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product recommendations in real-time to drive conversion, increase average order value, and improve customer satisfaction. This quickstart guide walks through building a complete real-time recommendation engine powered entirely by Snowflake.\u003C/p\u003E\n","\u003Cp\u003EYou will build a hybrid batch + real-time ML pipeline that:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGenerates synthetic retail customer, event, product catalog, and inventory data\u003C/li\u003E\u003Cli\u003EBuilds a Customer 360 gold layer using Dynamic Tables\u003C/li\u003E\u003Cli\u003ERegisters features in the Snowflake Feature Store for training data retrieval\u003C/li\u003E\u003Cli\u003ETrains two XGBoost models: a batch propensity model (daily) and a real-time product ranking model\u003C/li\u003E\u003Cli\u003EDeploys the ranking model to Snowpark Container Services (SPCS) for real-time inference\u003C/li\u003E\u003Cli\u003ESets up Snowflake Postgres for low-latency operational serving of features, propensity scores, and inventory\u003C/li\u003E\u003Cli\u003EBuilds a FastAPI orchestrator service that fetches data from Postgres, calls the ranking model, and returns top-N recommendations\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe architecture follows a pattern discussed in the context of retail Customer 360: the intersection of customer intelligence and product catalog data, positioned as a precursor to agentic commerce.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExtension Note:\u003C/strong\u003E The availability/delivery estimation step in this quickstart uses a deterministic SQL filter on inventory. For retailers with real logistics data (carrier performance, distance to warehouse, seasonal demand), this filter can be replaced with a CatBoost delivery estimation model to boost customer satisfaction and reduce returns.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Enterprise Edition account\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role or privileges to create databases, warehouses, compute pools, image repositories, services, and Postgres instances\u003C/li\u003E\u003Cli\u003ESPCS enabled on your account\u003C/li\u003E\u003Cli\u003ESnowflake Postgres enabled on your account\u003C/li\u003E\u003Cli\u003EBasic knowledge of Python and ML concepts\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to generate synthetic retail data using Snowflake UDTFs\u003C/li\u003E\u003Cli\u003EHow to build a Customer 360 gold layer with Dynamic Tables\u003C/li\u003E\u003Cli\u003EHow to use the Snowflake Feature Store for offline training data retrieval\u003C/li\u003E\u003Cli\u003EHow to train and register ML models in the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003EHow to deploy a model to SPCS and build a FastAPI orchestrator for real-time recommendations\u003C/li\u003E\u003Cli\u003EHow to use Snowflake Postgres for low-latency operational serving\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://snowflake.com\"\u003ESnowflake\u003C/a\u003E Enterprise Edition account\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.docker.com/\"\u003EDocker\u003C/a\u003E (for building and pushing the orchestrator container)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EReal-time product recommendation API powered by Snowflake ML\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture Overview\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EThe solution uses a hybrid batch + real-time