Linkservice

Question

How use 2 link service in multiple pipelines without conflict.. As it us over written every time have add new pipeline source and target db

Answer 1

Based on this old thread :

You should use the same linked service. Linked service is just a connection to your source and sink and the actual transformations are happening there irrespective of from where you call. So ideally there won't be any change in performance if you use same linked service or multiple linked service since the engine being triggered is the same.

When defining your linked services (for databases or storage accounts), create them in a way that they can be reused across multiple pipelines. This means using general-purpose names and configurations that can be applied to different pipelines without modification.

So you can use parameters in your pipelines to make the linked services flexible. This way, you can pass different values (database names, connection strings) to the same linked service depending on the pipeline or activity that is using it.

And you can use Global Parameters that can be used across multiple pipelines. You can use these parameters to manage different configurations for linked services, thus avoiding the need to overwrite them each time you add a new pipeline.

In your pipelines, reference these global parameters when configuring activities that require the linked service.

Then you can create Separate Linked Services when necessary. If you have environments like Dev, Test, and Prod, consider creating separate linked services for each environment. Name them clearly (LinkedService_Dev, LinkedService_Test) and reference the appropriate service in your pipeline depending on the environment.

If your pipelines require different Integration Runtimes (IRs), ensure that the linked services are correctly associated with the intended IR. Misconfiguration can lead to conflicts or unintended data flows.

You can also break down your data workflows into smaller, reusable pipeline modules. Each module can use the same linked service without conflicts if designed correctly. This modular approach simplifies the management and avoids overwriting linked services.

If you're working in a team or need to maintain versions, use source control (Git) integrated with ADF. This ensures that changes to linked services are tracked and managed across different pipelines.

Using Azure DevOps or GitHub Actions, implement CI/CD pipelines to automate the deployment of linked services and pipelines across environments. This can help manage versions and avoid conflicts when updates are made.

Answer 2

@Vineet S

Welcome to the Microsoft Q&A and thank you for posting your questions here

To use two linked services in multiple pipelines without conflicts in Azure Data Factory, you can follow these steps:

1. Create Separate Linked Services: Ensure that each pipeline uses its own linked service. This way, changes in one pipeline won’t affect the other. For example, create LinkedService1 for Pipeline A and LinkedService2 for Pipeline B.
2. Parameterize Linked Services: Use parameters to dynamically pass the linked service information to the pipelines. This allows you to reuse the same pipeline with different linked services. You can define parameters in the pipeline and then pass the linked service name as a parameter.
3. Use Global Parameters: Define global parameters in Azure Data Factory that can be used across multiple pipelines. This helps in maintaining consistency and avoiding conflicts.
4. Manage Linked Service Connections: Use the advanced properties of linked services to manage connections and avoid conflicts. You can specify different connection strings or credentials for each linked service.
5. Version Control: Implement version control for your pipelines and linked services. This helps in tracking changes and reverting to previous versions if needed.

for detailed information and screenshots check the link below

https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/how-to-advanced-properties-of-linked-services/ba-p/3627033

To avoid the issue of the dataset showing the previous table name when using the same dataset in multiple pipelines, you can parameterize the dataset. This way, you can dynamically pass the table name to the dataset, ensuring that each pipeline uses the correct table name without conflicts.

Here’s how you can do it:

Create a Parameterized Dataset:

• Go to your dataset in Azure Data Factory.
• Add a parameter for the table name.

Modify the Dataset to Use the Parameter:

• In the dataset’s JSON definition, replace the static table name with the parameter.

Pass the Parameter from the Pipeline:

• In each pipeline, pass the appropriate table name to the dataset parameter.

Hope this helps. Do let us know if you any further queries.