The function outputs a single complex column with subcolumns for each column mapped.Ī cache sink must be in a completely independent data stream from any transformation referencing it via a cache lookup. To learn more about the cache lookup syntax, see cached lookups.įor example, if I specify a single key column of column1 in a cache sink called cacheExample, calling cacheExample#lookup() would have one parameter specifies which row in the cache sink to match on. If you specify key columns, you can't use the outputs() function in a cache lookup. These are used as matching conditions when using the lookup() function in a cache lookup. In the sink settings, you can optionally specify the key columns of the cache sink. Unlike other sink types, you don't need to select a dataset or linked service because you aren't writing to an external store. To write to a cache sink, add a sink transformation and select Cache as the sink type. Common examples where a cache sink can help are looking up a max value on a data store and matching error codes to an error message database. This is useful when you want to reference data as part of an expression but don't want to explicitly join the columns to it. In mapping data flows, you can reference this data within the same flow many times using a cache lookup. The following video explains a number of different sink options for text-delimited file types.Ī cache sink is when a data flow writes data into the Spark cache instead of a data store. Development values for dataset parameters can be configured in Debug settings. Here you can pick or create the dataset your sink writes to. Sink settingsĪfter you've added a sink, configure via the Sink tab. ![]() To write data to those other sources from your data flow, use the Copy Activity to load that data from a supported sink. The service has access to more than 90 native connectors. Information and data flow script examples on these settings are located in the connector documentation. ![]() Settings specific to these connectors are located on the Settings tab. Currently, the following datasets can be used in a source transformation. Mapping data flow follows an extract, load, and transform (ELT) approach and works with staging datasets that are all in Azure. 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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