Parameterization in Pipelines
Parameterization in Pipelines
Parameterization in pipelines allows dynamic control over the execution of data workflows by using variables instead of hardcoded values. This makes pipelines reusable, scalable, and easier to maintain. Instead of writing multiple pipelines for different inputs or environments, a single parameterized pipeline can handle various scenarios.
For example, in Azure Data Factory or other ETL tools, you can define parameters such as file names, paths, table names, or date ranges. These parameters can be passed at runtime via triggers, datasets, or pipeline runs. During execution, these parameters are replaced with actual values, enabling flexibility.
Key advantages:
Reusability: One pipeline works for multiple use cases.
Maintainability: Easier updates and fewer duplicate pipelines.
Flexibility: Adjust behavior based on environment (Dev, Test, Prod).
Automation: Enables scheduled and event-based automation with dynamic inputs.
Parameter types may include string, integer, Boolean, or arrays. These are often used in activities like Copy Data, Lookup, or Execute Pipeline.
Example: You can use a parameterized file path like @concat('input/', pipeline().parameters.filename) to dynamically load different files each run.
In summary, parameterization enhances pipeline flexibility, reduces redundancy, and simplifies data integration processes across cloud and on-premises systems.
Copy Activity (Blob to SQL etc.)
Copy Activity (Blob to SQL etc.)
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