08e74665bb
Adds extract/openweathermap package with daily weather extraction for 8
coffee-growing regions (Brazil, Vietnam, Colombia, Ethiopia, Honduras,
Guatemala, Indonesia). Feeds crop stress signal for commodity sentiment score.
Extractor:
- OWM One Call API 3.0 / Day Summary — one JSON.gz per (location, date)
- extract_weather: daily, fetches yesterday + today (16 calls max)
- extract_weather_backfill: fills 2020-01-01 to yesterday, capped at 500
calls/run with resume cursor '{location_id}:{date}' for crash safety
- Full idempotency via file existence check; state tracking via extract_core
SQLMesh:
- seeds.weather_locations (8 regions with lat/lon/variety)
- foundation.fct_weather_daily: INCREMENTAL_BY_TIME_RANGE, grain
(location_id, observation_date), dedup via hash key, crop stress flags:
is_frost (<2°C), is_heat_stress (>35°C), is_drought (<1mm), in_growing_season
Landing path: LANDING_DIR/weather/{location_id}/{year}/{date}.json.gz
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Materia SQLMesh Transform Layer
Data transformation pipeline using SQLMesh and DuckDB, implementing a 3-layer architecture.
Quick Start
# From repo root
# Plan changes (dev environment)
uv run sqlmesh -p transform/sqlmesh_materia plan
# Apply to production
uv run sqlmesh -p transform/sqlmesh_materia plan prod
# Run model tests
uv run sqlmesh -p transform/sqlmesh_materia test
# Format SQL
uv run sqlmesh -p transform/sqlmesh_materia format
Architecture
3-Layer Data Model
landing/ ← immutable files (extraction output)
├── psd/{year}/{month}/ ← USDA PSD
├── cot/{year}/ ← CFTC COT
├── prices/coffee_kc/ ← KC=F daily prices
├── ice_stocks/ ← ICE daily warehouse stocks
├── ice_aging/ ← ICE monthly aging report
└── ice_stocks_by_port/ ← ICE historical EOM by port
staging/ ← read_csv + seed joins + cast (PSD)
└── staging.psdalldata__commodity
seeds/ ← static lookup CSVs (PSD code mappings)
├── seeds.psd_commodity_codes
├── seeds.psd_attribute_codes
└── seeds.psd_unit_of_measure_codes
foundation/ ← read_csv + cast + dedup (prices, COT, ICE)
├── foundation.fct_coffee_prices
├── foundation.fct_cot_positioning
├── foundation.fct_ice_warehouse_stocks
├── foundation.fct_ice_aging_stocks
├── foundation.fct_ice_warehouse_stocks_by_port
└── foundation.dim_commodity
serving/ ← pre-aggregated for web app
├── serving.coffee_prices
├── serving.cot_positioning
├── serving.ice_warehouse_stocks
├── serving.ice_aging_stocks
├── serving.ice_warehouse_stocks_by_port
└── serving.commodity_metrics
Layer responsibilities
staging/ — PSD only: reads landing CSVs directly via @psd_glob(), joins seed lookup tables, casts types, deduplicates. Uses INCREMENTAL_BY_TIME_RANGE (ingest_date derived from filename path).
seeds/ — Static lookup tables (commodity codes, attribute codes, unit of measure) loaded from seeds/*.csv. Referenced by staging.
foundation/ — All other sources (prices, COT, ICE): reads landing CSVs directly via glob macros, casts types, deduplicates. Uses INCREMENTAL_BY_TIME_RANGE. Also holds dim_commodity (the cross-source identity mapping).
serving/ — Analytics-ready aggregates consumed by the web app via analytics.duckdb. Pre-computes moving averages, COT indices, MoM changes. These are the only tables the web app reads.
Why no raw layer?
Landing files are immutable and content-addressed — the landing directory is the audit trail. A SQL raw layer would just duplicate file bytes into DuckDB with no added value. The first SQL layer reads directly from landing.
Environment Variables
| Variable | Default | Description |
|---|---|---|
LANDING_DIR |
data/landing |
Root of the landing zone |
DUCKDB_PATH |
local.duckdb |
DuckDB file (SQLMesh exclusive write access) |
The web app reads from a separate analytics.duckdb via export_serving.py.