architecture:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBatch Path (Daily):\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECustomer 360 Dynamic Table aggregates raw events into customer features\u003C/li\u003E\u003Cli\u003EPropensity model scores each customer's affinity per product category\u003C/li\u003E\u003Cli\u003EResults are stored in the GOLD schema\u003C/li\u003E\u003Cli\u003ECustomer features, propensity scores, and inventory are synced to Snowflake Postgres for low-latency serving\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReal-Time Path (Per Request):\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOrchestrator receives a recommendation request with \u003Ccode\u003Ecustomer_id\u003C/code\u003E and \u003Ccode\u003Ezip_code\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECustomer features, propensity scores, and inventory are fetched from Snowflake Postgres in parallel (~15ms)\u003C/li\u003E\u003Cli\u003ECustomer-product pair features are computed and sent to the ranking model service\u003C/li\u003E\u003Cli\u003ETop-N recommendations are returned with product details and estimated delivery\u003C/li\u003E\u003Cli\u003EFalls back to Snowflake warehouse queries if Postgres is not configured\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Design Decisions:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECustomer propensity is batch because it changes slowly (loyalty tier, purchase history)\u003C/li\u003E\u003Cli\u003EProduct ranking is real-time because it depends on current inventory and request context\u003C/li\u003E\u003Cli\u003EInventory availability is deterministic (SQL filter) rather than ML-based\u003C/li\u003E\u003Cli\u003EXGBoost is used for speed — lightweight tree models keep inference under 200ms\u003C/li\u003E\u003Cli\u003ESnowflake Postgres provides single-digit-millisecond lookups for operational serving, reducing end-to-end latency from ~850ms to ~80ms\u003C/li\u003E\u003Cli\u003ECustomer features and propensity scores are kept in separate Postgres tables due to different cardinality (1 row/customer vs 12 rows/customer), query patterns, and refresh cadences\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting Up Infrastructure\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EConnect to Snowflake and run the following SQL to create the database, schemas, warehouse, role, compute pool, and image repository.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Database and schemas\nCREATE OR REPLACE DATABASE RETAIL_RECOMMENDATION_QS;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.RAW;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.GOLD;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY;\n\n-- Warehouse\nCREATE OR REPLACE WAREHOUSE RETAIL_QS_WH\n  WITH WAREHOUSE_SIZE = 'MEDIUM'\n  AUTO_SUSPEND = 120\n  AUTO_RESUME = TRUE;\n\n-- Role\nCREATE OR REPLACE ROLE RETAIL_QS_ROLE;\nGRANT USAGE ON DATABASE RETAIL_RECOMMENDATION_QS TO ROLE RETAIL_QS_ROLE;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE RETAIL_RECOMMENDATION_QS TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.GOLD TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY TO ROLE RETAIL_QS_ROLE;\nGRANT USAGE ON WAREHOUSE RETAIL_QS_WH TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE DYNAMIC TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.GOLD TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE DYNAMIC TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.GOLD TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE