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Structured data becomes useful when its meaning survives the journey. Make units, provenance, and change semantics part of the design—not footnotes.
Give the important decisions a name before choosing an implementation.
Write down whether a record represents current state or an operation that changes state. A quantity of seven means little until the consumer knows whether it is a total, an increment, or a correction. Keep event identity separate from entity identity, and define how repeated delivery affects the stored result.
Document the meaning of each field along with its type. An absent value, a null, and a zero should not become interchangeable through convenience defaults. Include explicit units where they matter, and test values at the accepted boundaries. A readable fixture can reveal ambiguity that a syntactically valid schema leaves unresolved.
Keep the source reference and transformation history needed to explain a derived record. When combining observations, retain the relationship to the inputs where your application needs it. A normalized category can coexist with the source’s original category. Preserving that distinction makes review and later correction easier than overwriting the evidence.
Create an ordinary record, a record with an unknown value, and a correction to an earlier record. Ask another developer to interpret each using only your documentation. A disagreement about units, deletion, or order identifies a contract gap worth fixing before the first production consumer. Keep these examples as permanent fixtures and run them whenever the schema or normalization code changes.
Create a feed contract that explains units, missing values, corrections, and deletions before those details become production surprises.
A project to think about: A catalog importer that can distinguish a price correction from a newly added product.
Open the complete guideExplore adjacent topics, then return to your own project with a clearer plan.