Propagates the conformed city key (city_slug) from dim_venues through the
full pricing pipeline, eliminating 3 fragile LOWER(TRIM(...)) fuzzy string
joins with deterministic key joins.
Changes (cascading, task-by-task):
- dim_venues: add city_slug computed column (REGEXP_REPLACE slug derivation)
- dim_venue_capacity: join foundation.dim_venues instead of stg_playtomic_venues;
carry city_slug alongside country_code/city
- fct_daily_availability: carry city_slug from dim_venue_capacity
- venue_pricing_benchmarks: carry city_slug from fct_daily_availability;
add to venue_stats GROUP BY and final SELECT/GROUP BY
- city_market_profile: join vpb on city_slug = city_slug (was LOWER(TRIM))
- planner_defaults: add city_slug to city_benchmarks CTE; join on city_slug
- pseo_city_pricing: join city_market_profile on city_slug (was LOWER(TRIM))
- pipeline_routes._DAG: dim_venue_capacity now depends on dim_venues, not stg_playtomic_venues
Result: dim_venues.city_slug → dim_cities.(country_code, city_slug) forms a
fully conformed geographic hierarchy with no fuzzy string comparisons.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- eurostat.py: add nama_10r_2hhinc dataset config; append filter params to
request URL so server pre-filters the large cube before download
- stg_regional_income.sql: new staging model — reads nama_10r_2hhinc.json.gz,
filters to NUTS-1 codes (3-char), normalises EL→GR / UK→GB
- dim_locations.sql: add admin1_to_nuts1 VALUES CTE (16 German Bundesländer)
+ regional_income CTE; final SELECT uses COALESCE(regional, country) income
- init_landing_seeds.py: add empty seed for nama_10r_2hhinc.json.gz
Munich/Bayern now scores ~29K PPS vs Chemnitz/Sachsen ~19K PPS instead of
both inheriting the same national average (~25.5K PPS).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Adds a coordinate-based population lookup as a fallback when string name
matching fails (~29% of cities). Uses bbox pre-filter (0.14° ≈ 15 km) then
ST_Distance_Sphere to find the nearest GeoNames location in the same country.
Fixes localization mismatches: Milano≠Milan, Wien≠Vienna, München≠Munich.
Population cascade: Eurostat EU > US Census > ONS UK > GeoNames string >
GeoNames spatial > 0.
Coverage: 70.5% → 98.5% (5,401 / 5,481 cities with population > 0).
Key cities before/after:
Wien: 0 → 1,691,468
Milano: 0 → 1,371,498
München: already matched by string; verified still correct at 1,488,719
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- dim_cities: add geoname_id to geonames_pop CTE and final SELECT
Creates FK between dim_cities (city-with-padel-venues) and dim_locations (all GeoNames),
enabling joins to location_opportunity_profile for the first time.
- city_market_profile: pass geoname_id through base CTE and final SELECT
- pseo_city_costs_de: LEFT JOIN location_opportunity_profile on (country_code, geoname_id),
add opportunity_score to output columns
- pseo_country_overview: add avg_opportunity_score, top_opportunity_score, top_opportunity_slugs,
top_opportunity_names aggregates
Cities with no GeoNames name match get opportunity_score = NULL; templates guard with
{% if opportunity_score %}.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Part A: Data Layer — Sprints 1-5
Sprint 1 — Eurostat SDMX city labels (unblocks EU population):
- New extractor: eurostat_city_labels.py — fetches ESTAT/CITIES codelist
(city_code → city_name mapping) with ETag dedup
- New staging model: stg_city_labels.sql — grain city_code
- Updated dim_cities.sql — joins Eurostat population via city code lookup;
replaces hardcoded 0::BIGINT population
Sprint 2 — Market score formula v2:
- city_market_profile.sql: 30pt population (LN/1M), 25pt income PPS (/200),
30pt demand (occupancy or density), 15pt data confidence
- Moved venue_pricing_benchmarks join into base CTE so median_occupancy_rate
is available to the scoring formula
Sprint 3 — US Census ACS extractor:
- New extractor: census_usa.py — ACS 5-year place population (vintage 2023)
- New staging model: stg_population_usa.sql — grain (place_fips, ref_year)
Sprint 4 — ONS UK extractor:
- New extractor: ons_uk.py — 2021 Census LAD population via ONS beta API
- New staging model: stg_population_uk.sql — grain (lad_code, ref_year)
Sprint 5 — GeoNames global extractor:
- New extractor: geonames.py — cities15000.zip bulk download, filtered to ≥50K pop
- New staging model: stg_population_geonames.sql — grain geoname_id
- dim_cities: 5-source population cascade (Eurostat > Census > ONS > GeoNames > 0)
with case/whitespace-insensitive city name matching
Registered all 4 new CLI entrypoints in pyproject.toml and all.py.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three deviations from the quart_saas_boilerplate methodology corrected:
1. Fix dim_cities LIKE join (data quality bug)
- Old: FROM eurostat_cities LEFT JOIN venue_counts LIKE '%country_code%'
→ cartesian product (2.6M rows vs ~5500 expected)
- New: FROM venue_cities (dim_venues) as primary table, Eurostat for
enrichment only. grain (country_code, city_slug).