MODEL ON SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE FUNCTION ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT ROLE RETAIL_QS_ROLE TO ROLE ACCOUNTADMIN;\n\n-- Compute pool for SPCS\nCREATE COMPUTE POOL IF NOT EXISTS RETAIL_QS_COMPUTE_POOL\n  MIN_NODES = 1\n  MAX_NODES = 3\n  INSTANCE_FAMILY = CPU_X64_S;\n\nGRANT USAGE ON COMPUTE POOL RETAIL_QS_COMPUTE_POOL TO ROLE RETAIL_QS_ROLE;\nGRANT MONITOR ON COMPUTE POOL RETAIL_QS_COMPUTE_POOL TO ROLE RETAIL_QS_ROLE;\n\n-- Image repository\nCREATE IMAGE REPOSITORY IF NOT EXISTS RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_IMAGES;\nGRANT READ ON IMAGE REPOSITORY RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_IMAGES TO ROLE RETAIL_QS_ROLE;\n\n-- Stage for orchestrator spec\nCREATE STAGE IF NOT EXISTS RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_STAGE;\nGRANT ALL ON STAGE RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_STAGE TO ROLE RETAIL_QS_ROLE;\n\n-- SPCS permissions\nGRANT BIND SERVICE ENDPOINT ON ACCOUNT TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE SERVICE ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE SERVICE ON SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY TO ROLE RETAIL_QS_ROLE;\n\nSHOW IMAGE REPOSITORIES IN SCHEMA RETAIL_RECOMMENDATION_QS.RAW;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote the \u003Ccode\u003Erepository_url\u003C/code\u003E from the last command — you will need it when pushing the orchestrator container later.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating Reference Tables\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003EThese reference tables provide the foundation for synthetic data generation: store locations, product categories, loyalty tiers, and promotions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE RETAIL_QS_ROLE;\nUSE DATABASE RETAIL_RECOMMENDATION_QS;\nUSE SCHEMA RAW;\nUSE WAREHOUSE RETAIL_QS_WH;\n\n-- Store locations\nCREATE OR REPLACE TABLE STORES AS\nSELECT\n  SEQ4() AS STORE_ID,\n  CONCAT('Store_', LPAD(SEQ4()::STRING, 4, '0')) AS STORE_CODE,\n  CASE (ABS(HASH(SEQ4())) % 4)\n    WHEN 0 THEN 'Flagship' WHEN 1 THEN 'Standard'\n    WHEN 2 THEN 'Outlet' ELSE 'Express' END AS STORE_TYPE,\n  CASE (ABS(HASH(SEQ4(), 1)) % 10)\n    WHEN 0 THEN 'New York' WHEN 1 THEN 'Los Angeles' WHEN 2 THEN 'Chicago'\n    WHEN 3 THEN 'Houston' WHEN 4 THEN 'Phoenix' WHEN 5 THEN 'Dallas'\n    WHEN 6 THEN 'Seattle' WHEN 7 THEN 'Miami' WHEN 8 THEN 'Atlanta'\n    ELSE 'Denver' END AS CITY,\n  CASE (ABS(HASH(SEQ4(), 1)) % 10)\n    WHEN 0 THEN 'NY' WHEN 1 THEN 'CA' WHEN 2 THEN 'IL'\n    WHEN 3 THEN 'TX' WHEN 4 THEN 'AZ' WHEN 5 THEN 'TX'\n    WHEN 6 THEN 'WA' WHEN 7 THEN 'FL' WHEN 8 THEN 'GA'\n    ELSE 'CO' END AS STATE,\n  CASE (ABS(HASH(SEQ4(), 1)) % 10)\n    WHEN 0 THEN 'Northeast' WHEN 1 THEN 'West' WHEN 2 THEN 'Midwest'\n    WHEN 3 THEN 'South' WHEN 4 THEN 'West' WHEN 5 THEN 'South'\n    WHEN 6 THEN 'West' WHEN 7 THEN 'Southeast' WHEN 8 THEN 'Southeast'\n    ELSE 'West' END AS REGION,\n  CASE (ABS(HASH(SEQ4(), 2)) % 5)\n    WHEN 0 THEN 'Mall' WHEN 1 THEN 'Strip Center'\n    WHEN 2 THEN 'Standalone' WHEN 3 THEN 'Downtown'\n    ELSE 'Suburban' END AS LOCATION_TYPE,\n  (ABS(HASH(SEQ4(), 3)) % 30000 + 5000) AS SQUARE_FEET\nFROM TABLE(GENERATOR(ROWCOUNT => 500));\n\n-- Product categories\nCREATE OR REPLACE TABLE PRODUCT_CATEGORIES AS\nSELECT * FROM VALUES\n  ('CAT-001', 'Apparel', 'Womens Clothing', 25.00, 250.00),\n  ('CAT-002', 'Apparel', 'Mens Clothing', 30.00, 200.00),\n  ('CAT-003', 'Apparel', 