walkeros-understanding-mapping

walkerOS event data transformation uses structured mapping rules to align entity-action pairs and normalize data across source, collector, and destination flows. Patterns like key, value, map, loop, and condition enable consistent event transformations.

Yes, JSON mapping configurations in walkerOS support executable functions via the $code: prefix. This enables advanced data transformations directly within config files, allowing dynamic processing logic for complex event mapping workflows.

walkerOS event mapping handles consent through global and per-rule policy controls that enforce data governance. Pre-processing rules and consent-gated extractions filter data based on user consent before it reaches the destination.

Mapping nested product data into standardized events uses the loop pattern within walkerOS mapping rules. This transforms source events like product views into destination events such as view_item with nested items arrays.

walkerOS event mapping includes scenario-based tests for common flows like source-to-collector and collector-to-destination. These tests validate mapping configurations and ensure data transformation accuracy across the entire event flow pipeline.

The $code: prefix in JSON mapping configurations enables advanced transformations but requires careful policy handling. Executable functions must align with global pre-processing rules and consent-gated extraction policies to maintain data governance compliance.