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Dataset Parameters | Foundations of Azure Data Factory
Introduction to Data Engineering with Azure
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Introduction to Data Engineering with Azure

Introduction to Data Engineering with Azure

1. Getting Started with Azure and Core Tools
2. Foundations of Azure Data Factory
3. Data Flows and Transformations in ADF
4. Practical Problem Solving with ADF

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Dataset Parameters

Parameters in Azure Data Factory are dynamic elements that make pipelines, datasets, and linked services flexible and reusable. By leveraging parameters, you can create solutions that adapt to different scenarios with minimal configuration changes. In this chapter, we'll focus on dataset parameters, particularly using them to define the table name dynamically for a SQL dataset.

For example, in a SQL dataset, instead of hardcoding the table name, you can use a parameter to set the table name at runtime. This approach is especially useful when working with multiple tables or environments.

We created a dataset with a table name parameter to enable dynamic table selection during data operations. By defining a parameter for the table name in a SQL dataset, you can reuse the same dataset for multiple tables, making your workflows more efficient and adaptable.

This setup lays the foundation for the ForEach Activity, where we will dynamically iterate over multiple table names and perform operations on them. This approach is essential for scalable and flexible data integration pipelines.

What is an example of a dataset parameter in a SQL dataset?

What is an example of a dataset parameter in a SQL dataset?

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