'Kids Clothing', 15.00, 80.00),\n  ('CAT-004', 'Footwear', 'Athletic Shoes', 60.00, 200.00),\n  ('CAT-005', 'Footwear', 'Casual Shoes', 40.00, 150.00),\n  ('CAT-006', 'Accessories', 'Bags & Wallets', 30.00, 400.00),\n  ('CAT-007', 'Accessories', 'Jewelry', 20.00, 500.00),\n  ('CAT-008', 'Home', 'Bedding', 50.00, 300.00),\n  ('CAT-009', 'Home', 'Kitchen', 15.00, 200.00),\n  ('CAT-010', 'Beauty', 'Skincare', 15.00, 150.00),\n  ('CAT-011', 'Beauty', 'Makeup', 10.00, 80.00),\n  ('CAT-012', 'Electronics', 'Audio', 30.00, 400.00)\nAS c(CATEGORY_ID, DEPARTMENT, CATEGORY_NAME, MIN_PRICE, MAX_PRICE);\n\n-- Loyalty tiers\nCREATE OR REPLACE TABLE LOYALTY_TIERS AS\nSELECT * FROM VALUES\n  ('TIER-001', 'Bronze', 0, 499, 1.0, 0),\n  ('TIER-002', 'Silver', 500, 1499, 1.25, 5),\n  ('TIER-003', 'Gold', 1500, 4999, 1.5, 10),\n  ('TIER-004', 'Platinum', 5000, 99999, 2.0, 15)\nAS t(TIER_ID, TIER_NAME, MIN_ANNUAL_SPEND, MAX_ANNUAL_SPEND, POINTS_MULTIPLIER, DISCOUNT_PCT);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating the Product Catalog\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003EThe product catalog contains 2,000 products across the 12 categories defined above. Each product has a name, brand, price, and category assignment. This UDTF generates realistic retail product data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE SCHEMA RAW;\n\nCREATE OR REPLACE FUNCTION GENERATE_PRODUCT(SEED INT)\nRETURNS TABLE (\n  PRODUCT_ID NUMBER,\n  PRODUCT_NAME STRING,\n  CATEGORY_ID STRING,\n  DEPARTMENT STRING,\n  CATEGORY_NAME STRING,\n  BRAND STRING,\n  PRICE FLOAT,\n  WEIGHT_LBS FLOAT,\n  IS_PERISHABLE BOOLEAN,\n  PRODUCT_TYPE STRING\n)\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nHANDLER = 'Generator'\nAS $$\nimport random\n\nclass Generator:\n    BRANDS = {\n        'Apparel': ['NorthStyle', 'UrbanThread', 'ClassicCo', 'TrendLine', 'EverWear'],\n        'Footwear': ['StridePro', 'WalkEasy', 'RunFast', 'ComfortStep', 'TrailBlaze'],\n        'Accessories': ['LuxCarry', 'DailyGem', 'TrendSet', 'FineCraft', 'StyleVault'],\n        'Home': ['HomeNest', 'CozyLife', 'ModernLiv', 'PureLinen', 'CraftHome'],\n        'Beauty': ['GlowUp', 'PureSkin', 'FreshFace', 'NaturalGlow', 'LuxBeauty'],\n        'Electronics': ['SoundMax', 'AudioPeak', 'BeatWave', 'ClearTone', 'BassDrop'],\n    }\n    CATEGORIES = [\n        ('CAT-001', 'Apparel', 'Womens Clothing', 25.0, 250.0),\n        ('CAT-002', 'Apparel', 'Mens Clothing', 30.0, 200.0),\n        ('CAT-003', 'Apparel', 'Kids Clothing', 15.0, 80.0),\n        ('CAT-004', 'Footwear', 'Athletic Shoes', 60.0, 200.0),\n        ('CAT-005', 'Footwear', 'Casual Shoes', 40.0, 150.0),\n        ('CAT-006', 'Accessories', 'Bags & Wallets', 30.0, 400.0),\n        ('CAT-007', 'Accessories', 'Jewelry', 20.0, 500.0),\n        ('CAT-008', 'Home', 'Bedding', 50.0, 300.0),\n        ('CAT-009', 'Home', 'Kitchen', 15.0, 200.0),\n        ('CAT-010', 'Beauty', 'Skincare', 15.0, 150.0),\n        ('CAT-011', 'Beauty', 'Makeup', 10.0, 80.0),\n        ('CAT-012', 'Electronics', 'Audio', 30.0, 400.0),\n    ]\n    ADJECTIVES = ['Premium', 'Classic', 'Modern', 'Essential', 'Deluxe', 'Ultra',\n                  'Pro', 'Signature', 'Everyday', 'Performance']\n    NOUNS = {\n        'Womens Clothing': ['Blouse', 'Dress', 'Jacket', 'Skirt', 'Cardigan'],\n        'Mens Clothing': ['Shirt', 'Polo', 'Chinos', 'Blazer', 'Sweater'],\n        'Kids Clothing': ['Tee', 'Hoodie', 'Shorts', 'Romper', 'Jacket'],\n        'Athletic Shoes': ['Runner', 'Trainer', 'Court Shoe', 