merge(pipeline-lineage): conform geographic dimension hierarchy via city_slug
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@@ -34,6 +34,7 @@ SELECT
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v.tenant_id,
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v.country_code,
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v.city,
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v.city_slug,
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cc.active_court_count,
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ROUND(wh.hours_open_per_week, 1) AS hours_open_per_week,
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ROUND(wh.avg_hours_open_per_day, 1) AS avg_hours_open_per_day,
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@@ -42,6 +43,6 @@ SELECT
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ROUND(cc.active_court_count * wh.avg_hours_open_per_day, 1) AS capacity_court_hours_per_day,
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-- Total bookable court-hours per week
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ROUND(cc.active_court_count * wh.hours_open_per_week, 1) AS capacity_court_hours_per_week
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FROM staging.stg_playtomic_venues v
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FROM foundation.dim_venues v
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JOIN court_counts cc ON v.tenant_id = cc.tenant_id
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JOIN weekly_hours wh ON v.tenant_id = wh.tenant_id
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@@ -98,6 +98,8 @@ SELECT
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court_count,
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indoor_court_count,
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outdoor_court_count,
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-- Conformed city key: enables deterministic joins to dim_cities / venue_pricing_benchmarks
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LOWER(REGEXP_REPLACE(LOWER(COALESCE(city, '')), '[^a-z0-9]+', '-')) AS city_slug,
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extracted_date
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FROM ranked
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QUALIFY ROW_NUMBER() OVER (
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@@ -44,6 +44,7 @@ SELECT
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sa.tenant_id,
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cap.country_code,
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cap.city,
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cap.city_slug,
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cap.active_court_count,
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cap.capacity_court_hours_per_day,
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sa.available_slot_count,
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@@ -57,7 +57,7 @@ WITH base AS (
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FROM foundation.dim_cities c
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LEFT JOIN serving.venue_pricing_benchmarks vpb
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ON c.country_code = vpb.country_code
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AND LOWER(TRIM(c.city_name)) = LOWER(TRIM(vpb.city))
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AND c.city_slug = vpb.city_slug
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WHERE c.padel_venue_count > 0
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),
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scored AS (
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@@ -21,6 +21,7 @@ city_benchmarks AS (
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SELECT
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country_code,
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city,
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city_slug,
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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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@@ -128,7 +129,7 @@ SELECT
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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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AND cp.city_slug = cb.city_slug
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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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@@ -41,6 +41,6 @@ FROM serving.venue_pricing_benchmarks vpb
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-- Join city_market_profile to get the canonical city_slug and country metadata
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INNER JOIN serving.city_market_profile c
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ON vpb.country_code = c.country_code
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AND LOWER(TRIM(vpb.city)) = LOWER(TRIM(c.city_name))
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AND vpb.city_slug = c.city_slug
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-- Only cities with enough venues for meaningful pricing statistics
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WHERE vpb.venue_count >= 2
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@@ -17,6 +17,7 @@ WITH venue_stats AS (
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da.tenant_id,
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da.country_code,
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da.city,
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da.city_slug,
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da.price_currency,
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AVG(da.occupancy_rate) AS avg_occupancy_rate,
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MEDIAN(da.median_price) AS median_hourly_rate,
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@@ -29,12 +30,13 @@ WITH venue_stats AS (
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WHERE TRY_CAST(da.snapshot_date AS DATE) >= CURRENT_DATE - INTERVAL '30 days'
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AND da.occupancy_rate IS NOT NULL
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AND da.occupancy_rate BETWEEN 0 AND 1.5
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GROUP BY da.tenant_id, da.country_code, da.city, da.price_currency
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GROUP BY da.tenant_id, da.country_code, da.city, da.city_slug, da.price_currency
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HAVING COUNT(DISTINCT da.snapshot_date) >= 3
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)
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SELECT
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country_code,
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city,
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city_slug,
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price_currency,
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COUNT(*) AS venue_count,
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-- Pricing benchmarks
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@@ -54,4 +56,4 @@ SELECT
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SUM(days_observed) AS total_venue_days_observed,
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CURRENT_DATE AS refreshed_date
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FROM venue_stats
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GROUP BY country_code, city, price_currency
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GROUP BY country_code, city, city_slug, price_currency
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@@ -100,7 +100,7 @@ _DAG: dict[str, list[str]] = {
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"stg_regional_income", "stg_income_usa", "stg_padel_courts", "stg_tennis_courts",
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],
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"dim_venue_capacity": [
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"stg_playtomic_venues", "stg_playtomic_resources", "stg_playtomic_opening_hours",
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"dim_venues", "stg_playtomic_resources", "stg_playtomic_opening_hours",
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],
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"fct_availability_slot": ["stg_playtomic_availability"],
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"fct_daily_availability": ["fct_availability_slot", "dim_venue_capacity"],
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