- Also fixes REGEXP_REPLACE to LOWER() before regex so uppercase city
names aren't stripped to '-'
2. Rename fct_venue_capacity → dim_venue_capacity
- Static venue attributes with no time key are a dimension, not a fact
- No SQL logic changes; update fct_daily_availability reference
3. Add fct_availability_slot at event grain
- New: grain (snapshot_date, tenant_id, resource_id, slot_start_time)
- Recheck dedup logic moves here from fct_daily_availability
- fct_daily_availability now reads fct_availability_slot (cleaner DAG)
Downstream fixes:
- city_market_profile, planner_defaults grain → (country_code, city_slug)
- pseo_city_costs_de, pseo_city_pricing add city_key composite natural key
(country_slug || '-' || city_slug) to avoid URL collisions across countries
- planner_defaults join in pseo_city_costs_de uses both country_code + city_slug
- Templates updated: natural_key city_slug → city_key
Added transform/sqlmesh_padelnomics/CLAUDE.md documenting data modeling rules,
conformed dimension map, and source integration architecture.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Prerequisite for all pSEO serving models. Adds CASE-based country_name_en
and URL-safe country_slug to foundation.dim_cities, then selects them through
serving.city_market_profile so downstream models inherit them automatically.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three workstreams:
1. Playtomic full data extraction & transform pipeline:
- Expand venue bounding boxes from 4 to 23 regions (global coverage)
- New staging models for court resources, opening hours, and slot-level
availability with real prices from the Playtomic API
- Foundation fact tables for venue capacity and daily occupancy/revenue
- City-level pricing benchmarks replacing hardcoded country estimates
- Planner defaults now use 3-tier cascade: city data → country → fallback
2. Transactional email i18n:
- _t() helper in worker.py with ~70 translation keys (EN + DE)
- All 8 email handlers translated, lang passed in task payloads
3. Resend audiences restructured to 3 named audiences (free plan limit)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Eurostat JSON-stat format (4-7 dimension sparse dict with 583K values)
causes DuckDB OOM — pre-process in extractor to flat records.
Also fix dim_cities unused CTE bug and playtomic venue lat/lon path.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove raw/ layer — staging models now read landing JSON directly.
Rename all model schemas from padelnomics.* to staging.*/foundation.*/serving.*.
Web app queries updated to serving.planner_defaults via SERVING_DUCKDB_PATH.
Supervisor gets daily sleep interval between pipeline runs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Sync template from 29ac25b → v0.9.0 (29 template commits). Due to
template's _subdirectory migration, new files were manually rendered
rather than auto-merged by copier.
New files:
- .claude/CLAUDE.md + coding_philosophy.md (agent instructions)
- extract utils.py: SQLite state tracking for extraction runs
- extract/transform READMEs: architecture & pattern documentation
- infra/supervisor: systemd service + orchestration script
- Per-layer model READMEs (raw, staging, foundation, serving)
Also fixes copier-answers.yml (adds 4 feature toggles, removes stale
payment_provider key) and scopes CLAUDE.md gitignore to root only.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
git mv all tracked files from the nested padelnomics/ workspace
directory to the git repo root. Merged .gitignore files.
No code changes — pure path rename.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