'Cleats', 'Trail Shoe'],\n        'Casual Shoes': ['Loafer', 'Sneaker', 'Sandal', 'Boot', 'Slip-On'],\n        'Bags & Wallets': ['Tote', 'Crossbody', 'Wallet', 'Backpack', 'Clutch'],\n        'Jewelry': ['Necklace', 'Bracelet', 'Ring', 'Earrings', 'Watch'],\n        'Bedding': ['Sheet Set', 'Comforter', 'Pillow', 'Duvet', 'Blanket'],\n        'Kitchen': ['Mug Set', 'Cutting Board', 'Knife Set', 'Pan', 'Mixer'],\n        'Skincare': ['Moisturizer', 'Serum', 'Cleanser', 'Sunscreen', 'Mask'],\n        'Makeup': ['Foundation', 'Lipstick', 'Mascara', 'Palette', 'Blush'],\n        'Audio': ['Headphones', 'Earbuds', 'Speaker', 'Soundbar', 'Turntable'],\n    }\n\n    def process(self, seed):\n        rng = random.Random(seed)\n        cat_id, dept, cat_name, min_p, max_p = rng.choice(self.CATEGORIES)\n        brand = rng.choice(self.BRANDS[dept])\n        adj = rng.choice(self.ADJECTIVES)\n        noun = rng.choice(self.NOUNS[cat_name])\n        name = f"{brand} {adj} {noun}"\n        price = round(rng.uniform(min_p, max_p), 2)\n        weight = round(rng.uniform(0.2, 15.0), 1)\n        is_perishable = dept == 'Beauty' and rng.random() < 0.3\n        ptype = 'daily_staple' if price < 50 else ('mid_range' if price < 150 else 'premium')\n        yield (seed, name, cat_id, dept, cat_name, brand, price, weight, is_perishable, ptype)\n$$;\n\nCREATE OR REPLACE TABLE PRODUCT_CATALOG AS\nSELECT p.*\nFROM TABLE(GENERATOR(ROWCOUNT => 2000)) g,\nTABLE(GENERATE_PRODUCT(SEQ4()::INT)) p;\n\nSELECT DEPARTMENT, COUNT(*) AS CNT, ROUND(AVG(PRICE), 2) AS AVG_PRICE\nFROM PRODUCT_CATALOG GROUP BY DEPARTMENT ORDER BY CNT DESC;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating the Inventory Table\u003C/h2\u003E\n","\u003Cp\u003EDuration: 1\u003C/p\u003E\n","\u003Cp\u003EThe inventory table tracks stock levels per product per warehouse region. This is the deterministic availability layer — no ML needed for "is it in stock?"\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE SCHEMA RAW;\n\nCREATE OR REPLACE TABLE INVENTORY AS\nSELECT\n  p.PRODUCT_ID,\n  regions.REGION,\n  regions.WAREHOUSE_NAME,\n  CASE\n    WHEN UNIFORM(0, 100, RANDOM()) < 85 THEN UNIFORM(5, 500, RANDOM())\n    ELSE 0\n  END AS QTY_ON_HAND,\n  CASE regions.REGION\n    WHEN 'Northeast' THEN UNIFORM(1, 3, RANDOM())\n    WHEN 'Southeast' THEN UNIFORM(1, 4, RANDOM())\n    WHEN 'Midwest' THEN UNIFORM(2, 4, RANDOM())\n    WHEN 'South' THEN UNIFORM(2, 5, RANDOM())\n    WHEN 'West' THEN UNIFORM(2, 5, RANDOM())\n  END AS EST_DELIVERY_DAYS,\n  CURRENT_TIMESTAMP() AS LAST_UPDATED\nFROM PRODUCT_CATALOG p\nCROSS JOIN (\n  SELECT COLUMN1 AS REGION, COLUMN2 AS WAREHOUSE_NAME FROM VALUES\n    ('Northeast', 'WH-NE-01'),\n    ('Southeast', 'WH-SE-01'),\n    ('Midwest', 'WH-MW-01'),\n    ('South', 'WH-SO-01'),\n    ('West', 'WH-WE-01')\n) AS regions;\n\nSELECT REGION, COUNT(*) AS PRODUCTS, SUM(CASE WHEN QTY_ON_HAND > 0 THEN 1 ELSE 0 END) AS IN_STOCK\nFROM INVENTORY GROUP BY REGION;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerating Synthetic Customers\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EThis UDTF generates 50,000 synthetic retail customers with demographics, loyalty tier assignment, and channel preferences.