{
      "apiVersion": "2016-03-01",
      "name": "[variables('streamAnalyticsJobName')]",
      "location": "[resourceGroup().location]",
      "type": "Microsoft.StreamAnalytics/StreamingJobs",
      "dependsOn": [
        "[concat('Microsoft.Devices/IotHubs/', parameters('iotHubName'))]"
      ],
      .........

But it's not all sad news :)

1. Starting the job. Once the input, query and output settings have been specified you'll have to manually start the job in the Azure Portal UI or by calling the REST API.
2. Fully configuring the PowerBI OAuth credentials. My output is configured as a PowerBI (PBI) Streaming Dataset. In order to connect and send data the Stream Analytics jobs needs the correct permissions - specifically it needs an OAuth token. You can't specify this in the template because it's generated on the fly by AzureAD. You will need to deploy the template which creates the job and output which thinks it's connected to PBI using the user principal name you supply. Then you will need to via the Azure Portal go in to the output definition, and click on "Re-authorize". This makes the AzurePortal talk to AzureAD and obtain an OAuth token (which is then stored inside the job definition). It will be used whenever the job output needs to send data to PBI.

How did I work out the template when it's not documented?

How do I connect to input X or send to output Y?

Show me your fully working example.

1. Read messages from an IoT Hub. Messages are JSON in UTF-8.
2. Perform a simple query. Err, it just copies the input to the output. You can add your own query in place of mine.
3. Output to a PowerBI Streaming Dataset. You specify in the template output the name of the dataset and table. You will see these appear automatically in the PBI Portal once the first message has been sent there (i.e. they don't get created when you create your Stream Analytics job output and configure the authentication). This caught me out a few times.

1. iotHubName: The name of the IoTHub you want to create. Must be unique across the whole of Azure.
2. userDisplayName: A human readable name to associate the OAuth token for connecting to the PBI Streaming Dataset. It doesn't seem to really matter what you enter here but something is required.
3. userLoginName: The username in user@domain.com format that you want to use for authenticating against PowerBI.
4. streamAnalyticsJobName: The name of the main job which will be created.

