ENGINEERING CASE STUDY / 03

Enterprise Intelligence Automation Platform — n8n

An event-driven data-to-decision pipeline connecting ingestion, quality quarantine, dimensional analytics, BI refresh orchestration and operational recovery.

Local production-style project · Evidence reviewed 2026-10-29

Project overview

Local engineering portfolio. An event-driven data-to-decision pipeline connecting ingestion, quality quarantine, dimensional analytics, BI refresh orchestration and operational recovery. The scope is documented below so reviewers can distinguish delivered capabilities from design targets.

The engineering challenge

Fragile ingestion scripts can replay transactions, hide invalid records and leave reporting failures without a clear recovery path.

Solution architecture

Eight n8n workflows coordinate a FastAPI ingress, PostgreSQL warehouse, validation quarantine, refresh adapters and numerically grounded reporting.

01 / ARCHITECTURE

FastAPI validates ingress and batch fingerprints before orchestration.

02 / ARCHITECTURE

n8n coordinates ingestion, precedence-based quality rules, transactional ETL, monitoring and recovery; PostgreSQL holds dimensional and operational state.

03 / ARCHITECTURE

Power BI and AI adapters default to local mock/template modes. Mailpit provides local email inspection without requiring cloud subscriptions.

Implementation

  • SHA-256 batch fingerprinting and replay protection.
  • Bad-record quarantine, dimensional upserts and operational monitoring.
  • Bounded retries, dead-letter isolation and recovery validation.
  • Grounded reporting with numerical validation and an adapter-based Power BI refresh workflow.

Visual gallery

Each asset is classified and linked to its source. Design references are not presented as completed application screens.

Data-ingestion architecture derived from actual repository workflow nodes and connections; not an n8n canvas screenshot
Architecture diagramData-ingestion architecture derived from actual repository workflow nodes and connections; not an n8n canvas screenshot
View original source ↗

Technical evidence

The repository documents eight implemented workflows, 51 passing tests and a 1,792 rows/second local synthetic-data benchmark.

Reviewed from public interfaces and repository documentation on 29 October 2026. Upstream tests and benchmarks were not rerun as part of this website implementation.

Limitations & scope

  • Local Docker project using deterministic synthetic data. It is not a deployed enterprise cloud service.
  • Default Power BI and AI integrations are mock/template adapters; configured cloud integrations were not verified here.
  • README-linked canvas captures show account setup instead of workflows. They are excluded; the published diagram derives from workflow JSON and is explicitly labeled.
  • The 51 tests and 1,792 rows/s benchmark are repository-reported evidence from a developer workstation, not tests rerun by this website or cloud SLAs.

Technology stack

  • n8n
  • FastAPI
  • Python
  • PostgreSQL
  • Docker
  • Power BI integration

Explore the evidence & next steps