I have a device which sends data in the form of Hexadecimal value based on an algorithm to IoT Hub. I want to parse that data into a json string to store it in Cosmos DB. Is there any way to achieve this?
I would like to add to Roman's comment on your question, he's right to offer this link to Stream Analytics. It will get the job done. Depending on how many devices you have and how often you are receiving telemetry, you might want to consider using Azure Functions instead. See this sample on how to integrate Azure Functions between IoT Hub and CosmosDB.
The reason I offer this extra solution is that a Stream Analytics Job will cost you a fixed price per hour per streaming unit, while a Function is paid by consumption. Because the conversion from hexadecimal is a fairly small Function, you might even use it for free whereas a Stream Analytics Job in West Europe will cost at least 74 Euros.
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In the context of Azure IoT hub, when would one use Stream Analytics over Time Series Insights?
The product pages and documentation for both indicates they are heavily geared for IoT/data applications. However, I'm not clear on the differences.
The use case I have is both real time monitoring as well as ETL analysis. Could (or even should?) the two be used together?
One immediate difference I can see is that Time Series Insights stores the data whereas Stream Analytics (I think) would need the developer to integrate storage.
In short, stream analytics is about transforming, filtering and aggregation of data and time series insight is about visualising (stored) data.
Data passed through stream analytics is typically forwarded to resources like power bi (for realtime monitoring) or storage like a database for later analysis or processing.
One immediate difference I can see is that Time Series Insights stores the data whereas Stream Analytics (I think) would need the developer to integrate storage.
This is a correct statement. TSI is a data store, but its purpose is to create an environment to (visually) analyze that data. ASA cannot be used to analyze data on its own.
You could use ASA to transform the data and have the data send to Event Hub. That same Event Hub can then be used as a data source for TSI.
In order to write sensor data from an IoT device to a SQL database in the cloud I use an Azure Streaming Analytics job. The SA job has an IoT Hub input and a SQL database output. The query is trivial; it just sends all data through).
According to the MS price calculator, the cheapest way of accomplishing this (in western Europe) is around 75 euros per month (see screenshot).
Actually, only 1 message per minute is send through the hub and the price is fixed per month (regardless of the amount of messages). I am surprised by the price for such a trivial task on small data. Would there be a cheaper alternative for such low capacity needs? Perhaps an Azure function?
If you are not processing the data real-time then SA is not needed, you could just use an Event Hub to ingest your sensor data and forward it on. There are several options to move data from the Event Hub to SQL. As you mentioned in your question, you could use an Azure Function or if you want a no-code solution, you could us a Logic App.
https://learn.microsoft.com/en-us/azure/connectors/connectors-create-api-azure-event-hubs
https://learn.microsoft.com/en-us/azure/connectors/connectors-create-api-sqlazure
In addition to Ken's answer, the "cold path" can be your solution, when the telemetry data are stored in the blob storage by Azure IoT Hub every 720 seconds (such as a maximum batch frequency).
Using the Azure Event Grid on the blob storage, it will trigger an EventGridTrigger subscriber when we can handle starting a streaming process for this batch (or for a group of batches within an one hour). After this batch process is done, the ASA job can be stopped.
Note, that the ASA job is billed based on the active processing time (that's the time between the Start/Stop) which your cost using an ASA job can be significantly dropped down.
Working on a IoT telemetry project that receives humidity and weather pollution data from different sites on the field. I will then apply Machine Learning on the collected data. I'm using Event Hubs and Stream Analytics. Is there a way of pulling the data to Azure Machine Learning without the hassle of writing an application to get it from Stream Analytics and push to AML web service?
Stream Analytics has a functionality called the “Functions”. You can call any web service you’ve published using AML from within Stream Analytics and apply it within your Stream Analytics query. Check this link for a tutorial.
Example workflow in your case would be like the following;
Telemetry arrives and reaches Stream Analytics
Streaming Analytics (SA) calls the Machine Learning function to apply it on the data
SA redirects it to the output accordingly, here you can use the PowerBI to create a predictions dashboards.
Another way would be using R, and here’s a good tutorial showing that https://blogs.technet.microsoft.com/machinelearning/2015/12/10/azure-ml-now-available-as-a-function-in-azure-stream-analytics/ .
It is more work of course but can give you more control as you control the code.
Yes,
This is actually quite easy as it is well supported by ASA.
You can call custom AzureML function from your ASA query when you create this function from the portal.
See the following tutorial on how to achieve something like this.
I am trying to collect data from a ioT device, as for now i am using this code to simulate the device. remote_monitoring. It send data and i can se the data in the dashboard. Next thing is that i want to save the data to a SQL database. I was thinking of using Stream Analytics to do the job. The problem i am having now is that when i select ioT HUB as a input i get the error
Please check if the input source is configured correctly and data is in correct format.
I am trying to find documentation if there is something special i need to add to my JSON object before i send it.
IoTHub is a supported input for Azure Stream Analytics and there is nothing wrong with using ASA as a "pump" to copy data from IoT Hub or Event Hubs to a store like SQL DB. Many use cases of ASA combine such "archiving" with other functions. The only thing to be careful with is the limited ingress rate of many ASA outputs, so SQL DB may not be able to keep up and throttle ASA in which case ASA may fall behind beyond the used hub's retention window, causing data loss.
Try to use Event Hub, update my post when i have a awnser.
What about this doc https://github.com/Azure/azure-content/blob/master/articles/stream-analytics/stream-analytics-define-inputs.md ? Does it help you?
I have just started learning Azure IoT and it's quite interesting. I am confuse about does IoT Hub stores data somewhere?
i.e. Suppose i am passing room Temperature to IoT hub and want to store it in database for further use. How it's possible?
I am clear on how device-to-cloud and cloud-to-device works with IoT hub.
IoT Hub exposes device to cloud messages through an event hubs endpoint. Event Hubs has a retention time expressed in days. It's a stream of data that the reading client could re-read more time because the cursor is on client side (not on server side like queues and topics). With IoT Hub the related retention time is 1 day by default but you can change it.
If you want to store received messages from device you need to have a client reading on the Event Hubs exposed endpoint (for example with an Event Processor Host) that has the business logic to process the messages and store them into a database for example.
Of course you could use another decoupling layer so that the client reads from event hubs and store messages into queues. Then you have another client that at its own pace reads from queues and store into database. In this way you have a fast path reading event hubs.
This is pretty much the use case for all IoT scenarios.
Step 1: High scale data ingestion via Event Hub.
Step 2: Create and use a stream processing engine (Stream Analytics or HDInsight /Storm). You can run conditions (SQL like queries) to filter and store appropriate data in either cold or hot store for further analytics.
Step 3: Storage for cold-path analytics can be Azure BLOB. Stream Analytics can directly be configured to write the Data into it. Cold can contain all other data that doesn't require querying and will be cheap.
Step 4: Processing for hot-path analytics. This is data that is more regularly queries for. Or data where real time analytics needs to be carried on. Like in your case checking for Temperature values going beyond a threshold! needs an urgent trigger!
Let me know if you face any challenges while configuring the Stream analytics job! :)
If you take a look at the IoT Suite remote monitoring preconfigured solution (https://azure.microsoft.com/documentation/articles/iot-suite-remote-monitoring-sample-walkthrough/) you'll see that it persists telemetry in blob storage and maintains device status information in DocumentDb. This preconfigured solution gives you a working illustration of the points made in the previous answers.