I am pushing events to my Event hub, then this data is being analyzed in Azure Stream Analytics. I'd like to visualize output from stream analytics in Grafana.
What is the easiest approach to achieve this?
Azure Stream Analytics job can natively ingest the data into Azure Data Explorer. https://techcommunity.microsoft.com/t5/azure-data-explorer/azure-data-explorer-is-now-supported-as-output-for-azure-stream/ba-p/2923654
You can then use the Azure Data Explorer plugin in Grafana. https://techcommunity.microsoft.com/t5/azure-data-explorer/azure-data-explorer-is-now-supported-as-output-for-azure-stream/ba-p/2923654
Another option is to use Power BI instead of Grafana. https://learn.microsoft.com/en-us/azure/stream-analytics/stream-analytics-power-bi-dashboard
If I remember correctly, Grafana doesn't store data locally, you need to define a data source on top of one of the compatible storage systems.
Azure Stream Analytics doesn't come with a storage layer either, it's compute only.
So if you want to use ASA and Grafana, you need to output data from ASA to a data source that is supported by Grafana in ingress.
Looking at both lists that leaves only MSSQL via Azure SQL (hopefully it's compatible) as a native option. It's not a bad option for narrow dashboards, or if you intend to store your data in a RDBMS anyway. You can store your entire payload in an NVARCHAR(MAX) if you don't plan to consume the data in SQL.
But being clever, we can actually use the Functions output to write to any other store, or call any API. I'm not sure if Grafana has a direct ingestion API, but Azure Monitor does and it's a supported data source in Grafana.
The other option would be to go through ADX as explained in the other answer.
Not straightforward but doable ;)
Related
How can we create the real-time data pipeline while data resides on Azure Data Lake Storage Gen2, and the analytics has to be done using Elastic Stack.
What can be the integration tool or technique for the completion of this design?
As #Nick.McDermaid mentioned in the comment that you need to reconsider your design. AFAIK there is no such tool available which can integrate Azure Data Lake Gen2 and Elastic Stack for real time analytics.
Alternatively, the better way to implement your requirement is by using the Azure products designed for real time analytics like Azure Stream Analytics, Azure Synapse Analytics, etc. You can also consider Azure Data Factory for data movement and transformation.
You can check out this page to know more about all the analytics products available in Azure. Choose the best which suits your requirement and try to implement using official document examples.
I'm following the instructions to set up App Insights to spool to SQL using Azure Stream Analytics, but I'm trying to deviate slightly to use an on-premise SQL server (that the web application already uses) over VPN.
At the point of adding the output, this is failing with:
Is it the case that IP addresses are not supported, or is it something more fundamental than that?
You are probably looking for answers directly to your question, which Jean-Sébastien answers succinctly. But an alternative architecture, if you haven't considered it already...
You could stream to a transient Azure SQL Database or Blob storage (likely cheaper depending on your workload), and then use Azure Data Factory tunnelled via a Self-Hosted Data Factory Integration Runtime to "send" the data back to on-premise SQL.
Data Factory V2 also has blob triggers, so rather than needing a schedule it could pickup any new blobs in micro batches.
I say "send" in quotation marks as the Integration Runtime actually creates an outgoing connection to from on-premise to Azure, yet gives the capability for push-like data transfer.
If data factory proves useful, here is a guide creating copy pipelines: https://learn.microsoft.com/en-us/azure/data-factory/tutorial-hybrid-copy-portal
Albeit this guide is for on-prem sql to blob, but it gives you a stronger starting point.
At this time only Azure SQL Databases are supported in Azure Stream Analytics.
Sorry for the inconvenience.
Thanks,
JS (Azure Stream Analytics)
I am looking into combining data (stored in Azure SQL) and real-time stream data (coming via IoT Hub) in Stream Analytics. One way I found is to use blob storage to copy the SQL Azure data and use it as Input type "Reference Data" and in Stream Analytics query editor JOIN with the streaming data which works fine. However, I am looking into whether it is possible to use JavaScript UDF function capability in stream analytics to get data from SQL Azure and combine with streaming IoT data? I also don't know which one is the suggested approach to combining these type of data together?
Thanks
UDFs in streaming analytics won't allow you to call out to external services like SQL. They're used for things like basic data manipulation, regex, Math, etc. If your SQL data is slow moving in nature, the approach you've outlined here of using something like Data Factory to move SQL information into Blob storage and then use it as a Reference data inside your Stream Analytics query is the correct way (and only way currently) to solve your problem.
If it's fast moving data in SQL you'd want to investigate hooking into the SQL database changes and then publishing them on to Event Hubs. You could then pull this into your query as a second Data Stream input type and do the appropriate joins in your query.
I am new to Azure. I have sensors and would like to send data from sensors to the Azure backend, preferably to a database. After collecting those sensor data I would like to display them on a dashboard. I wonder if there is a sample tutorial or source code to implement such a solution. Hope you can help me.
Thanks in advance & Best Regards.
The Azure IoT Suite is an accelerator that configures a solution using standard Azure services and each one comes with a dashboard. The source code is available on GitHub: Remote Monitoring and Predictive Maintenance
There are multiple ways of achieving this as Azure is not a final product but consists of different "modules" if you will.
If the idea is to create a dashboard that shows the sensor data, you don't necessarily need to store them into a database. You can create live streams and display them. If the storage of data is also of concern, then in parallel you can do that as well.
The redirection logic of the data here would be Stream Analytics, it works with the concepts of an input sink, a query and an output sink. Now in your case you might want to create an Event Hub/ IoT Hub, then use it as an input sink to the Stream Analytics, and use PowerBI as the output sink. This will get the data and display it in PBi. If you also want to store the data, you can add another output sink for different options like , blob storage, document db, azure sql database etc.
Also there is an Azure IoT suite remote monitoring solution, that automates most of those tasks with some extra modules, you can use that to create a solution and use it as a boilerplate.
Below are step-by-step tutorials for;
Event Hubs
Stream analytics
PowerBI
Hope this helps!
Mert
Looking to do real time metric calculations on event streams, what is a good choice in Azure? Stream Analytics or Storm? I am comfortable with either SQL or Java, so wondering what are the other differences.
It depends on your needs and requirements. I'll try to lay out the strengths and benefits of both. In terms of setup, Stream Analytics has Storm beat. Stream Analytics is great if you need to ask a lot of different questions often. Stream Analytics can also only handle CSV or JSON type data. Stream Analytics is also at the mercy of only sending outputs to Azure Blob, Azure Tables, Azure SQL, PowerBI; any other output will require Storm. Stream Analytics lacks the data transformation capabilities of Storm.
Storm:
Data Transformation
Can handle more dynamic data (if you're willing to program)
Requires programming
Stream Analytisc
Ease of Setup
JSON and CSV format only
Can change queries within 4 minutes
Only takes inputs from Event Hub, Blob Storage
Only outputs to Azure Blob, Azure Tables, Azure SQL, PowerBI
If you are looking for versatility over flexibility. I'd go with Stream Analytics, if you require specific operations that are limited by Stream Analytics, it's worth looking into Spark, which gives you data persistence options. On the Stream Analytics outputs side, one interesting thing would be to output into an Event Hub and consume it from there giving you unlimited flexibility on how you want to consume the data.
Below is the output options for Stream Analytics and the link for Apache Spark on Azure
Hope this helps.