If reading/writing to storage via pipeline, Synapse Workspace MSI would be the security principal performing any operation (Read, Write, Delete) on the storage.To write data to internal tables, the connector now uses COPY statement instead of CETAS/CTAS approach. If you prefer take advantage of the new feature-rich capabilities now available via the Synapse workspace and Studio and load data directly from Azure Apache Spark to Dedicated Pool in Azure Synapse Workspace is recommended that you enable Synapse workspace features on an existing dedicated SQL pool (formerly SQL DW).īefore we start, here is some initial considerations. The intention of this guide is to help you with which configuration will be required if you need to load data from Azure Synapse Apache Spark to Dedicated SQL Pool (formerly SQL DW). Consider a scenario where you are trying to load data from Synapse Spark to Dedicated pool (formerly SQL DW) using Synapse Pipelines, and additionally you are using Synapse Workspace deployed with Managed Virtual Network. Usually, customers do this kind of operation using Synapse Apache Spark to load data to Dedicated Pool within Azure Synapse Workspace, but today, I would like to reproduce a different scenario that I was working on one of my support cases. The intention of this Guide is not explain all the Connector features if you require a deeper understanding of how this connector works start here. The connector supports Scala and Python language on Synapse Notebooks to perform this operations. The Azure Synapse Dedicated SQL Pool Connector for Apache Spark is the way to read and write a large volume of data efficiently between Apache Spark to Dedicated SQL Pool in Synapse Analytics. ![]() A common data engineering task is explore, transform, and load data into data warehouse using Azure Synapse Apache Spark.
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