Azure Synapse Analytics provides a powerful platform for managing big data and running analytics at scale. In this video, Mitchell Pearson of Pragmatic Works explains how to incorporate stored procedure activities into an Azure Synapse Analytics pipeline. This feature can be used to streamline database operations, making it easier to automate data processing workflows. Below is a breakdown of the key points discussed in the video.
To begin, Mitchell walks through the essential preliminary setup required before working with the stored procedure activity in Azure Synapse Analytics. He starts by creating a basic table within a database. This control table includes columns like:
Although the example uses a simple control table, in a production environment, this table would likely have additional columns such as status updates or validation checks, helping to manage ETL processes efficiently.
Once the database and table are set up, the next step is to establish a connection between Azure Synapse and the database. Mitchell uses Azure SQL Database for this example. The process includes creating a link service in Azure Synapse Analytics:
After successfully testing the connection, the link service is created, enabling Azure Synapse to interact with the database.
The stored procedure activity is part of a larger pipeline that processes files retrieved from an Azure Data Lake. In the previous video of the series, Mitchell demonstrated how the Get Metadata activity retrieves metadata from files in a folder. The stored procedure activity is introduced as a way to process this metadata and insert it into the database.
Here’s how the pipeline works:
In Azure Synapse Analytics, the stored procedure activity requires specifying a few key parameters, such as the file name, last modified date, and record insert date. However, since the metadata returned by the Get Metadata activity only includes file names and types, additional steps are needed to retrieve the last modified date.
To address this, Mitchell suggests using an additional Get Metadata activity inside the For Each loop to fetch the complete metadata for each file. This step will provide the last modified date, which can then be passed into the stored procedure.
After the stored procedure is configured, the next step is to map the input parameters. This allows the stored procedure to dynamically receive values from the pipeline and insert them into the database. For each iteration of the For Each loop, the following parameters are passed:
Mitchell uses the UTCNow() function to pass the current timestamp into both the LastModifiedDate and RecordInsertDate fields.
Once everything is configured, Mitchell runs the pipeline in debug mode to test its functionality. By doing this, he can test the pipeline without publishing it, allowing for easy adjustments if necessary. During the debug session, the pipeline successfully inserts the file names and metadata into the table, as demonstrated by running a SQL query to check the table contents.
Mitchell wraps up the video by reinforcing the simplicity of using the stored procedure activity within an Azure Synapse Analytics pipeline. By following these steps, users can automate data ingestion processes and interact with databases in a seamless manner.
Here are the key takeaways:
For more information and to access the scripts used in the video, be sure to check the download link in the description. Pragmatic Works also offers comprehensive training on Azure Synapse Analytics and other data-related topics through live events, boot camps, and on-demand courses.
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