(04) Systems
Systems & architecture
Projects built as bridges toward Data Engineering.
Multi-user personal finance platform: a layered Rust workspace (pure domain, SQLx data layer, Axum API, background worker) over PostgreSQL, serving an Expo app for iOS, Android and web plus a Tauri desktop shell.
Engineering decisions
- —Hexagonal crate split: an I/O-free domain checked in CI
- —Row-level security scoped on every pooled connection
- —Transactional outbox for push notifications, SKIP LOCKED job claims
- —Rotating refresh tokens, rate-limited login, typed error codes
- —Prometheus metrics, OTLP traces, health probes, graceful shutdown
- —Playwright E2E on the real stack, k6 load tests, compile-time checked SQL
Topology
Expo · Tauri
Mobile, web & desktop
Rust · Axum
Typed HTTP API, JWT
Rust · Tokio
Scheduled jobs
PostgreSQL 18
Triggers, RLS, analytics
Private source — walkthrough on request→Data bridge
Integrity enforced in the database: balances maintained only by triggers, row-level security per user, SQL analytics functions for trends and budgets, idempotent imports of third-party exports.
End-to-end data platform for Emilia-Romagna air quality on ARPAE open data, enriched with Open-Meteo weather: Rust ingestion into partitioned Parquet, dbt models on DuckDB locally and BigQuery on GCP, Dagster orchestration. Work in progress: data exploration is done.
Engineering decisions
- —Idempotent incremental ingestion: rolling-window reprocessing and upserts on the natural key
- —Layered dbt models (staging, intermediate, marts) with SCD Type 2 station history
- —Data quality as code: uniqueness, completeness and freshness tests
- —Completeness measured against an expected time grid, never filled in raw
- —Legal limits in a dbt seed, never hardcoded
- —Same models on DuckDB and BigQuery, infrastructure via Terraform
Topology
Rust
ARPAE and Open-Meteo, idempotent
Parquet
Raw partitioned by year/month
dbt · DuckDB · BigQuery
Staging, intermediate, marts
Dagster
Schedules, backfills, lineage
Repository↗Data bridge
A pure data engineering project: provisional measurements revised after validation, gaps and inconsistent formats in the source, turned into a historized, tested dataset that answers legal-limit exceedances, pollutant trends and weather correlation.