I am using Diagnostic settings to move real time Telemetry data to ADLS2 storage account. I am planning to create a custom Application map application which will need Telemetry data. Since I am importing data to ADLS2, is there any way to query the data in ADLS2 like we do in App insights using Kusto query. I am planning to create a .net 6 api to query the data.
I am open to alternatives to ADLS2 as well.
Please suggest.
Related
Azure Application Insights does not allow telemetry data retention for more then few days, however it has option called "Continuous export" which exports data into Azure Storage Blobs, so question is how do I build reports using data stored in blobs? Is there a way to use Azure Application Insight's Reporting system itself to point to blob storage as "Data Source" and see reports ?
How are others later building reports on Azure Application Insights data that is exported using "Continuous export" option ?
Regards
You can import the data from the Continuous Export into Azure Data Explorer (ADX). Here is a full article that explains this. In ADX you can keep the data as long as you want to.
Then, you can use the cross-query feature of Azure Monitor to also query data from ADX and thus you get a unified view of current and historical data.
We are working on implementing a new project in Azure. The idea is to move out of on-premise systems into the cloud as we have our vendors, partners and clients moving into the cloud. The option we are trying out is to use Azure Data Share and have Azure SQL Database subscribe to the data.
The thing we are now trying to explore is once a new data snapshot is created how do we import this data into Azure SQL Database?
For instance we have Partner information and this information is made available via Azure Data Share and new data snapshot is created daily.
The part that I am not sure of is how to synchronize this data between Azure Data Share and Azure SQL Database.
Also, Is there an api available to expose this data out to external vendors, partners or clients from Azure SQL Database after we have data sync to Azure SQL Database from Azure Data Share?
Azure Data Share -> Azure SQL Database
Yes, Azure SQL Database is a supported.
Azure Data Share -> SQL Server Database (on-prem)? Is this option supported?
No, SQL Server Database (on-prem) is not supported.
Is there an api that could be consumed to read data?
Unfortunately, there is no such API that could be consumed to read data.
Azure Data Share enables organizations to simply and securely share data with multiple customers and partners. In just a few clicks, you can provision a new data share account, add datasets, and invite your customers and partners to your data share. Data providers are always in control of the data that they have shared. Azure Data Share makes it simple to manage and monitor what data was shared, when and by whom.
Azure Data Share helps enhance insights by making it easy to combine data from third parties to enrich analytics and AI scenarios. Easily use the power of Azure analytics tools to prepare, process, and analyze data shared using Azure Data Share.
Which Azure data stores does Data Share support?
Data Share supports data sharing to and from Azure Synapse Analytics, Azure SQL Database, Azure Data Lake Storage, Azure Blob Storage, and Azure Data Explorer. Data Share will support more Azure data stores in the future.
The below table details the supported data sources for Azure Data Share.
How to synchronize this data between Azure Data Share and Azure SQL Database.
You need to choose “Snapshot setting” to refresh data automatically.
A data provider can configure a data share with a snapshot setting. This allows incremental updates to be received on a regular schedule, either daily or hourly. Once configured, the data consumer has the option to enable the schedule.
I am trying to find the way to use the application insight logged data in azure search. For now I am able to export data from Application Insight to Blob storage. But when I am trying to fetch it from azure search then I am getting data related to file not the real data stored in file. Also, in every 0.5-1 minute new file is being created in blob.
Could you please help me to find the way to use the application insight data in azure search?
I don't want to use SQL database as data source in azure search because might be some performance issue will occur if I schedule it to get the data from SQL database in every hour and also it will not be synced.
Please suggest.
Has anybody ever moved Google Analytics data into Azure? I have seen a handful of ways to do it but I am not sure what I am getting myself into. The Google Analytics data is becoming quite large and I am wondering if it is best suited to leave it in google storage and access it from Azure or move it to something like HDInsight or Data Lake. I need to join the data across several disparate data stores, SQL Azure, Blob, and Table Storage. I was also looking into Apache Drill and Presto as a possible solution to unify the data access. Just looking to see if anybody out there has dealt with this same issue and has any experience to share. Thanks!
Preface
I don't have experience with Presto so I can only comment on the feasibility of doing this with Drill. Also I have not used Azure services so my advice is theoretical.
Drill Storage Plugins
Drill will allow you to perform any SQL queries you want on data originating from different sources, provided that each data source has a storage plugin. A storage plugin is simply a piece of code in Drill that allows you to interface with a data source. Since you are concerned with performing queries on 3 data sources, we need to determine if each of those 3 data sources have a Storage plugin.
SQL Azure
I assume SQL Azure has a jdbc driver for java. If so then Drill can be configured to use SQL Azure by following these instructions.
Azure Blob
Azure Blob storage has an implementation of the hadoop filesystem api which Drill uses to read data from file systems. So you could theoretically add the hadoop-azure jar and its dependencies https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-azure/2.7.0 to Drill's class path and configure Drill's DFS storage plugin to use it.
Additionally the data in Azure Blob would have to be stored in a supported file format like: json, parquet, csv, or hadoop sequence files.
Azure Table
This looks like Microsoft's custom NoSQL database. Currently Drill does not support it.
Conclusion
With a bit of work you could use Drill to query data on both Azure SQL and Blob, but not Azure Table.
Currently as part of our requirements we are working with the below Azure components
Azure Event Hub
Azure Stream Analytics
Azure Table Storage
Azure Sql DB
Basically with first 3 components, we will be building an Analytics and Reports platform.
Currently as we just started we analyze the data from Azure Table Storage and display it in the analytics dashboard.
Recently we came across a new Azure product Azure Data Lake . Doing some research on microsoft website , we could see we can easily migrate data from Azure Table Storage (with help of Azure Data Factory) to Azure Lake Store. Creating big data pipelines using Azure Data Lake and Azure Data Factory
As we go through the above link, it's mentioned that we need to create an Azure Data Lake Analytics pipeline to process the data.
So what am unclear is the where will be analytics output data will be saved. Do we need to save the analytics output to some DB ? or can we real-time analytics through a Http request ?
We have huge number rows of records in Azure Table Storage that will be moved to Azure Data Lake. For this scenario is it a good option or Can we go an analytics-based solution from Azure Table Storage itself.
Please share your thoughts
You can store your analytics output data on Azure Data Lake Store (a data repository that enables you to store all kinds of data in their raw format without defining schemas.) after processing it through Azure Data lake Analytics (An analytics service that enables you to run jobs on data sets without having to think about clusters.)
As you said "We have huge number rows of records in Azure Table Storage that will be moved to Azure Data Lake.", I think performing analytics on data placed on Azure data lake store is much more efficient because it offers unlimited storage with immediate read/write access to it and scaling the throughput you need for your workloads. It's also offers small writes at low latency for big data sets. So I believe it is better choice then Azure Table storage.