Observed Signal · Jul 6, 2026 · Technical Guide · Source: DEV Community · Impact: 1/5 · Sentiment: Positive
Normalize Data from 33 Disparate Sources
A developer post from Multita describes a practical approach to normalizing heterogeneous public data (HTML, hidden JSON, PDFs) collected from 33 Argentine government systems. The recommended pattern is to define a single output schema (an "output contract") and implement one adapter/translator per source that converts each source's fields and formats (dates, amounts, status labels) into the canonical schema. Benefits include isolating parsing changes to a single adapter, simplifying business logic, and making it easy to add new sources without touching core logic. The article gives concrete normalization choices (ISO dates, integer pesos, a small controlled set of status values) and advises choosing the output format before writing scrapers.
Practical engineering guide about scraping and canonicalizing heterogeneous public data; useful for data engineers but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Multita aggregated traffic-fine data from more than 33 official Argentine systems.
- They enforce a single output schema and implement one adapter (translator) per source to map disparate source formats into that schema.
- Canonical normalization rules include ISO-formatted dates, integer peso amounts, and a reduced set of standardized status values (deuda, pago_voluntario, en_juzgado, sin_deuda).
- The approach isolates parsing/scraping complexity inside adapters so business logic only depends on the canonical schema.
Connected Companies & Entities
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Ontology Mapping & Concepts
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