I have created an azure ml experiment which fetches data from API and updates it in sql azure database. My power bi report picks data from this database and displays the report. The data from the source is changing frequently. So I need something like a checkbox in power bi which when checked will trigger the azure ml experiment and update the database with latest data.
I know that we can schedule it to run in Rstudio pipeline but we are not thinking of this approach as it is not financially viable.
Thanks in Advance.
You could use a direct query connection from Power BI to your Azure SQL instance. Then the reports in power bi will be always up to date with the latest data you have. Then the only question is when to trigger the ML experiment. If this really needs to be on demand (rather than on a schedule) you could do that in a button in your own App. You could embed the report in your app so that you get an end to end update.
You could have a look at the Azure Data Factory (ADF), that will help you build data pipelines in the cloud.
You can use ADF to read the data from the API (refresh your data), batch-wise-score it in Azure Machine Learning, and push it directly to your Azure SQL making PowerBI always seeing the latest data which will be scored.
Take a look at the following blog where they take data through this kind of pipeline. You just have to change that the data doesn't come from Stream Analytics but from your API.
http://blogs.msdn.com/b/data_insights_global_practice/archive/2015/09/16/event-hubs-stream-analytics-azureml-powerbi-end-to-end-demo-part-i-data-ingestion-and-preparation.aspx
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I am currently working on a project that involves using Azure DevOps as the main management tool. However, our team has decided to analyze the data generated in the platform through Power BI reports. As such, there are some attachments in the work items that we would like to see displayed in PBI, is there any way to do this?
There are multiple ways to connect to the powerbi to devops and get the work item data. Such as by using analytical view, oData queries and oData feed, etc.
To Create power bi reports using analytical view first you have to import data in the power bi, this can be done by selecting azure devops as an option in the Online service tab which itself is in the get data tab in the powerbi destop app
getdata -> online options -> azure devops
After that it will ask you for organization and team and ask you to login to your devops account too.
After this a popup will immerge and in which you can select the work items data based upon the duration time which has passed such today, last 30 days, entire history, etc.
Refer the following documentation
I'm no MS expert - recently hopped onto the Azure train and apologies in advance if I get some information wrong.
Basically need some input in Azure's architecture utilising Azure Data Factory (as the ETL/ELT tool) and Azure SQL database (as the storage), to a BI output - Power BI. My situation is this;
I have on-premise data sources such as Oracle DB, Oracle Cloud SSAS, MS SQL server db
I'd like to have a MS cloud infrastructure solution for reporting purposes.
No data migration needed - merely pumping on-prem data onto cloud and producing a BI reporting solution
Based on my limited knowledge and Google research, Azure Data Factory caters for all my on-prem sources, as well as the future cloud Azure SQL database. If future analysis is needed, Azure Storage and Azure Databricks can be added in to this architecture. I have sketched out the architecture of my proposed solution.
Just confirming my understanding
Without Azure Storage & Databricks (the 2 pink boxes), the 2 Azure component (DF & SQL database) is sufficient to take data from on-premise sources, process on cloud & output into Power BI.
With Azure Storage & Databricks (the 2 pink boxes), processing will be more efficient as their summarised function is to store training data models & act as an analytics processing engine.
Azure SQL database is more suitable, as compared to Azure SQL datawarehouse as my data sources does not exceed 1TB; cost-wise is cheaper AND one of my data sources contain data from call centers, hence OLTP is more suitable. Plus I have Azure Databricks to support the analytical bit that SQL datawarehouse does (OLAP).
Any other comments to help me understand this whole architecture will be great!
I am a new learner of Azure. I was wondering if we have #Query (value="...") kind or any equivalence for DocumentDb (CosmosDB). Because, the documentDB does not take #Query. I am looking to convert the sql query (From jpa to cosmosDB).
Taking data from on-prem or IaaS sources like SQL on a VM, Oracle etc, requires a Self-Hosted Integration Runtime (SHIR).
Please review the Modern Data Warehouse pattern which sounds similar to what you are proposing.
Hey,
I am having trouble in creating a Data Flows task which uses an on-prem source. Is this not possible in the Preview version?
I have created a self hosted IR to connect ADF to my laptop, and that is what I want to use. In the pic below I am trying to create a dataset off self hosted IR. It works great in Copy task but for Data Flows it is greyed out.
