A mapping defines reusable data flow logic that you can use in mapping tasks. Use a mapping to define data flow logic that is not available in data loader or data transfer tasks, such as specific ordering of logic.
You can create the following types of mappings:
Mapping
Create a mapping when you need flexibility in your sources, targets, and transformation options. A mapping can read and write to a wide variety of heterogeneous data sources. It also offers a large variety of data transformation options.
When you run a mapping, Data Integration processes some or all of the transformation logic. You can choose to push some or all the transformation logic to the source, to the target, or both. Data Integration processes any transformation logic that isn't pushed to the sources and targets.
Mapping in SQL ELT mode
Create a mapping in SQL ELT mode when your target is a cloud data warehouse, your source is in the same cloud ecosystem or a compatible hyperscaler, and you want all data processing to occur within the underlying cloud infrastructure. For example, you want to read data from your Snowflake cloud data warehouse or data lake, load it to your Snowflake cloud data warehouse, and perform all of the data transformation within the Snowflake ecosystem.
When you run a mapping in SQL ELT mode, Data Integration translates the transformation logic into ecosystem-specific SQL statements and commands that run in the underlying cloud data warehouse. This increases the data processing speed because the data isn't moved out of the cloud infrastructure for processing. It also increases the efficiency of the data integration pipelines.
Use the Mapping Designer to configure mappings. When you configure a mapping, you describe the flow of data from source to target. You can add transformations to transform data, such as an Expression transformation for row-level calculations or a Filter transformation to remove data from the data flow. A transformation includes field rules to define incoming fields. Links visually represent how data moves through the data flow.
You can configure parameters to enable additional flexibility in how you can use the mapping. Parameters act as placeholders for information that you define in the mapping task. For example, you can use a parameter for a source connection in a mapping, and then define the source connection when you configure the task.
You can use components such as mapplets, shared sequences, and user-defined functions in mappings. Components are assets that support mappings. For example, a mapplet is reusable transformation logic that you can use in mappings. A shared sequence is a reusable sequence that you can use in multiple Sequence transformations.