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.
Related
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
I am trying to migrate a pipeline from a data factory with pipelines/ds/ls related to other pipelines. To do this, I want to find all the related ds/ls/resources to the pipeline that I want to migrate to a different data factory(differnet env). What would be the way to do so? Secondly, how would you do it using ARM Template in release pipelines?
You can simply accomplish this task by importing/exporting ARM templates. This will export your datasets, Linked Services and pipelines settings. But if you are changing your data source and destination, you need to change them separately.
To export the pipeline configuration as a template just go to that pipeline and click on three dots on right side and click on Export template option.
We are trying to use self hosted integration runtime to extract data from on-prem fileshare. To implement CI/CD, I have created arm templates from the data factory where IR is successfully working and enabled sharing on for the Data Factory in which I am going to deploy my pipelines using ARM templates. I can successfully deploy pipeline and self hosted IR and linked services but IR is not available in the new data factory connections.
Is it normal? Because to use CI/CD with Data Factory, as soon as ARM gets deployed we should be ready to run pipelines without manual changes? And if I am correct then can anyone help why IR in the new Data Factory isn't available which is making the pipeline failed when I am trying to run it.
Self Hosted Integration Runtime are tied to the ADF it is created in.
To use CI/CD with Self Hosted IR you need to do following steps:
Create a new Data Factory other than the ones you using in CI/CD
process,then create the Self hosted Integration Runtime their.(This
ADF doesn't need to contain any of your pipeline or Dataset).
Go to the newly created Integration Runtime and click on edit or pencil
icon. Go to sharing tab of opened window.
Click on Grant Permission to other Data factory.(Search and Give Permission to all ADF
involved in CI/CD Process).
Copy the resource id Displayed. Go to the DEV Data Factory and create new Self hosted runtime of type linked.
5.Enter the Resource ID when asked and click create.
6.Then proceed to setup CI/CD process through DEV Data Factory.
Since through ARM template in all other Data factory linked Self Hosted IR will be created and if you provided permission then everything will work.
A Self-Hosted Integration Runtime, is 'owned' by exactly one Data Factory instance. The way the 'owner' and the 'sharer' factories define the IR are different. When you deployed one over the other, the type changed and you ended up with either two 'owners' or two 'sharers'. Since there can only be one 'owner' or a 'sharer' points to an 'owner', things break.
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