79f7fc6fad
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>
136 lines
4.5 KiB
SQL
136 lines
4.5 KiB
SQL
-- Per-city planner defaults for the financial calculator.
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-- When a user selects a city in the planner, these values pre-fill the inputs.
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-- Consumed by: padelnomics.planner.routes — /api/market-data endpoint.
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--
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-- 3-tier data cascade:
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-- 1. City-level: real pricing/occupancy from Playtomic availability snapshots
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-- 2. Country-level: median across cities in same country
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-- 3. Hardcoded fallback: market research estimates (only when no Playtomic data)
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--
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-- Units are explicit in column names. Monetary values in local currency.
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MODEL (
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name serving.planner_defaults,
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kind FULL,
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cron '@daily',
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grain city_slug
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);
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WITH -- Real city-level benchmarks from Playtomic
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city_benchmarks AS (
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SELECT
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country_code,
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city,
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median_peak_rate,
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median_offpeak_rate,
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median_occupancy_rate,
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median_daily_revenue_per_venue,
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median_court_count,
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venue_count,
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total_venue_days_observed,
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price_currency
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FROM serving.venue_pricing_benchmarks
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),
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-- Country-level medians (fallback when a city has no availability data)
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country_benchmarks AS (
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SELECT
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country_code,
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MEDIAN(median_peak_rate) AS median_peak_rate,
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MEDIAN(median_offpeak_rate) AS median_offpeak_rate,
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MEDIAN(median_occupancy_rate) AS median_occupancy_rate,
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MEDIAN(median_court_count) AS median_court_count,
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SUM(venue_count) AS total_venues,
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MIN(price_currency) AS price_currency
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FROM city_benchmarks
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GROUP BY country_code
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),
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-- Hardcoded global fallbacks (only for countries with zero Playtomic coverage)
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hardcoded_fallbacks AS (
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SELECT * FROM (VALUES
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('DE', 22.0, 16.5, 0.55, 4, 'EUR'),
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('ES', 16.0, 12.0, 0.62, 6, 'EUR'),
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('GB', 24.0, 18.0, 0.52, 4, 'GBP'),
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('FR', 18.0, 13.5, 0.58, 5, 'EUR'),
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('IT', 15.0, 11.0, 0.60, 6, 'EUR'),
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('PT', 12.0, 9.0, 0.65, 6, 'EUR'),
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('AT', 20.0, 15.0, 0.54, 4, 'EUR'),
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('CH', 28.0, 21.0, 0.50, 4, 'CHF'),
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('NL', 20.0, 15.0, 0.56, 4, 'EUR'),
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('BE', 18.0, 13.5, 0.57, 4, 'EUR'),
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('SE', 22.0, 16.5, 0.50, 4, 'SEK'),
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('US', 20.0, 15.0, 0.58, 6, 'USD'),
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('MX', 12.0, 9.0, 0.55, 4, 'MXN'),
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('AR', 10.0, 7.5, 0.60, 4, 'ARS'),
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('DK', 24.0, 18.0, 0.48, 4, 'DKK'),
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('NO', 26.0, 19.5, 0.45, 4, 'NOK'),
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('FI', 22.0, 16.5, 0.48, 4, 'EUR')
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) AS t(country_code, peak_rate, offpeak_rate, occupancy, courts, currency)
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),
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city_profiles AS (
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SELECT
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city_slug,
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country_code,
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city_name,
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padel_venue_count,
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population,
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market_score,
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venues_per_100k
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FROM serving.city_market_profile
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)
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SELECT
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cp.city_slug,
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cp.country_code,
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cp.city_name,
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cp.padel_venue_count,
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cp.population,
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cp.market_score,
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-- Peak rate: city → country → hardcoded
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ROUND(COALESCE(
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cb.median_peak_rate,
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ctb.median_peak_rate,
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hf.peak_rate
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), 2) AS rate_peak,
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-- Off-peak rate
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ROUND(COALESCE(
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cb.median_offpeak_rate,
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ctb.median_offpeak_rate,
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hf.offpeak_rate
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), 2) AS rate_off_peak,
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-- Occupancy (utilisation)
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ROUND(COALESCE(
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cb.median_occupancy_rate,
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ctb.median_occupancy_rate,
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hf.occupancy
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), 4) AS avg_utilisation_pct,
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-- Typical court count
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COALESCE(
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cb.median_court_count,
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ctb.median_court_count,
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hf.courts
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) AS courts_typical,
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-- Revenue estimate (city-level only)
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cb.median_daily_revenue_per_venue AS daily_revenue_per_venue,
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-- Data provenance
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CASE
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WHEN cb.venue_count IS NOT NULL THEN 'city_data'
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WHEN ctb.total_venues IS NOT NULL THEN 'country_data'
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ELSE 'hardcoded'
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END AS data_source,
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CASE
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WHEN cb.total_venue_days_observed >= 100 THEN 1.0
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WHEN cb.total_venue_days_observed >= 30 THEN 0.8
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WHEN cb.venue_count IS NOT NULL THEN 0.6
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WHEN ctb.total_venues IS NOT NULL THEN 0.4
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ELSE 0.2
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END AS data_confidence,
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COALESCE(cb.price_currency, ctb.price_currency, hf.currency, 'EUR') AS price_currency,
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CURRENT_DATE AS refreshed_date
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FROM city_profiles cp
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LEFT JOIN city_benchmarks cb
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ON cp.country_code = cb.country_code
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AND LOWER(TRIM(cp.city_name)) = LOWER(TRIM(cb.city))
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LEFT JOIN country_benchmarks ctb
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ON cp.country_code = ctb.country_code
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LEFT JOIN hardcoded_fallbacks hf
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ON cp.country_code = hf.country_code
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