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE SCHEMA RAW;\n\nCREATE OR REPLACE FUNCTION GENERATE_RETAIL_CUSTOMER(SEED INT)\nRETURNS TABLE (\n  CUSTOMER_ID NUMBER,\n  EMAIL STRING,\n  PHONE STRING,\n  FIRST_NAME STRING,\n  LAST_NAME STRING,\n  DATE_OF_BIRTH DATE,\n  GENDER STRING,\n  STREET_ADDRESS STRING,\n  CITY STRING,\n  STATE STRING,\n  ZIP_CODE STRING,\n  CUSTOMER_SINCE_DATE DATE,\n  LOYALTY_TIER STRING,\n  HAS_APP BOOLEAN,\n  EMAIL_SUBSCRIBER BOOLEAN,\n  SMS_SUBSCRIBER BOOLEAN\n)\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nPACKAGES = ('faker')\nHANDLER = 'Generator'\nAS $$\nimport random\nfrom faker import Faker\n\nclass Generator:\n    TIERS = ['Bronze', 'Silver', 'Gold', 'Platinum']\n    TIER_WEIGHTS = [0.40, 0.30, 0.20, 0.10]\n\n    def process(self, seed):\n        fake = Faker('en_US')\n        Faker.seed(seed)\n        rng = random.Random(seed)\n        cid = seed + 1000000\n        gender = rng.choices(['Female', 'Male', 'Non-binary'], weights=[55, 40, 5])[0]\n        first = fake.first_name_female() if gender == 'Female' else fake.first_name_male() if gender == 'Male' else fake.first_name()\n        last = fake.last_name()\n        email = f"{first.lower()}.{last.lower()}{rng.randint(1,999)}@{rng.choice(['gmail.com','yahoo.com','outlook.com','aol.com'])}"\n        phone = fake.phone_number()\n        dob = fake.date_of_birth(minimum_age=18, maximum_age=75)\n        addr = fake.street_address()\n        city = fake.city()\n        state = fake.state_abbr()\n        zipcode = fake.zipcode()\n        since = fake.date_between(start_date='-10y', end_date='today')\n        tier = rng.choices(self.TIERS, weights=self.TIER_WEIGHTS)[0]\n        has_app = rng.random() < 0.45\n        email_sub = rng.random() < 0.70\n        sms_sub = rng.random() < 0.35\n        yield (cid, email, phone, first, last, dob, gender, addr, city, state, zipcode, since, tier, has_app, email_sub, sms_sub)\n$$;\n\nCREATE OR REPLACE TABLE CUSTOMERS AS\nSELECT c.* FROM TABLE(GENERATOR(ROWCOUNT => 50000)) g,\nTABLE(GENERATE_RETAIL_CUSTOMER(SEQ4()::INT)) c;\n\nSELECT LOYALTY_TIER, COUNT(*) AS CNT,\n  ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (), 1) AS PCT\nFROM CUSTOMERS GROUP BY LOYALTY_TIER ORDER BY CNT DESC;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerating Synthetic Events\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EThis UDTF generates a realistic event stream for each customer: browsing, cart activity, purchases, post-purchase events, loyalty events, and in-store visits. Events include product IDs from the catalog to enable recommendation model training.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE SCHEMA RAW;\n\nCREATE OR REPLACE FUNCTION GENERATE_RETAIL_EVENTS(\n  CUSTOMER_ID INT, HAS_APP BOOLEAN, CITY STRING, STATE STRING, LOYALTY_TIER STRING\n)\nRETURNS TABLE (\n  EVENT_ID STRING,\n  CUSTOMER_ID NUMBER,\n  EVENT_TYPE STRING,\n  EVENT_TIMESTAMP TIMESTAMP_NTZ,\n  CHANNEL STRING,\n  SESSION_ID STRING,\n  DEVICE_TYPE STRING,\n  PRODUCT_ID NUMBER,\n  PRODUCT_CATEGORY STRING,\n  PRODUCT_PRICE FLOAT,\n  QUANTITY INT,\n  CART_VALUE FLOAT,\n  ORDER_ID STRING,\n  ORDER_TOTAL FLOAT,\n  STORE_ID INT,\n  PROMO_CODE STRING,\n  POINTS_EARNED INT,\n  POINTS_REDEEMED INT\n)\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nHANDLER = 'Generator'\nAS $$\nimport random\nimport uuid\nfrom datetime import datetime, timedelta\n\nclass Generator:\n    EVENT_TYPES = {\n        'browse': ['page_view', 'product_view', 'category_browse', 'search', 'add_to_wishlist'],\n        'cart': ['add_to_cart', 'remove_from_cart', 'cart_view', 'abandoned_cart'],\n        'purchase': ['checkout_start', 'payment', 'order_complete'],\n        'post': ['order_shipped', 'order_delivered', 'return_initiated', 'review_submitted'],\n        'loyalty': ['points_earned', 'points_redeemed', 