![]() The Azure Synapse Workspace DB connector is currently in public preview and can only work with Spark Lake databases at this time The databases created through the Azure Synapse database templates are also accessible when you select Workspace DB. This will alleviate the need to add linked services or datasets for those databases. When using data flows in Azure Synapse workspaces, you will have an additional option to sink your data directly into a database type that is inside your Synapse workspace. Instead of selecting a sink dataset, you select the linked service you want to connect to. To use an inline dataset, select the format you want in the Sink type selector. Inline datasets are based in Spark, and their properties are native to data flow. If your sink is heavily parameterized, inline datasets allow you to not create a "dummy" object. Inline datasets are recommended when you use flexible schemas, one-off sink instances, or parameterized sinks. Occasionally, you might need to override certain settings or schema projection in the sink transformation. These reusable entities are especially useful when you use a hardened schema. Dataset objects are reusable entities that can be used in other data flows and activities such as Copy. ![]() When a format is supported for both inline and in a dataset object, there are benefits to both. To learn how to use a specific connector, see the appropriate connector document. Most formats are available in only one or the other. When you create a sink transformation, choose whether your sink information is defined inside a dataset object or within the sink transformation. The sink transformation determines the shape and location of the data you want to write to. To write to additional sinks, create new streams via new branches and conditional splits.Įach sink transformation is associated with exactly one dataset object or linked service. Every data flow requires at least one sink transformation, but you can write to as many sinks as necessary to complete your transformation flow. If you are new to transformations, please refer to the introductory article Transform data using a mapping data flow.Īfter you finish transforming your data, write it into a destination store by using the sink transformation. This article applies to mapping data flows. Data flows are available both in Azure Data Factory and Azure Synapse Pipelines.
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