Data Fetcher Agent — Connecting to Snowflake FEATURE_WH
Generating optimized multi-table JOIN query:
SELECT
g.seller_id, g.city_tier,
g.poi_count_500m, g.poi_count_1km, g.poi_count_3km,
g.avg_distance_to_highway_km, g.residential_density_score,
g.commercial_zone_flag,
e.avg_listing_views_30d, e.avg_response_time_hrs,
e.conversion_rate_30d, e.active_listings_count, e.reply_rate_7d,
d.avg_household_income_proxy, d.population_density_per_sqkm,
l.llm_area_quality_score, l.llm_area_tier, l.llm_poi_sentiment
FROM FEATURE_MART.SELLER_GEO_FEATURES g
INNER JOIN FEATURE_MART.SELLER_ENGAGEMENT_FEATURES e
ON g.seller_id = e.seller_id
AND g.feature_month = DATE_TRUNC('MONTH', e.feature_week_start)
LEFT JOIN FEATURE_MART.CITY_DEMOGRAPHICS_FEATURES d
ON g.city_id = d.city_id
AND g.feature_quarter = d.feature_quarter
LEFT JOIN FEATURE_MART.LLM_LOCATION_FEATURES l
ON g.seller_id = l.seller_id
AND g.feature_month = l.feature_month
WHERE g.city_tier IN (2, 3)
AND g.feature_month = DATE_TRUNC('MONTH', CURRENT_DATE())
AND g.seller_id IS NOT NULL
Geo↔Engagement join:
98.7%
📊 Base feature set assembled: 67,430 rows × 868 features | Overall non-null: 99.1%
Handing off to Feature Engineer Agent for validation & enrichment →