The document outlines Microsoft's cloud solutions for IoT, emphasizing the integration of various Azure services like IoT Hub, Data Lake, Stream Analytics, and Cosmos DB for data ingestion, processing, and analysis. It highlights the potential business benefits of IoT implementation, such as operational improvements and revenue growth, along with technical features like bi-directional communication and device management. Additionally, it covers capabilities such as predictive maintenance, real-time analytics, and secure device provisioning, showcasing a comprehensive architecture for intelligent applications.

1. Microsoft Cloud
Workshop

2. INGEST STORE PREP & TRAIN MODEL & SERVE
R E F E R E N C E A R C H I T E C T U R E S :
I N T E L L I G E N T A P P L I C A T I O N S
Azure Blob Storage
Business/custom apps
(Structured)
Logs, files and media
(unstructured)
Azure SQL Data
Warehouse
Azure Analysis
Services
Azure Data Factory
Sensors and IoT
(unstructured)
Azure HDInsight (Kafka)
Azure IoT Hub
Cosmos DB
Predictive apps
Operational Reports
&
Analytical Dashboards
(PowerBI)
SQL Database
SQL
Polybase
Azure Databricks /
HDInsight
https://azure.microsoft.com/en-au/solutions/architecture/
Azure Stream
Analytics

3. New monetization avenues
due to IoT-related services
Companies that increased
revenue as a result of IoT
implementation
Average increase in
operating income (avg. 8%)
among the most digitally
transformed enterprises
p
p
p
Data +
intelligence
Optimize
operations
Transform
products
Engage
customers
Empower
employees
Connected “things” by 2025
generating 180ZB of data
p

4. Azure Data Lake
Azure Time Series
Insights
Azure Maps
Azure Stream Analytics
Azure Machine Learning
Azure HD Insight
Spark, Storm, Kafka
Azure Event Hubs
Azure IoT Hub Device
Provisioning Service
Azure IoT Hub
Windows 10 IoT Core
Azure IoT Edge
Azure Sphere
Azure Certified for IoT
Azure IoT Device SDK
Azure Logic Apps
Azure Websites
Azure Monitor
Azure Event Grid
Microsoft Power BI
Microsoft Flow
Azure Functions
PlatformUse|Solutions
IoT Edge

5. Azure IoT Central
Analytics, dashboards and visualization
User roles and permissions
Monitoring rules and triggered actions
Fully hosted and managed by Microsoft
Device connectivity and management
Risk-free trial with simplified pricing
No cloud development expertise required

6. Azure IoT solution
accelerators
Predictive Maintenance
Connected FactoryRemote Monitoring
Device Simulation
End-to-end implementation
Completely customizable
Open-source microservices based architecture
Device connectivity and management
Dashboards, visualization, and insights
Workflow automation and integration
Command and control
Preconfigured solutions

7. Core Subsystems
Things Insights Actions
Provision and
send data from
device to cloud
Device
Management
Stream processing and
rules evaluation over data
Store data Integrate with business processes
Visualize data and learnings
aka.ms/iotrefarch

8. © Microsoft Corporation
Serverless Azure service
Billions of devices and assets
Supports Linux, iOS, Android, Linux, Windows, and
real-time operating system (RTOS) devices
Supports reliable bi-directional communication—
device-to-cloud and cloud-to-device
Ingests data in real-time
Can manage IoT devices at scale with device
management
Extends the power of the cloud to edge devices
with Azure IoT Edge
Azure IoT Hub
A fully-managed service to connect, monitor, and manage billions of IoT assets

9. © Microsoft Corporation
Per device security - IoT Hub lets you set up individual identities and credentials for your connected devices. This helps maintain
the confidentiality of cloud-to-device and device-to-cloud authentication. You can also selectively revoke the access rights to
specific devices as needed.
Device monitoring and management - Administrators can remotely maintain, update, and manage IoT devices at scale from the
cloud. Service personnel will rarely have to travel to the asset location. It also monitors device connectivity.
Provisioning - With IoT Hub Device Provisioning Service you can register and provision devices with zero-touch in a secure and
scalable way. Devices can be provisioned via the Azure portal.
Bi-directional communication - IoT Hub lets you establish bi-directional communication between Azure and IoT devices. Use
device-to-cloud telemetry data to understand the state of your devices and assets. In cloud-to-device messages, reliably send
commands and notifications to your connected devices—and track message delivery with acknowledgement receipts. Device
messages are sent in a durable way to accommodate intermittently connected devices.
Device twins - Using device twins, you can store, synchronize, and query device metadata and state information. Device twins
are JSON documents that store device state information like metadata, configurations, and conditions. IoT Hub maintains a
device twin for each device that you connect to IoT Hub.
Route messages - IoT Hub enables you to define message routes based on routing rules to control where your hub sends
device-to-cloud messages. Routing rules don’t require you to write any code, and can take the place of custom post-ingestion
message dispatchers.
Azure IoT Hub capability overview

