Azure is Big and fast growing … C

lien

t la

yer

(on

-pre

mis

es)

Tablet Phone

Games

consolePC

On-premises

databaseBrowserOffice Add-in

On-premises

service

AD

Multifactor

Authentication

Access

Co

ntr

ol

Layer

Inte

gra

tio

n

layer

Service Bus CDN

BizTalk

Services

Traffic

Manager

Virtual

NetworksExpress

Route

Ap

pli

cati

on

layer

API Mgmt Websites

Cloud

Services VM

Mobile

Services

Media

Services

Notification

Hubs Scheduler Automation

Data

Layer

Storage Blobs Tables Queues Data

Machine

Learning HD Insight

Backup and

RecoverySQL

Database Caching StorSimple

♥

Devices Device Connectivity Storage Analytics Presentation & Action

Event Hubs SQL DatabaseMachine

LearningApp Service

Service BusTable/Blob

Storage

Stream

AnalyticsPower BI

External Data

SourcesDocumentDB HDInsight

Notification

Hubs

External Data

SourcesData Factory Mobile Services

BizTalk Services

{ }

Relay

Queue

Topic

Notification Hub

Event Hub

Event Producers

Event Producers

Intake millions of events per secondProcess data from connected devices/apps

Integrated with highly-scalable publish-subscriber ingestor

Easy processing on continuous streams of data Transform, augment, correlate, temporal operations

Detect patterns and anomalies in streaming data

Correlate streaming with reference data

End-to-End Architecture Overview

Data Source Collect Process ConsumeDeliver

Event Inputs- Event Hub

- Azure Blob

Transform- Temporal joins

- Filter

- Aggregates

- Projections

- Windows

- Etc.

Enrich

Correlate

Outputs- SQL Azure

- Azure Blobs

- Event Hub

Azure

Storage

• Temporal Semantics

• Guaranteed delivery

• Guaranteed up time

Azure Stream Analytics

Reference Data- Azure Blob

SELECT count(*), Topic FROM TweetsGROUP BY Topic, TumblingWindow(second, 5)

No code compilation, easy to author and deploy

Brings together event streams, reference data and machine learning extensions

All operators respect, and some use, the temporal properties of events

These should (mostly) look familiar if you know relational databases

Filters, projections, joins, windowed (temporal) aggregates, text and date manipulation

Our toll station has multiple toll booths, where a sensor placed on top of the booth scans an RFID card affixed to the windshield of the vehicles as they pass the toll booth.

The passage of vehicles through these toll stations can be modelled as event streams over which interesting operations can be performed.

Toll Id

EntryTime LicensePlate State Make ModelVehicle Type

Vehicle Weight

Toll Tag

12014-09-10 12:01:00.000

JNB 7001 NY Honda CRV 1 1535 7

22014-09-10 12:02:00.000

YXZ 1001 NY Toyota Camry 1 1399 4 123456789

…

Toll Id ExitTime LicensePlate

1 2014-09-10T12:03:00.0000000Z JNB 7001

2 2014-09-10T12:03:00.0000000Z YXZ 1001

…

Projections

1, 1450, “VW”,

“Golf”, (…)

2, 1230, “Toyota”,

“Camry”, (…)

1, 2400, “VW”,

“Passat”, (…)1, 980, “Ford”,

“Fiesta”, (…)

SELECT TollId, VehicleWeight / 1000 AS Tons FROM EntryStream

1, 1.45 2, 1.23 1, 2.40 1, 0.980

Show me the Toll Id and Vehicle Weight in Tons for all vehicles passing through the Toll Booth

Filters

SELECT Model FROM EntryStream WHERE Make = "VW"

1, 1450, “VW”,

“Golf”, (…)

2, 1230, “Toyota”,

“Camry”, (…)

1, 2400, “VW”,

“Passat”, (…)1, 980, “Ford”,

“Fiesta”, (…)

“Golf” “Passat”

Show me the Model of vehicles manufactured by Volkswagen

Tumbling Windows

SELECT TollId, COUNT(*) FROM EntryStreamGROUP BY TollId, TumblingWindow(minute,5)

How many vehicles entered each toll both every 5 minutes?

Aggregate functions

Scalar functions

Date and time:

String:

Types

Type Description

bigint Integers in the range -2^63 (-9,223,372,036,854,775,808) to 2^63-1 (9,223,372,036,854,775,807).

float Floating point numbers in the range - 1.79E+308 to -2.23E-308, 0, and 2.23E-308 to 1.79E+308.

nvarchar(max) Text values, comprised of Unicode characters. Note: A value other than max is not supported.

datetime Defines a date that is combined with a time of day with fractional seconds that is based on a 24-hour clock and relative to

UTC (time zone offset 0).

Windows 10 IoT Editions

+Microsoft

Azure IoTSecurity &

Identity

Windows

Updates

Visual Studio &

UWP

Windows 10 IoT for industry devicesDesktop Shell, Win32 apps, Universal apps and drivers

Minimum: 1 GB RAM, 16 GB storage

X86/x64

Windows 10 IoT for mobile devicesModern Shell, Mobile apps, Universal apps and drivers

Minimum: 512 MB RAM, 4 GB storage

ARM

Windows 10 IoT CoreUniversal Apps and Drivers

No shell or MS apps

Minimum: 256MB RAM, 2GB storage

X86/x64 or ARM Integrated

Device

Connectivity

New User

Interfaces