Robust Import Set Transformers
Summarize
Summary of Robust Import Set Transformers
Robust import set transformers provide an advanced method for extracting, transforming, and loading data into one or more target tables, offering a more flexible alternative to traditional transform maps in the ServiceNow AI Platform.
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Key Features
- Separation of Functions: Unlike transform maps that combine transformation and processing into one, robust import set transformers separate these tasks, allowing for greater flexibility.
- Single Read Operation: When loading data to multiple target tables, robust import set transformers streamline the process by requiring only one read operation, enhancing performance.
- Extract Transform Load (ETL): This functionality allows users to define how data is extracted, transformed, and loaded, including mapping entities and their fields.
- Support for Nested Data Structures: As of the Paris release, ETL definitions can handle complex data formats like JSON/XML payloads.
Key Outcomes
By utilizing robust import set transformers, ServiceNow customers can efficiently manage data integration tasks with improved performance and flexibility. This enables streamlined processing of data into target tables, reduces the need for multiple transform maps, and accommodates complex data structures, ultimately enhancing operational efficiency.
Use robust import set transformers instead of transform maps if you want to extract, transform, and load data to one or more target tables.
Robust import set transformers versus transform maps
Transform maps define the mapping from imported data stored in a staging table to a single target table in the ServiceNow AI Platform. Transform maps also insert data into target tables, performing both transform and processing functions. You can define multiple table mappings with multiple transform maps.
The Robust Transform Engine (RTE) and the robust import set transformer separate the transform and processing functions, providing a more flexible alternative to transform maps. The robust import set transformer allows you to extract data from a source table into an intermediary data structure. You can transform the data as desired and then load that data to one or more target tables. Records are processed as batches to enhance performance.
With transform maps, if you want data from the source table to go to three different target tables, you must create three separate transform maps. Each transform map parses the data separately, which results in three separate read operations. By contrast, the robust import set transformer requires only a single read operation to prepare the data for three target tables. The robust import set transformer is more efficient, especially when dealing with multiple target tables.
Extract Transform Load (ETL)
When you use the robust import set transformer, Extract Transform Load (ETL) functionality transfers imported data to target tables. You define how the data is extracted, transformed, and loaded to one or more target tables. You can use ETL definitions to do the following:
- Define entities (an abstraction similar to tables).
- Define entity fields (an abstraction similar to table fields).
- Define mapping between entities, and optionally designate whether a specific mapping should be ignored during data integration.
- Define entity field mappings.
- Define entity operations.
For an overview of ETL definitions, see Extract Transform Load (ETL) definition overview. For a step-by-step guide on how to create ETL definitions, see Create Extract Transform Load (ETL) definitions.
{
"records":[
{
"network":{
"location":"San Diego",
"computers":[
{
"id":"C100",
"os":"Mac",
"disks":[
{
"size":"200GB",
"type":"SSD"
},
{
"size":"1TB",
"type":"Magnetic"
},
{
"size":"1TB",
"type":"Magnetic"
}
]
},
{
"id":"C200",
"os":"Windows",
"disks":[
{
"size":"5TB",
"type":"Magnetic"
}
]
}
]
}
}
]
}