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This tutorial is a step-by-step blueprint for a Vehicle Telemetry job analysis on Azure using Streaming Analytics tools for Visual Studio.For connected car and real-time predictive Vehicle Telemetry Analysis, there’s a necessity to specify opportunities for new solutions. These opportunities include  

  • How a car could be shipped globally with the required smart hardware to connect to the internet within the next few years.
  • How the embedded connections could define Vehicle Telemetry predictive health status so automotive companies will be able to collect data on the performance of cars,
  • How to send interactive updates and patches to car’s instrumentation remotely,
  • How to avoid car equipment damage with precautionary measures with prior notification

All these require an intelligent vehicle health telemetry analysis which you can implement using Azure Streaming.

Stream Analytics tools for Visual Studio

The Stream Analytics tools for Visual Studio help prepare, build, and deploy real-time events on Azure. Optionally, the tools enable you to monitor the streaming job using local sample data as job input testing as well as real-time monitoring, job metrics, diagram view, and so on. This tool provides a complete development setup for the implementation and deployment of real-world Azure Stream Analytics jobs using Visual Studio.

Developing a Stream Analytics job using Visual Studio

Post the installation of the Stream Analytics tool, a new stream analytics job can be created in Visual Studio.


  1. You can get started in Visual Studio IDE from File | New Project. Under the Templates, select Stream Analytics and choose Azure Stream Analytics Application.

Azure Streaming Analytics - New project2. Next, the job name, project, and solution location should be provided. Under Solution menu, you may also select options such as Add to solution or Create new instance apart from Create new solution from the available drop-down menu during Visual Studio Stream Analytics job creation:

Solution Explorer3. Once the ASA job is created, in the Solution Explorer, the job topology folder structure could be viewed as Inputs (job input), Outputs (job output), JobConfig.json, Script.asaql (Stream Analytics Query file), Azure Functions (optional), and so on:

4. Next, provide the job topology data input and output event source settings by selecting Input.json and Output.json from Inputs and Outputs directories, respectively.

5. For a Vehicle Telemetry Predictive Analysis demo using an Azure Stream Analytics job, we need to take two different job data streams. One should be a Stream type for an illimitable sequence of real-time events processed through Azure Event Hub along with Hub policy name, policy key, event serialization format, and so on:

Stream Analytics Input configuration

Defining a Stream Analytics query for Vehicle Telemetry job analysis using Stream Analytics tools

To assign the streaming analytics query definition, the Script.asasql file from the ASA project should be selected by specifying the data and reference stream input joining operation along with supplying analyzed output to Blob storage as configured in job properties.

Query to define Vehicle Telemetry (Connected Car) engine health status and pollution index over cities

For connected car and real-time predictive Vehicle Telemetry Analysis, there’s a necessity to specify opportunities for new solutions in terms of how a car could be shipped globally with the required smart hardware to connect to the internet within the next few years. How the embedded connections could define Vehicle Telemetry predictive health status so automotive companies will be able to collect data on the performance of cars, to send interactive updates and patches to car’s instrumentation remotely, and just to avoid car equipment damage with precautionary measures with prior notification through intelligent vehicle health telemetry analysis using Azure Streaming.

The solution architecture of the Co:tUlected Car-Vehicle Telemetry Analysis case study used in this demo, with Azure Stream Analytics for real-time predictive analysis, is as follows:

Predictive vehicle Health telemetry analytics

Testing Stream Analytics queries locally or in the cloud

  1. Azure Stream Analytics tools in Visual Studio offer the flexibility to execute the queries either locally or directly in the cloud. In the Script.asaql file, you need to provide the respective query of your streaming job and test against local input stream/Reference data for query testing before processing in Azure:

Publish to Azure2. To run the Stream Analytics job query locally, first select Add Local Input by right-clicking on the ASA project in VS Solution Explorer, and choose to Add Local Input:

Stream analytics local input configuration3. Define the local input for each Event Hub Data Stream and Blob storage data and execute the job query locally before publishing it in Azure:

Event Hub Data4. After adding each local input test data, you can test the Stream Analytics job query locally in VS editor by clicking on the Run Locally button in the top left corner of VS IDE:

  • Vehicle diagnostic
  • Usage-based insurance
  • Engine emission control
  • Engine performance remapping
  • Eco-driving
  • Roadside assistance call
  • Fleet management

