": { ... } } Adding Security and Action Information Schema-Free based on well-defined standards like JSON-LD and W3C’s Web of Things Vocabulary
  • 47. What about Timeseries? Schema Design Pattern: Bucketing https://www.mongodb.com/collateral/time-series-best-practices
  • 48. Telediagnostics: The Future of Mercedes Benz Services Learn more about the project in Madalin Broscaru’s MongoDB.live presentation: Telediagnostics@Mercedes Benz powered by MongoDB … the Vehicle Data Conditioning (VDC) where these technical vehicle events are processed … CAC Retail Customer … and the follow-up processes are triggered with real-time recommendations for actions. Vehicles are transmitting regularly status and health data into ... Aggregated Quality Analysis
  • 49. Telediagnostics: The Future of Mercedes Benz Services { ............. "schema": "3.1.0" "createdAt": {..}, ............. "vehicleIdentData": { "chassisNumber": "WDD24708A5432J63", "countryCode": "4f3490b14e238a5f", "modelSeries": "f16ad22d42064811", "modelType": "2196868af1c70d74", "modelYear": "5e01ac15d73c3e4a", "steering": "4a3424fe6411461c" }, "basicData": { "mileage": {..}, "batteries": [..], "tanks": [..], "tiresPressure": [..], }, "controlUnits": [..], "affectedFunctions": [..], "vehicleClusterMessagesData": [..], "maintenanceData": {...}, .............. } "controlUnits": [ { "ecuId": {..} "name": "a927e49b0549f00f71", "detailsHardware": {}, "detailsSoftware": {}, "dtc": [ { "code": "B214F73", ... "failureText": "81957650ea", "environmentalData": {...} }, ... ] }, ... ]
  • 50. Or: How does a solution look like? Show me the architecture and the code! Fleet Management Demo
  • 51. IoT is broad - let’s look into a specific example Source of Industries: IHS Markit Fleet Management Covers fleets of commercial vehicles, fork lifts, trains, goods, literally anything that forms a fleet and needs to be monitored and actively managed. Major Challenges High distribution of fleet, unstable network connections, multitude of device types and data structures, scaling from 100’s to 1,000’s to 1,000,000s Typical Benefits Reduction of shipping costs, goods arrive in time, less outages and damages, reaction times in minutes instead of hours, CO2 reporting and reduction, higher fleet utilization, compliance with legal requirements
  • 52. Getting Started with MongoDB & HiveMQ MongoDB Atlas Free Tier Cluster Retention ½ Day HiveMQ vehicles/trucks/truck-XXXXX MQTT Subscriber Python Trucks on tour sending location, speed, break time MQTT Subscriber Python Truck Simulator: - >9000 Warehouses across Germany - Trucks travel between random warehouses - Every second, the current location, speed, speed limit and break information is sent Real-Time Position of Trucks: - Subscription to MQTT topics for live visualization Analysis of Trucks: - Visualization of historical data based on truck routes
  • 53. Getting Started with MongoDB & HiveMQ MongoDB Atlas Free Tier Cluster Retention ½ Day HiveMQ vehicles/trucks/truck-XXXXX MQTT Subscriber Python Trucks on tour sending location, speed, break time MQTT Subscriber Python Truck Simulator: - >9000 Warehouses across Germany - Trucks travel between random warehouses - Every second, the current location, speed, speed limit and break information is sent Real-Time Position of Trucks: - Subscription to MQTT topics for live visualization Analysis of Trucks: - Visualization of historical data based on truck routes
  • 54. Or: What about X, Y and Z? Q&A
  • 55. Get Started Today! MongoDB Atlas https://cloud.mongodb.com HiveMQ https://hivemq.com/ We love to hear your feedback! Dr. Christian Kurze | Principal Solutions Architect | christian.kurze@mongodb.com Dominik Obermaier | CTO and Co-Founder | dominik.obermaier@hivemq.com