For this question, I asked Azure support for help and they replied me with the answer:
Answer:
This means on-premise SQL server is not supported as dataset in data flow in current stage.
Screen shot:
Update:
Data flow now only support Azure IR so it doesn’t support on-premise dataset.
Refer to Integration runtime types.
Hope this helps.
If your goal is to use visual data transformations in ADF using Mapping Data Flows with on-prem data, then build a pipeline with a Copy Activity first. Use the Self-Hosted Integration Runtime with the Copy Activity to stage your data in Blob Store. Then add a subsequent Execute Data Flow activity to transform that data.
I made video on how to do this:
https://www.youtube.com/watch?v=IN-4v0e7UIs
Reproduce your issue on my side ,however nothing about this feature is claimed on the official document. As you can see everywhere about data flow cliams that:
You could submit any voice here:
Also found a feedback for data flow in ADF for your reference.If you need push the progress of it,you could vote up it. Also,i would suggest you referring to the comments in the link:
For access to the 80+ ADF connectors, use Copy Activity to stage data
for transformation.
Data Flows will access data in your lake (Blob, ADB, ADW, ADLS) for
transformation.
Think of Copy Activity as your data heavy-lifting activity and Data
Flow as your data transformation engine.
I have some T-sql scripts which generate some data and we manually update them into the excel spreedsheet, we need a way to push this into azure sql database, from a job so that we can access them from there and remove the manual process of uploading the information to the azure sql database every time. What is the best way to do this?
I assume you are trying to move data from an on prem server to Azure. The simplest method may be Azure Data Sync.
You could load your data from your queries into an on prem table which syncs to Azure.
On all your SQL Server instances, you can create a Linked Server to one Azure SQL Database. Once the linked server is created you can directly insert on Azure SQL Database from your on-premises SQL Server instances.
Here is how you create the Linked Server.
Below image shows how you insert data on Azure SQL Database using the linked server.
For detailed steps, you can visit this tutorial.
I think you can think about Azure Data Factory.
Azure Data Factory Copy Active can help you use T-sql scripts to move data to another Azure SQL database.
For more details, please the Azure tutorial:Copy multiple tables in bulk by using Azure Data Factory.
When the pipeline created, you can trigger and monitor the pipeline runs.
Trigger the pipeline on a schedule:
You can create a scheduler trigger to schedule the pipeline to run periodically (hourly, daily, and so on). In this procedure, you create a trigger to run every minute until the end date and time that you specify.
Please see: Trigger the pipeline on a schedule.
This can help you push the data to Azure SQL Database automatically.
Hope this helps.
you can try the SSIS package? which automates the process of data upload data into azure sql database.... i have not used ssis for Azure but to sink data from csv/xls/xlsx into ms sql server database,,I refered this article which can be helpful in anyway
I have several external data APIs that I access using some Python scripts. My scripts run from an on-premises server, transform the data, and store it in a SQL Server database on the same server. I suppose it's a rudimentary ETL system run with Python and T-SQL.
The system is about to grow quite a bit with new APIs and will require more complex data pipelines (for example, some of the API data will be spun off to more than one table). I think this would be a good time to move the system onto Azure (we are heavily integrated with Microsoft so it will have to be Azure!).
I have spent a few days researching the Azure products that would let me run Python scripts to access data from web APIs and store the processed data in a cloud database. I'm looking for advice on what sort of Azure products other people have used for similar jobs. At the moment it seems I will need:
Azure SQL Database to hold the processed data that can be accessed by various colleagues.
Azure Data Factory to manage, log, and schedule the pipeline jobs and to run my custom Python scripts (is this even possible?).
Azure Batch to run the aforementioned Python scripts but I'm not sure about this.
I want to put together a proposal basically and start thinking about costs but it would be good to hear from someone who has done something similar - am I on the right track or completely off? Should I just stay on-premises? Thank you in advance.
Azure SQL Database, Azure SQL Data Warehouse are good for relational data. And if you want to use NoSQL, you could go with Azure Cosmos DB. If you want to use Files to store data, you could use Azure Data Lake.
For python scripts, you could use custom activity or Data bricks for Azure Data Factory.
Azure SQL Warehouse should be used if the amount of data you want to load is in petabytes. Also, Azure Data warehouse is not meant for complex transformations. I would recommend it for plain data load with PolyBase.