'tier_upgrade'],\n        'store': ['store_visit', 'pos_purchase'],\n    }\n    CATEGORIES = ['CAT-001','CAT-002','CAT-003','CAT-004','CAT-005','CAT-006',\n                  'CAT-007','CAT-008','CAT-009','CAT-010','CAT-011','CAT-012']\n    PROMOS = ['SPRING20', 'SUMMER40', 'BTS25', 'BLACKFRI', 'HOLIDAY15', 'NEWYEAR30', 'VIPONLY', 'FREESHIP']\n    TIER_ACTIVITY = {'Bronze': 40, 'Silver': 70, 'Gold': 110, 'Platinum': 160}\n\n    def process(self, customer_id, has_app, city, state, loyalty_tier):\n        rng = random.Random(customer_id)\n        num_events = self.TIER_ACTIVITY.get(loyalty_tier, 60) + rng.randint(-20, 30)\n        num_events = max(10, num_events)\n        base_date = datetime.now() - timedelta(days=365)\n        sessions = rng.randint(5, num_events // 3 + 1)\n        fav_cats = rng.sample(self.CATEGORIES, k=min(3, len(self.CATEGORIES)))\n        order_counter = 0\n\n        for _ in range(num_events):\n            ts = base_date + timedelta(seconds=rng.randint(0, 365 * 86400))\n            session_id = f"S-{customer_id}-{rng.randint(1, sessions)}"\n            group = rng.choices(\n                ['browse', 'cart', 'purchase', 'post', 'loyalty', 'store'],\n                weights=[35, 15, 8, 5, 5, 12 if not has_app else 7]\n            )[0]\n            evt = rng.choice(self.EVENT_TYPES[group])\n\n            if has_app:\n                channel = rng.choices(['Web', 'App', 'Store'], weights=[40, 40, 20])[0]\n                device = rng.choice(['Mobile', 'Desktop', 'Tablet']) if channel != 'Store' else 'POS'\n            else:\n                channel = rng.choices(['Web', 'Store'], weights=[55, 45])[0]\n                device = rng.choice(['Mobile', 'Desktop']) if channel != 'Store' else 'POS'\n\n            cat = rng.choices(self.CATEGORIES, weights=[3 if c in fav_cats else 1 for c in self.CATEGORIES])[0]\n            cat_idx = self.CATEGORIES.index(cat)\n            prod_id = cat_idx * 200 + rng.randint(0, 160)\n            price = round(rng.uniform(10, 300), 2)\n            qty = rng.choice([1, 1, 1, 2, 3]) if evt in ('add_to_cart', 'order_complete', 'pos_purchase') else 0\n            cart_val = round(price * qty, 2) if qty > 0 else None\n            order_id = None\n            order_total = None\n            store_id = rng.randint(0, 499) if channel == 'Store' else None\n            promo = rng.choice(self.PROMOS) if evt == 'order_complete' and rng.random() < 0.15 else None\n            pts_earned = rng.randint(10, 200) if evt == 'points_earned' else 0\n            pts_redeemed = rng.randint(50, 500) if evt == 'points_redeemed' else 0\n\n            if evt in ('order_complete', 'pos_purchase'):\n                order_counter += 1\n                order_id = f"ORD-{customer_id}-{order_counter}"\n                order_total = round(price * max(qty, 1) * rng.uniform(0.85, 1.15), 2)\n\n            yield (\n                str(uuid.UUID(int=rng.getrandbits(128))),\n                customer_id, evt, ts, channel, session_id, device,\n                prod_id, cat, price, qty, cart_val, order_id, order_total,\n                store_id, promo, pts_earned, pts_redeemed\n            )\n$$;\n\nCREATE OR REPLACE TABLE EVENT_STREAM AS\nSELECT e.* FROM CUSTOMERS c,\nTABLE(GENERATE_RETAIL_EVENTS(\n  c.CUSTOMER_ID, c.HAS_APP, c.CITY, c.STATE, c.LOYALTY_TIER\n)) e;\n\nSELECT EVENT_TYPE, COUNT(*) AS CNT\nFROM EVENT_STREAM GROUP BY EVENT_TYPE ORDER BY CNT DESC LIMIT 15;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will generate approximately 3-5 million events depending on tier distribution. The query should take 2-4 minutes on a MEDIUM warehouse.