Required code in pyspark to download ARM template in Azure data factory and store it in the dataframe
Related
I've been reading the following link.
https://learn.microsoft.com/en-us/azure/data-factory/continuous-integration-delivery-improvements
It mentions using npm to build the Data Factory ARM templates and use the resulting artefact to deploy to UAT/Prod etc instead of using the adf_publish branch.
Has anyone got a sample Yaml file that does this?
Also, how would you handle overriding the ARM Template Parameters Json file for changing over parameterization such as the Environments Key Vault etc e.g. Dev-KV -> UAT-KV, Prod-KV
This is what I did. I followed this article to get my yaml file setup, there is a github repo in the article that has all of this persons code.
Azure Data Factory CI-CD made simple: Building and deploying ARM templates with Azure DevOps YAML Pipelines
Then I used Global parameters and referenced those everywhere in my pipelines. Here is a ref for that: Global parameters in Azure Data Factory
And finally, I used the overrideParameters option in my yaml pipeline to deploy the correct version of the parameter to the correct environment. Here is a ref for that: ADF Release - Set global params during deployment
Hope that helps!
Have had a look around but can't see any concrete information. Essentially, if anyone could help it would be great. We are building reporting in the cloud and looking to ingest data from Dataverse that can then be reported on in Power BI
Looking at everything i can see, there is Azure Synapse and Data Factory. What i am trying to understand and learn is whether we use either ADF or Synapse or if its a combination of both
Going into ADF Studio and Synapse Studio, they look very similar so not quite sure what i should be using for this
If anyone could help or provide some info, that would be great
Thanks
Azure synapse provides all the functionalities of Azure Data factory and even more. Using both Azure Synapse studio and Azure data factory, you can perform ETL (extract, transform, load) operations without using any code. Azure Synapse provides enterprise data warehousing and big data analytics.
Azure data factory is a data integration service that allows user to create workflows for moving data and transforming it. Azure synapse, however, will provide additional services like notebooks, SQL scripts, store tables etc. All these functionalities help to ingest, prepare, manage, and analyze data using Power BI or Machine learning.
If you want to use Azure data factory to ingest data, you need to use either azure synapse or Power BI to analyze your data and build reports. But using Azure synapse is a much better choice because it has an integrated Power BI service which allows you to build reports or datasets within the Synapse studio. Hence it would be a better choice to use Azure Synapse instead of Azure data factory or a combination of both.
To understand more about the differences between azure synapse and azure data factory, refer to the following link.
https://azurelessons.com/azure-synapse-analytics-vs-azure-data-factory/
The following link provides an insight about how to use azure synapse studio to work on Power BI.
https://learn.microsoft.com/en-us/azure/synapse-analytics/get-started-visualize-power-bi
So I am facing the following problem: I have a bunch of Azure Data Factory V1
Pipelines in one specific data factory, these pipelines, each have, around 400 data sets.
I need to move all of them to a new resource group / environment and put their json definition in a git repo.
So my questions is, how can I download all the pipelines definitions for a data factory and all the data sets definitions in their json format from Azure?
I don't want to click each one and copy-paste from the Azure UI, as it will take ages.
Call Rest API is good way for both V1 and V2. See this doc.
For ADF V1, you can try using Visual Studio.
Connect via Cloud Explorer to your data factory.
Select the data factory and choose Export to New Data Factory Project
This is documented on SQL Server Central.
Another thing to try is to have Azure script out an ARM template of the Data Factory.
I need to know how can i build continuous deployment for Azure Data factory using VSTS. I know there is an Azure data factory deployment available in VSTS release. But I'm looking for other options using Powershell for deployment.
If anyone has already done anything specific to this provide the links.
This blog should get you started. I'm using a comparable method for deployment. Before deploying the JSON files using a PowerShell command, I edit them to insert environment specific values into the Data Factory definitions. You can pass these values as parameters from the TFS deployment-pipeline.
When I try to deploy ADF project from visual studio to azure, I get the error:
21.02.2017 13:03:32- Publishing Project 'MyProj.DataFactory'....
21.02.2017 13:03:32- Validating 10 json files
21.02.2017 13:03:37- Publishing Project 'MyProj.DataFactory' to Data Factory 'MyProjDataFactory'
21.02.2017 13:03:37- Starting upload of Dependency D:\Sources\MyProjDataFactory\Dependencies\ParseMyData.usql
The dependency is Azure Data Lake Analytics U-SQL script.
Where are the dependencies stored in azure?
UPDATE:
When i try to orchestrate a U-SQL stored proc instead of script the visual studio validator trows me the error on build:
You have a couple of options here.
1) Store the USQL file in Azure Blob Storage. In which case you'll need a linked service in your Azure Data Factory to blobs. Then upload the file manually or add the file to your Visual Studio project dependencies for data factory. Unfortunately this will mean the USQL becomes static in the ADF project and not linked in any way to your ADL project so be careful.
2) The simplest way is just to in line the USQL code directly in the ADF JSON. Again is means you'll need to manually refresh code from the ADL project.
3) My preferred approach... Create the USQL as a stored procedure in the Azure Data Lake Analytics service. Then reference the proc in the JSON using [database].[schema].[procname]. You can also pass parameters to the proc from ADF. For example the time slice. This also assumes you already have ADL setup as a linked service in ADF.
Hope this helps.
I have a blog post about the 3rd options and passing params here if your interested: http://www.purplefrogsystems.com/paul/2017/02/passing-parameters-to-u-sql-from-azure-data-factory/
Thanks