c3c8333407
- Delete 6 data raw models (coffee_prices, cot_disaggregated, ice_*, psd_data) — pure read_csv passthroughs with no added value - Move 3 PSD seed models raw/ → seeds/, rename schema raw.* → seeds.* - Update staging.psdalldata__commodity: read_csv(@psd_glob()) directly, join seeds.psd_* instead of raw.psd_* - Update 5 foundation models: inline read_csv() with src CTE, removing raw.* dependency (fct_coffee_prices, fct_cot_positioning, fct_ice_*) - Remove fixture-based SQLMesh test that depended on raw.cot_disaggregated (unit tests incompatible with inline read_csv; integration run covers this) - Update readme.md: 3-layer architecture (staging/foundation → serving) Landing files are immutable and content-addressed — the landing directory is the audit trail. A raw SQL layer duplicated file bytes into DuckDB with no added value. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
83 lines
3.2 KiB
Markdown
83 lines
3.2 KiB
Markdown
# Materia SQLMesh Transform Layer
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Data transformation pipeline using SQLMesh and DuckDB, implementing a 3-layer architecture.
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## Quick Start
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```bash
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# From repo root
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# Plan changes (dev environment)
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uv run sqlmesh -p transform/sqlmesh_materia plan
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# Apply to production
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uv run sqlmesh -p transform/sqlmesh_materia plan prod
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# Run model tests
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uv run sqlmesh -p transform/sqlmesh_materia test
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# Format SQL
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uv run sqlmesh -p transform/sqlmesh_materia format
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```
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## Architecture
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### 3-Layer Data Model
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```
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landing/ ← immutable files (extraction output)
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├── psd/{year}/{month}/ ← USDA PSD
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├── cot/{year}/ ← CFTC COT
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├── prices/coffee_kc/ ← KC=F daily prices
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├── ice_stocks/ ← ICE daily warehouse stocks
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├── ice_aging/ ← ICE monthly aging report
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└── ice_stocks_by_port/ ← ICE historical EOM by port
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staging/ ← read_csv + seed joins + cast (PSD)
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└── staging.psdalldata__commodity
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seeds/ ← static lookup CSVs (PSD code mappings)
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├── seeds.psd_commodity_codes
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├── seeds.psd_attribute_codes
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└── seeds.psd_unit_of_measure_codes
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foundation/ ← read_csv + cast + dedup (prices, COT, ICE)
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├── foundation.fct_coffee_prices
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├── foundation.fct_cot_positioning
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├── foundation.fct_ice_warehouse_stocks
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├── foundation.fct_ice_aging_stocks
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├── foundation.fct_ice_warehouse_stocks_by_port
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└── foundation.dim_commodity
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serving/ ← pre-aggregated for web app
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├── serving.coffee_prices
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├── serving.cot_positioning
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├── serving.ice_warehouse_stocks
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├── serving.ice_aging_stocks
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├── serving.ice_warehouse_stocks_by_port
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└── serving.commodity_metrics
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```
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### Layer responsibilities
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**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).
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**seeds/** — Static lookup tables (commodity codes, attribute codes, unit of measure) loaded from `seeds/*.csv`. Referenced by staging.
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**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).
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**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.
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### Why no raw layer?
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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.
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## Environment Variables
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| Variable | Default | Description |
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|----------|---------|-------------|
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| `LANDING_DIR` | `data/landing` | Root of the landing zone |
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| `DUCKDB_PATH` | `local.duckdb` | DuckDB file (SQLMesh exclusive write access) |
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The web app reads from a separate `analytics.duckdb` via `export_serving.py`.
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