Architecture Reference

Appearance

Data Pipelines and Analytics ​

Data Pipelines and Analytics Graphics Coverage ​

Primary chapter graphic: Data Lake Architecture Flow, Batch and Stream Processing Tradeoff. Accepted graphics: 2. Reviewed non-signal pages: 1. Open graphics in review: 0. QA status lives in graphics audit and visual review ledger.

Corpus pages: p. 125, p. 188-189, p. 351 Coverage: 4 pages; low-confidence extraction ranges: p. 351

This chapter is part of Marius's owned architecture build corpus. The text routes decisions; durable implementation signal is carried by accepted graphics, reviewed non-signal decisions, and the linked QA audit.

Chapter Visuals ​

Accepted graphics carry the canonical design signal for this chapter. Each selected source page is either accepted as a graphic or explicitly marked non-signal in the source-faithful ledger. Review and QA state live in visual inventory, visual review ledger, and graphics audit.

Data Lake Architecture Flow ​

Data Lake Architecture Flow

Batch and Stream Processing Tradeoff ​

Batch and Stream Processing Tradeoff

Open Review Queue ​

  • none

Reviewed Non-Signal Pages ​

  • Data Pipelines And Analytics: Stream + Database Map: source p. 125; batch 05; status non-signal/reviewed; ledger reason in visual-review-ledger.json

Use When ​

  • Operational data needs to become reports, dashboards, models, or recurring analytical artifacts.

Avoid When ​

  • The source data is not trusted or lacks ownership.

Core Model ​

  • Pipelines move data through raw, cleaned, modeled, and served states with freshness and quality contracts.
  • Prefer explicit ownership over accidental coupling. Every boundary should say who owns correctness, cost, data, recovery, and change.
  • Use corpus page pointers for inspection, and keep the chapter notes focused on reusable design decisions.

Implementation Guidance ​

  • Define source, ingestion mode, transformation rules, quality checks, backfill strategy, and serving model.
  • Write the smallest useful design note: purpose, inputs, outputs, state, failure behavior, observability, and rollback.
  • Choose the first implementation that can be tested against the real workflow without hiding a known production risk.

Tradeoffs ​

  • Batch jobs simplify correctness; streaming improves freshness at higher operational cost.
  • Centralization reduces duplicated work but can become a bottleneck when every team needs exceptions.
  • Specialized infrastructure helps at scale, but it must earn its operational cost.

Failure Modes ​

  • A dashboard becomes the only place where metric logic exists.
  • The diagram shows boxes but not ownership, retry behavior, data freshness, or user-visible failure.
  • The system has no proof path for the highest-risk assumption.

Decision Checklist ​

  • Track lineage, freshness, schema drift, failed loads, and backfill coverage.
  • Name the owner, source of truth, timeout, retry policy, and evidence that the path works.
  • Add one regression check for the failure mode most likely to recur.

Neutral Automation Examples ​

  • A sales dashboard materializes daily facts from operational events and labels every chart with its refresh time.
  • A neutral internal automation starts with fixtures, then adds credentials, permissions, and production scheduling only after the boundary is tested.
  • A customer-facing workflow keeps irreversible actions behind explicit approval until metrics show it is safe to automate further.