Introduction to spark notebooks - Learn
docs.microsoft.com/en-us/learn/modules/ingest-data-with-apache-spark-notebooks-azure-synapse-analytics/2-introduction-spark-notebooks
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with an integrated notebook experience.
look and feel of the integrated notebook experience within Azure Synapse Studio is similar to that of Jupyter notebooks in Azure Machine Learning Service.
To ingest data through notebooks, you can use a linked service already defined in the Azure Synapse workspace.
e a linked service defined for an Azure Storage account. By using a linked service, the keys and configurations associated with the storage account are stored in the linked service, and this information is used when you use the notebook to ingest data into an Apache Spark DataFrame from the storage account on which the linked service is defined.
primary storage account defined when it is created.
d to browse data stored in the primary storage account, there's no need to provide the secrets or keys.
ata tab on the left hand-side in the Synapse workspace, you can right-click on a file stored in the primary storage account, and then select New notebook to open a notebook with the data from the file generated.
o write multiple languages in one notebook by using the magic commands expressed by using the %%<Name of Language> synta
could create a temporary table to store ingested data within the notebook, and then use the magic command to enable multiple languages to work with this data.
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