In this episode of Pragmatic Works' "Introduction to Synapse," Mitchell Pearson walks viewers through the process of creating dynamic datasets using parameters in Azure Synapse Analytics (ASA). These dynamic datasets are reusable across various pipelines and activities, providing a more flexible and scalable solution for working with data. This blog will summarize the key steps in the video, demonstrating how to leverage parameters within ASA to create dynamic, efficient workflows.
Mitchell starts by revisiting a previous project where a For Each activity iterates over data files in Azure Data Lake, processes them, and writes the information to a database table. However, a limitation arises when the metadata activity only returns basic file information (name and type). The goal of this session is to go beyond that and retrieve the file's last modified date using parameters, making the process more dynamic and reusable.
To achieve this goal, the first step is to create a dynamic dataset. Mitchell demonstrates how to create a dataset that connects to Azure Data Lake Gen 2, specifically designed for CSV files. The key difference here is the use of parameters to replace hard-coded values. This enables the dataset to be reused for multiple files rather than being static and tied to a single file.
Mitchell emphasizes that instead of importing a schema, the dataset should be left flexible so it can accommodate various file structures within the data lake.
Once the dataset is created, the next step is to add parameters. A parameter is a placeholder for dynamic values. Mitchell walks through creating a new parameter called fileName that will hold the name of each file iterated over in the For Each activity.
By doing this, each time the For Each activity iterates, the fileName parameter dynamically updates to the name of the file being processed.
With the dynamic dataset in place, Mitchell now revisits the For Each activity. Here, he shows how the activity iterates over the files and invokes the Get Metadata activity to fetch details like the last modified date of each file.
After configuring everything, Mitchell runs the pipeline to test the setup. The For Each loop successfully iterates through the files, retrieving their last modified dates and writing the information into the database table. He highlights that by using dynamic datasets and parameters, the process becomes scalable and avoids creating multiple static datasets for each file.
Mitchell concludes the session by reinforcing the importance of dynamic datasets and parameters in Azure Synapse Analytics. By leveraging these tools, users can create reusable, scalable pipelines that avoid unnecessary bloat in the workspace. This method allows for more efficient management of data processes, making it easier to handle a variety of file structures and metadata.
For those interested in learning more, Mitchell suggests reviewing the previous episodes in the series to fully understand the concepts and build upon this knowledge. Dynamic datasets are a powerful tool for streamlining your data operations in ASA, and with the right configurations, they can save you significant time and effort.
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