10. © Microsoft Corporation
Full list of all the IoT Hub endpoints: https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-devguide-endpoints
Azure IoT Hub endpoints
Azure IoT Hub exposes several endpoints for different purposes
Some important endpoints

11. IoT Hub
Device id
Send/receive data via IoT Hub
C2D queue
endpoint
D2C send
endpoint D2C receive
endpoint
C2D send and
feedback endpoints
Event processing
(hot path)
Device
Client SDKs
• C
• Java
• C# (.Net
Standard 1.3)
• Python
Device management, device business logic,
Connectivity monitoring
Client SDKs
• .Net
• Node
• Java
• Python

12. IoT Hub
Device Twin
IoT Hub device management
Device
Device management, device business logic,
Connectivity monitoring
Properties
Tags
Desired
Reported
Properties
Desired
Reported
Method

13. • Simple "plug and play” provisioning
• Minimize manual connection
requirements
• Enhanced security through HSM
• Global availability
IoT Solution US IoT Solution ChinaIoT Solution Germany

14. Azure IoT Edge
Compatible with popular operating systems
Code symmetry between cloud and edge
for easy development and testing
Secure solution from chipset to cloud
Move cloud and custom workloads
to the edge, securely
Seamless deployment of AI and
advanced analytics
Configure, update and monitor
from the cloud

16. Azure Time Series Insights
IoT scale time-series data store
Easy IoT Hub connection
Store, query, and visualize billions of events
Get near real-time insights in seconds
Schema-less store, just send data
Build apps using Time Series Insights APIs

18. Azure Maps
Render maps and satellite imagery across many
geographies
Integrate rich mapping visualizations into applications
Calculate routes from N to N points for optimal
calculations
Convert places and addresses to coordinates; or,
convert coordinates to addresses or cross streets
Show real time traffic information
Obtain time zone and current time information

19. Azure Sphere

20. Control
Measure
Insight,
optimization
Quantified Organization
Assistance,
Task completion
Intent,
Context
Artificial Intelligence
Measure
Insight,
optimization
Quantified SelfDIGITAL
WORLD
Data
ControlData
ControlData
VI
PEOPLE
SPACES
PHYSICAL
WORLD

21. © Microsoft Corporation
Canonical operations
Streaming
Connect, collect, and store
Ingest
Process and analyze
Analytics
Connect, collect, and store
Actions
A
B
C

22. © Microsoft Corporation
 Input Capacity: 1 MB/s per TU*
 Output Capacity: 2 MB/s per TU*
 Latency: 50 ms avg, 99% < 100ms
 Events/second: 1,000
 Max message size: 256 KB
*In Azure Event Hubs, capacity is purchased in throughput units (TU). Add TUs to increase capacity.
Event
publisher
Partition
Partition
Partition
Reader
Reader
Reader
Event
Consumer
Event hubs
Azure Event Hubs:
Scale and performance
Azure Event Hubs
A highly scalable, fully-managed telemetry ingestion service

23. Stream
Analytics on
IoT Edge
Presentation &
Action
Storage &
Batch Analysis
Stream
Analytics
Event Queuing
& Stream
Ingestion
Event
production
IoT Hub
Applications
Archiving for long
term storage/
batch analytics
Real-time dashboard
Azure
Stream
Analytics
Automation to
kick-off workflows
Machine Learning
Reference Data
Event Hubs
Blobs
Devices &
Gateways

24. © Microsoft Corporation
Azure Blob Storage
Azure IoT Hub
Azure Event Hubs
Reference data
Streaming data
Streaming data
Azure Stream
Analytics
Integration with Azure Event Hubs and IoT Hub
Azure Stream Analytics has built-in, first class integration with Azure Event Hubs and IoT Hub
Data from Azure Event Hubs and Azure IoT Hub
can be sources of streaming data to Azure Stream Analytics
The connections can be established through the Azure Portal without any coding
Azure Blob Storage is supported as a source of reference data
Azure Stream Analytics supports compression across all data
stream input sources—Event Hubs, IoT Hub, and Blob Storage

