Curious how to loop through files using pySpark? Patrick walks through how he did it for use within his Azure Synapse Analytics Pipelines and Notebooks.
Looping through a list using PySpark in Azure Synapse Pipelines is a great way to process large datasets. PySpark allows you to do so in a distributed manner, meaning that your dataset is split up and processed on multiple nodes in the Azure Synapse cluster. This makes processing more efficient and faster. To loop through a list using PySpark, you will need to use the for loop statement. This statement allows you to iterate through each item in the list and perform the necessary operations. Additionally, you can also use mappartitions, map, and flatMap to loop through the list and perform the necessary operations.
Mar 13, 2023 — Synapse pipelines use workspace's Managed Service Identity (MSI) to access the storage accounts. To use MSSparkUtils in your pipeline activities ...
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Not in this result: Synapse Pipelines
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If we want to kick off a single Apache Spark notebook to process a list of tables we can write the code easily. The simple code to loop through the list of ...
Using PySpark to incrementally processing and loading schema drifted CSV files to Azure Synapse Analytics data warehouse in Azure Databricks.
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