{
  "$schema": "https://schema.management.azure.com/schemas/2015-01-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "iotHubName": {
      "type": "string",
      "minLength": 3,
      "defaultValue": "wibblehub66",
      "metadata": {
        "description": "Name of the IoT Hub instance to provision."
      }
    },
    "userDisplayName": {
      "type": "string",
      "minLength": 3,
      "defaultValue": "Billy Bunter",
      "metadata": {
        "description": "Your name for connecting Stream Analytics to PowerBI."
      }
    },
    "userLoginName": {
      "type": "string",
      "minLength": 3,
      "defaultValue": "billybunter@microsoft.com",
      "metadata": {
        "description": "Your loginname/alias for connecting Stream Analytics to PowerBI."
      }
    }
  },
  "variables": {
    "streamAnalyticsJobName": "MyAceJobName"
  },
  "resources": [
    {
      "apiVersion": "2016-02-03",
      "location": "[resourceGroup().location]",
      "name": "[parameters('iotHubName')]",
      "type": "Microsoft.Devices/IotHubs",
      "properties": {
        "eventHubEndpoints": {
          "events": {
            "retentionTimeInDays": 1,
            "partitionCount": 2
          }
        },
        "cloudToDevice": {
          "defaultTtlAsIso8601": "PT1H",
          "maxDeliveryCount": 10,
          "feedback": {
            "maxDeliveryCount": 10,
            "ttlAsIso8601": "PT1H",
            "lockDurationAsIso8601": "PT60S"
          }
        },
        "location": "[resourceGroup().location]"
      },
      "sku": {
        "name": "S1",
        "capacity": 2
      }
    },
    {
      "apiVersion": "2016-03-01",
      "name": "[variables('streamAnalyticsJobName')]",
      "location": "[resourceGroup().location]",
      "type": "Microsoft.StreamAnalytics/StreamingJobs",
      "dependsOn": [
        "[concat('Microsoft.Devices/IotHubs/', parameters('iotHubName'))]"
      ],
      "properties": {
        "sku": {
          "name": "standard"
        },
        "outputErrorPolicy": "stop",
        "eventsOutOfOrderPolicy": "adjust",
        "eventsOutOfOrderMaxDelayInSeconds": 0,
        "eventsLateArrivalMaxDelayInSeconds": 5,
        "dataLocale": "en-US",
        "inputs": [
          {
            "Name": "IoTHub",
            "Properties": {
              "DataSource": {
                "Properties": {
                  "iotHubNamespace": "[parameters('iothubname')]",
                  "sharedAccessPolicyKey": "[listkeys(resourceId('Microsoft.Devices/IotHubs/IotHubKeys',parameters('iothubname'), 'iothubowner'),'2016-02-03').primaryKey]",
                  "sharedAccessPolicyName": "iothubowner",
                  "endpoint": "messages/events"
                },
                "Type": "Microsoft.Devices/IotHubs"
              },
              "Serialization": {
                "Properties": {
                  "Encoding": "UTF8"
                },
                "Type": "Json"
              },
              "Type": "Stream"
            }
          }
        ],
        "transformation": {
          "name": "Transformation",
          "properties": {
            "streamingUnits": 1,
            "query": "SELECT\r\n    *\r\nINTO\r\n    [PBI]\r\nFROM\r\n    [IoTHub]"
          }
        },
        "outputs": [
          {
            "name": "PBI",
            "properties": {
              "dataSource": {
                "type": "PowerBI",
                "outputPowerBISource": {
                  "dataSet": "HeartDataSet",
                  "table": "HeartDataTable",
                  "groupId": "",
                  "groupName": "My Workspace",
                  "refreshToken": "dummytoken",
                  "tokenUserDisplayName": "[parameters('userDisplayName')]",
                  "tokenUserPrincipalName": "[parameters('userLoginName')]"
                },
                "properties": {
                  "dataSet": "HeartDataSet",
                  "table": "HeartDataTable",
                  "groupId": "",
                  "groupName": "My Workspace",
                  "refreshToken": "dummytoken",
                  "tokenUserDisplayName": "[parameters('userDisplayName')]",
                  "tokenUserPrincipalName": "[parameters('userLoginName')]"
                }
              },
              "serialization": null,
              "diagnostics": null
            }
          }
        ]
      }
    }
  ],
  "outputs": {
    "IoTHub": {
      "type": "object",
      "value": "[reference(resourceId('Microsoft.Devices/IotHubs',parameters('iotHubName')), '2016-02-03')]"
    },
    "SharedAccessPolicyKey": {
      "value": "[listkeys(resourceId('Microsoft.Devices/IotHubs/IotHubKeys',parameters('iothubname'), 'iothubowner'),'2016-02-03').primaryKey]",
      "type": "string"
    },
    "Hostname": {
      "value": "[reference(resourceId('Microsoft.Devices/IotHubs',parameters('iotHubName')), '2016-02-03').hostname]",
      "type": "string"
    }
  }
}

Step 1 - Prepare a Raspian Linux SD card.

Task:  Install Raspian Linux onto an SD card.

1. Download the latest Raspbian Jessie with desktop Linux image to your local machine. Other download options are available at Download Raspian.
2. Download the Etcher image writing utility and install it.
3. Insert the SD card into your computer.
4. Open Etcher and select from your hard drive the .zip file you downloaded in step 1.
5. Select the SD card you wish to write your image to.
6. Review your selections and click 'Flash!' to begin writing data to the SD card.
7. Once flashing is complete, create a new empty file named ssh (with no extension) in the root of the drive that holds the SD card. This will ensure that the SSH daemon is enabled once the Raspberry Pi has started and you can logon over the network.

Step 2 - Boot the Raspberry Pi and connect over the network.

• HDMI - so you can watch the boot process from a screen.
• Ethernet cable - avoid connecting to corporate networks as firewalls and proxies/proxy authentication problems can waste setup time.
• Keyboard and mouse.

Task: Power up the Raspberry Pi and obtain its IP address.

• Insert the SD card into the base of the Raspberry Pi. Ensure the electric contacts are facing upwards.
• Power-up the RPi by plugging in the micro-USB power supply. Wait until it reaches the desktop.

• Click on the Terminal icon on the top menu to open a prompt and type ifconfig to obtain the IP address which has been assigned by your DHCP server to the Raspberry Pi.