25. Better
Data

26. Better
Data

27. Optimized Databricks Runtime Engine
DATABRICKS I/O SERVERLESS
Collaborative Workspace
Cloud storage
Data warehouses
Hadoop storage
IoT / streaming data
Rest APIs
Machine learning models
BI tools
Data exports
Data warehouses
Azure Databricks
Enhance Productivity
Deploy Production Jobs & Workflows
APACHE SPARK
MULTI-STAGE PIPELINES
DATA ENGINEER
JOB SCHEDULER NOTIFICATION & LOGS
DATA SCIENTIST BUSINESS ANALYST
Build on secure & trusted cloud Scale without limits
Azure Databricks

28. © Microsoft Corporation
For archiving for compliance (raw data)
For batch analysis using Big Data analytics
To train machine learning models Event Hubs IoT Hub Kafka on HDInsight
Stream
Analytics
Storm on
HDInsight
Azure Databricks
(Spark Streaming)
Stream ingestion
Long-term storage
Stream analytics
Raw data Processed data
Why store streaming data?

29. Azure Storage
Object Storage
Data Transport
File storage
Hybrid Storage
Lift and shift of legacy
applications that require file
shares to the cloud
Secure, centralized storage
target for backup/disaster
recovery
Move or migrate data into
Azure
Secure, intelligent data tiering
between on-premises and cloud
storage
Azure Blobs Azure Files
Azure NetApp Files
Azure Import/Export
Azure DataBox
Azure StorSimple
Azure File Sync
Avere
Disk Storage
Reliable, persistent, high
performing storage for Virtual
Machines
Premium
Standard

30. Blob Level
Tiering
Blob-Level Tiering
Individual blobs can move
between tiers
All tiers co-exist in the same
storage account
New API to set blob tier:
SetBlobTier
Acknowledged immediately from
service
Get APIs (GetBlobProperties
and ListBlobs) return current tier
and archive status
New headers "x-ms-access-tier”
and “x-ms-archive-status”
Preview @ Build, GA CY18:
Automated Lifecycle
Management
Blob REST API
Hot Tier
Lower Transaction cost
Cool Tier
Lower Capacity cost
Archive Tier
Lowest Capacity cost

31. © Microsoft Corporation
No limits on number of files, size of individual files, total
amount of data stored, how long data can be stored, or
ingestion throughput
Low latency and high throughput workloads can be used
for ingesting streaming data
Stores all data types
Is Hadoop-compatible via WebHDFS REST API.
Supported by leading Hadoop distros and HDInsight
Provides POSIX-style permissions for RBAC
Integrates with Azure AD for authentication
Azure Data Lake store
A highly scalable, parallel, file system in the cloud that’s specifically optimized for Big Data analytics
Backend storage in Azure
Data node Data node Data node Data node Data nodeData node
Sh
ard
Sh
ardBlock
Block 1 Block 2
Block
n…
Azure Data Lake Store file
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ard
Sh
ardBlock
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ard
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ardBlock
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ard
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ardBlock
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ardBlock
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ardBlock

32. Azure Cosmos DB
SQL
MongoDB
Table API
Turnkey global
distribution
Elastic scale out
of storage & throughput
Guaranteed low latency
at the 99th percentile
Comprehensive
SLAs
Five well-defined
consistency models
DocumentColumn-family
Key-value Graph
A globally distributed, massively scalable, multi-model database service

33. Any data, any way, anywhere

34. © Microsoft Corporation
Big Data streaming pattern with Azure
Real-time applications
Real-time dashboards
Sensors and IoT
(unstructured)
Event hubs IoT hub Kafka on HDInsight Azure Stream
Analytics
Storm on
HDInsight
Azure Databricks
(Spark Streaming)
Azure ML
Studio
R Server Azure Databricks
(Spark ML)
Machine learning
Stream ingestion
Long-term storage
Stream analytics
Data Lake Store SQL DB Cosmos DB Azure Blob Storage
Business/custom apps
(structured)
Logs, files, and media
(unstructured)
Power BI

35. Azure IoT School
aka.ms/iotschool