Data Management: Profiles, Maps, and Document Handling
What to Know
Data Management covers how Boomi represents, transforms, and validates data. The core concepts are profiles (JSON, XML, flat file, database, EDI), maps (transformation between profiles), functions (built-in transformations like string, date, math, and lookup functions), and document handling (split, combine, batch, and document cache). Candidates must understand profile types and their constraints: JSON profiles handle arrays and nested objects; XML profiles handle namespaces and attributes; flat file profiles use delimiters and fixed-width definitions.
Understand map functions and their categories, user-defined functions, and lookup (a lookup table component used in maps). Know document cache (stores documents during process execution for later retrieval) versus dynamic document properties (metadata on the document). Understand data process shapes such as cleanse, combine, split, and batch - and that batching groups documents for a single connector call.
Traps
A frequent trap is assuming a map can transform a JSON array directly into multiple documents - it cannot; you must split first. Another trap: confusing document cache with process property persistence. Watch for claims that a lookup table is updated at runtime by the map - lookups are read-only during map execution unless explicitly configured otherwise. Also watch for namespace handling errors in XML maps: missing namespace declarations cause mapping failures.
Rule of Thumb
If the source is a JSON array and the destination expects one document per element, split before mapping. If the destination connector needs a batch, combine or batch after mapping.
Worked Example
A process receives a JSON array of 50 orders and must insert each into a database. Use a split shape to produce 50 documents, then map each document to the database profile, then use a database connector operation per document. Do not map the array directly - the map expects a single document.