Data Pipeline Architecture Plan

Data becomes an asset only when it arrives reliably, in a known shape, at a predictable time. Until then it is a collection of exports somebody has to reconcile by hand every month.

This document designs the pipeline that moves your data from every source system into a single trusted destination. We inventory each source — databases, APIs, file drops, event streams, third-party platforms — and specify how each is ingested, on what schedule or trigger, and what happens when a source is late, malformed, or unavailable.

Transformation logic is documented stage by stage: cleaning, deduplication, type coercion, enrichment, and aggregation, with the business rules stated explicitly so results are reproducible and auditable. The storage design covers file or table format, partitioning strategy, and compression, chosen for how your data is actually queried.

Idempotency, retries, backfills, and late-arriving data are treated as first-class requirements, because in production they always are. The plan includes monitoring and data quality checks so failures surface immediately rather than at the end of the quarter.