• Download the PuTTY SSH and Telnet client and launch it.
• Enter the IP address of the Raspberry Pi and click Open. Accept the message about keys.

• Enter pi as the logon name, and raspberry as the password.
• Once you have reached the command line, change the default password for the pi user.

Step 3 - Install the .NET Core Runtime.

1. .NET Core Runtime. Needs to be installed on any machine where a .NET Core application will run.
2. .NET Core SDK. Needs to be installed on any machine used to develop .NET Core applications.
3. IDE Tools. Adds-on for your chosen IDE to automate the development process.

Task: Install the .NET Core Runtime on the Raspberry Pi.

• Run sudo apt-get install curl libunwind8 gettext. This will use the apt-get package manager to install three prerequiste packages.
• Run curl -sSL -o dotnet.tar.gz https://dotnetcli.blob.core.windows.net/dotnet/Runtime/release/2.0.0/dotnet-runtime-latest-linux-arm.tar.gz to download the latest .NET Core Runtime for ARM32. This is refereed to as armhf on the Daily Builds page.
• Run sudo mkdir -p /opt/dotnet && sudo tar zxf dotnet.tar.gz -C /opt/dotnet to create a destination folder and extract the downloaded package into it.
• Run sudo ln -s /opt/dotnet/dotnet /usr/local/bin` to set up a symbolic link...a shortcut to you Windows folks ;) to the dotnet executable.
• Test the installation by typing dotnet --help.

• Try to create a new .NET Core project by typing dotnet new console. Note this will prompt you to install the .NET Core SDK however this link won't work for Raspian on ARM32. This is expected behaviour.

• Optional: Follow the steps outlined in the .NET Core on Raspberry Pi guide to create a test console application on your local development machine then run it on the RPi. The key action here is that you will create an application then publish the it using the dotnet publish -r linux-arm command.

The Maker Show: Azure IoT Starter Kits

Adafruit Raspberry Pi 3 Kit

Adafruit Feather M0 Kit (Arduino-compatible)

Adafruit Feather HUZZAH ESP8266 Kit (Arduino-compatible)

Seeed – Intel Edison Kit

SparkFun Thing ESP8266 Dev Kit (Arduino-Compatible)

Seeed – Grove IoT Commercial Gateway Kit

Migrate Azure IaaS Solutions from ASM to ARM Using migAz, Part 1

Migrate Azure IaaS Solutions from ASM to ARM Using migAz, Part 2

1. CreateBaseResources - This will create an Azure Virtual Network and Storage Account. You only need to run this once.
2. CreateOOBImage - This will create a virtual machine (Win2012R2) and add it to the above network and use the storage account for the main OS disk. You'll run this whenever you need to build a new machine and add it to the network, however you'll need to change some basic settings like machine name etc. in the parameters.json file each time you do. One of the more interesting things is that it's configured with a custom script extension which will download a PowerShell script from a location you configure (in the parameters.json file) and run this once the VM has built. This was needed to trigger the post-configuration of the machine.
3. RemoveVM - This will remove (duh) a virtual machine from the network and clean-up the left over .vhd files from the storage account.

1. The input source of the streaming data (perhaps messages being sent from an embedded device or people posting on Twitter).
2. What patterns of data you are interested in. This is done via a SQL-like language.
3. The output location for the results of the analysis.

What can I use Stream Analytics for?

1. Personalized, real-time stock-trading analysis and alerts offered by financial services companies.
2. Real-time fraud detection.
3. Data and identity protection services.
4. Reliable input and analysis of data generated by sensors and actuators embedded in physical objects (Internet of Things, or IoT).
5. Web clickstream analytics.
6. Customer relationship management (CRM) applications issuing alerts when customer experience within a time frame is degraded.

So how do I get started?

https://azure.microsoft.com/en-gb/documentation/learning-paths/stream-analytics/

• Introduction to Azure Stream Analytics with Santosh Balasubramanian
• Handling IoT data with Azure Stream Analytics
• Deep Dive: Azure Stream Analytics Query Language
• https://channel9.msdn.com/Blogs/Windows-Azure/Azure-Stream-Analytics-Overview
• An Introduction to Azure Stream Analytics