I read what in azure blob very nice save some data for statistics or something else, after it create requests for blob and show statistics to website (dashboard).
But I don't know how to use stream analytics for showing statistics. It is some SDK for create query to blob and generate josn data. Or ... I don't know.
And I have more question about it:
How to save data to blob (it is json data or something else). I don't
know format data for it issue.
How to use stream analytics for create request to blob and after it get data for showing in dashboard.
And maybe you know how to use this technology. Help me please. Thanks, and have a nice day.
#Taras - did you get a chance to toy with the Stream Analytics UI?
When you add a blob input you can either add an entire container - which means Stream Analytics will scan the entire container for new files or you can specify a path prefix pattern which will make Stream Analytics look in only that path.
You can also specify tokens such as {date}, {time} on the path prefix pattern to help guide Stream Analytics on the files to read.
Generally speaking - it is highly recommended to use Event Hub as input for the improved latency.
As for output - you can either use Power BI which would give you an interactive dashboard or you can output to some storage (blob, table, SQL, etc...) and build a dashboard on top of that.
You can also try to do one of the walkthroughs to get a feel for Stream Analytics: https://azure.microsoft.com/en-us/documentation/articles/stream-analytics-twitter-sentiment-analysis-trends/
Thanks!
Ziv.
Related
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 ;)
Using Log Analytics, is it possible to search thru data stored in a container inside an Azure storage account? We have an Azure Function that reaches out to an API in O365 for log data and then it pushes that data into a storage account. We would like to be able to query this data.
We can push content inside your container to log analytics workspace repository using something called log analytics http data collector API.
We need to build your own integration of sending container content to log analytics by leveraging http data collector API.
You may refer to the suggestion mentioned in the article
https://learn.microsoft.com/en-us/azure/azure-monitor/platform/data-collector-api
Additional information: - Azure Functions
- Azure Automation
- Logic App
With any of these what you will do is have some schedule that will run on certain interval. When it is ran you will execute query against Log Analytics to get data. The results from the query you will transfer to Azure Storage may be as blob. You might have to do some transformation on the data depending on your scenario. The most important that you have to make sure is that you do not miss data or upload the same data twice to the storage. Log Analytics query language allows you to specify time frame for the results. I hope this will help you.
Kindly let us know if the above helps or you need further assistance on this issue.
I have an event hub which receives telemetry data from different devices. I created a stream analytics job to process this data and output it to various sinks (Power BI, Cosmos DB and Data Lake). While creating the data lake output I found that I couldn't set the output path based on the message payload. The path I can set inside the sink is of the format: [folder_structure]/{date}{time}. I need a very specific folder structure which would check the message payload and put the file in the specified location. Is there any way to do that?
This capability is currently available in private preview - for output to blob storage.
https://azure.microsoft.com/en-us/blog/4-new-features-now-available-in-azure-stream-analytics/
If this is something you can use, please provide details at the following url. we will add you to the preview program.
https://forms.office.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR8zMnUkKzk5Elg9i6hoUmJVUNDhIMjJESFdVNDhRODNMTVZTNDVIR0w2Qi4u
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.