So, specify the following schema during the designing of a connected car streaming job query with Stream Analytics using parameters such as Vehicle Index no, Model, outside temperature, engine speed, fuel meter, tire pressure, and brake status, by defining INNER join with Event Hub data streams along with Blob storage reference streams containing vehicle model information:

Select  input.vin,  BlobSource.Model,  input.timestamp, input.outsideTemperature,
input.engineTemperature,  input.speed,  input.fuel, input.engineoil,
input.tirepressure,  input.odometer,  input.city, input.accelerator_pedal_position, input.parking_brake_status, input.headlamp_status,  input.brake_pedal_status,
input.transmission_gear_position,  input.ignition_status, input.windshield_wiper_status,  input.abs  into  output  from input  join  BlobSource
on  input.vin  =  BlobSource.VIN

The query could be further customized for complex event processing analysis in terms of defining windowing concepts like Tumbling window function, which assigns equal length non-overlapping series of events in streams with a fixed time slice.

The following Vehicle Telemetry analytics query will specify a smart car health index parameter with complex streams from a specified two-second timestamp interval in the form of a fixed length series of events:

select  BlobSource.Model,  input.city,count(vin)  as  cars, avg(input.engineTemperature)  as  engineTemperature, avg(input.speed)  as  Speed,  avg(input.fuel)  as  Fuel, avg(input.engineoil)  as  EngineOil,avg(input.tirepressure) as  TirePressure,  avg(input.odometer)  as  Odometer
into  EventHubOut  from  input  join  BlobSource
on  input.vin  =  BlobSource.VIN  group  by  BlobSource.model, input.city,  TumblingWindow(second,2)

The following Vehicle Telemetry analytics query will specify a smart car health index parameter with complex streams from a specified two-second timestamp interval in the form of a fixed length series of events:

5. The query could be executed locally or submitted to Azure. While running the job locally, a Command Prompt will appear asserting the local Stream Analytics job’s running status, with the output data folder location:

Command prompt6. If run locally, the job output folder would contain two files in the project disk location within the ASALocalRun directory named, with the current date timestamp. Two output files would be present in .csv and .json formats respectively:

Vehicle Telemetry

Now, if submitted the job to Azure from the Stream Analytics project in Visual Studio, it offers a beautiful job dashboard while providing an interactive job diagram view, job metrics graph, and errors (if any).

The Vehicle Telemetry Predictive Health Analytics job dashboard in Visual Studio provides a nice job diagram with Real-Time Insights of events, with a display refreshed at a minimum rate of every 30 minutes:

Visual Studio

The Stream Analytics job metrics graph provides interactive insights on input and output events, out of order events, late events, runtime errors, and data conversion errors related to the job as appropriate:

Connected Car-Predictive Vehicle Telemetry Analytics

For Connected Car-Predictive Vehicle Telemetry Analytics, you may configure the data input streams processed with complex events by using a definite timestamp interval in a non-overlapping mode such as Tumbling window over a two-second time slicer. The output sink should be configured as Service Bus Event Hub in a data partitioning unit of 32, with a maximum message retention period of 7 days. The output job sink processed events in Event Hub could be archived as well in Azure blob storage for a long-term infrequent access perspective:

Azure Service Bus

The Azure Service Bus, Event Hub job output metrics dashboard view configured for vehicle telemetry analysis is as follows:

  • On the left side of the job dashboard, the Job Summary provides a comprehensive view controller of the job parameters such as job status, creation time, job output start time, start mode, last output timestamp, output error handling mechanism provided for quick reference logs, late event arrival tolerance windows, and so on. The job can be stopped and started, deleted, or even refreshed by selecting icons from the top left menu of the job view dashboard in VS:

Stream Analytics - Vehicle Telemetry Analytics

Optionally, a Stream Analytics complete project clone can also be generated by clicking on the Generate Project icon from the top menu of the job dashboard.

This article is an excerpt from the book, Stream Analytics with Microsoft Azure, written by Anindita Basak, Krishna Venkataraman, Ryan Murphy, and Manpreet Singh. This book provides lessons on Real-time data processing for quick insights using Azure Stream Analytics.

Stream Analytics with Microsoft Azure

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How to get started with Azure Stream Analytics and 7 reasons to choose it

 


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