I have a centralised log analytics workspace in Azure and a use-case for streaming (or otherwise ingesting) all Log Analytics data in the centralised workspace to a kafka "data backbone".
My question is:
Are there recommended patterns for this specific use case?
I've done some research but found nothing out of the box in terms of kafka connectors or integration patterns for ingesting Azure log analytics data wholesale into Kafka directly.
(I suspect this is a rare use case)
It appears the recommended integration pattern (from Microsoft) is via Azure data export to Azure storage account:
Data export in a Log Analytics workspace lets you continuously export data per selected tables in your workspace. You can export to an Azure Storage account or Azure Event Hubs as the data arrives to an Azure Monitor pipeline. This article provides details on this feature and steps to configure data export in your workspaces.
https://learn.microsoft.com/en-us/azure/azure-monitor/logs/logs-data-export?tabs=portal
(anecdotal: it appears this solution is acceptable to some enterprise customers)
Related
I have created a log analytics workspace which used the grab all metrics from a databricks cluster.
Actually, my query would be to get the times when the cluster were running more than 10 worker instances. Can I find this information through log analytics in azure for databricks?
any hint would be apprecited.
You can use the Log4j appender to send your Azure Databricks application logs to Azure Log Analytics.
Refer Send application logs using Log4j to know the steps to implement the same.
You can also visualize the Databricks resource metrics using Grafana and Virtual Machine. Check this official Microsoft documentation for the same. Use dashboards to visualize Azure Databricks metrics
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.
Is there a way to access the metadata of Azure Data Catalog? I looked up the documentation and went through the Azure Activity log of Azure Data Catalog. However, it seems like there is no access activities(i.e. who accessed Azure Data Catalog at what point of time) log I can use. Is there such activity anywhere in Azure at the moment?
Unfortunately there is no such way to check the activity logs. I would recommend you to please have a look at Azure Purview which has updated Data Catalog features.
You can refer to this document which has describes how to configure metrics, alerts, and diagnostic settings for Azure Purview using Azure Monitor: Azure Purview metrics in Azure Monitor
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)
Where does data logs of Azure Pipeline v2 gets stored, I would like to retrieve data of failed pipelines for specific date.( Dont want to use azure portal to view these data). Is there any table/view holds such datalogs from database.
To my knowledge, to obtain diagnostic logs you can use Azure Monitor, Operations Management Suite (OMS), or monitor those pipelines visually.
By Azure Pipeline v2, you mean Azure Data Factory v2. Alert and Monitor data factories using Azure Monitor
Diagnostic logs:
Save them to a Storage Account for auditing or manual inspection. You can specify the retention time (in days) using the diagnostic settings.
Stream them to Event Hubs for ingestion by a third-party service or custom analytics solution such as PowerBI.
Analyze them with Log Analytics
The logs are stored on Azure Data Factory web server for 45 days. If you want to get the pipeline run and activity run metadata, you can use Azure Data Factory SDK to extract the information you need and save it somewhere you want.
Recommended approach on this for log term analysis as well as limiting access to a production data factory would be to configure logs to be sent to log analytics. Be sure to enable dedicated logging tables as this will help on the backend in terms of organizing your logs.
From there you can also set up alerts and access groups running off of log analytics queries for better monitoring.