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuilding the Customer 360 Gold Layer\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EThe Customer 360 Dynamic Table aggregates raw events into a rich customer feature set. It refreshes automatically as new data arrives.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE SCHEMA GOLD;\n\nCREATE OR REPLACE DYNAMIC TABLE CUSTOMER_360\n  TARGET_LAG = '30 minutes'\n  WAREHOUSE = RETAIL_QS_WH\nAS\nWITH purchase_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    COUNT(DISTINCT ORDER_ID) AS TOTAL_ORDERS,\n    SUM(ORDER_TOTAL) AS LIFETIME_SPEND,\n    AVG(ORDER_TOTAL) AS AVG_ORDER_VALUE,\n    MAX(EVENT_TIMESTAMP) AS LAST_PURCHASE_DATE,\n    COUNT(DISTINCT PRODUCT_CATEGORY) AS CATEGORIES_PURCHASED,\n    DATEDIFF('day', MAX(EVENT_TIMESTAMP), CURRENT_TIMESTAMP()) AS DAYS_SINCE_PURCHASE\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('order_complete', 'pos_purchase')\n  GROUP BY CUSTOMER_ID\n),\nbrowse_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    COUNT(*) AS TOTAL_BROWSE_EVENTS,\n    COUNT(DISTINCT SESSION_ID) AS TOTAL_SESSIONS,\n    COUNT(DISTINCT PRODUCT_CATEGORY) AS CATEGORIES_BROWSED,\n    MODE(PRODUCT_CATEGORY) AS FAVORITE_BROWSE_CATEGORY,\n    AVG(PRODUCT_PRICE) AS AVG_BROWSED_PRICE\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('product_view', 'category_browse', 'search')\n  GROUP BY CUSTOMER_ID\n),\ncart_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    SUM(CASE WHEN EVENT_TYPE = 'add_to_cart' THEN 1 ELSE 0 END) AS ADD_TO_CART_COUNT,\n    SUM(CASE WHEN EVENT_TYPE = 'abandoned_cart' THEN 1 ELSE 0 END) AS ABANDONED_CART_COUNT\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('add_to_cart', 'abandoned_cart')\n  GROUP BY CUSTOMER_ID\n),\nchannel_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    MODE(CHANNEL) AS PREFERRED_CHANNEL,\n    MODE(DEVICE_TYPE) AS PREFERRED_DEVICE,\n    SUM(CASE WHEN CHANNEL = 'Store' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS STORE_RATIO\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  GROUP BY CUSTOMER_ID\n),\nloyalty_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    SUM(POINTS_EARNED) AS TOTAL_POINTS_EARNED,\n    SUM(POINTS_REDEEMED) AS TOTAL_POINTS_REDEEMED\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('points_earned', 'points_redeemed')\n  GROUP BY CUSTOMER_ID\n),\nfavorite_product_cat AS (\n  SELECT\n    CUSTOMER_ID,\n    MODE(PRODUCT_CATEGORY) AS FAVORITE_PURCHASE_CATEGORY\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('order_complete', 'pos_purchase')\n  GROUP BY CUSTOMER_ID\n)\nSELECT\n  c.CUSTOMER_ID,\n  c.FIRST_NAME,\n  c.LAST_NAME,\n  c.EMAIL,\n  c.GENDER,\n  c.DATE_OF_BIRTH,\n  DATEDIFF('year', c.DATE_OF_BIRTH, CURRENT_DATE()) AS AGE,\n  c.CITY,\n  c.STATE,\n  c.ZIP_CODE,\n  c.CUSTOMER_SINCE_DATE,\n  DATEDIFF('month', c.CUSTOMER_SINCE_DATE, CURRENT_DATE()) AS TENURE_MONTHS,\n  c.LOYALTY_TIER,\n  c.HAS_APP,\n  c.EMAIL_SUBSCRIBER,\n  c.SMS_SUBSCRIBER,\n  COALESCE(p.TOTAL_ORDERS, 0) AS TOTAL_ORDERS,\n  COALESCE(p.LIFETIME_SPEND, 0) AS LIFETIME_SPEND,\n  COALESCE(p.AVG_ORDER_VALUE, 0) AS AVG_ORDER_VALUE,\n  p.LAST_PURCHASE_DATE,\n  COALESCE(p.DAYS_SINCE_PURCHASE, 999) AS DAYS_SINCE_PURCHASE,\n  COALESCE(p.CATEGORIES_PURCHASED, 0) AS CATEGORIES_PURCHASED,\n  COALESCE(b.TOTAL_BROWSE_EVENTS, 0) AS TOTAL_BROWSE_EVENTS,\n  COALESCE(b.TOTAL_SESSIONS, 0) AS TOTAL_SESSIONS,\n  COALESCE(b.CATEGORIES_BROWSED, 0) AS CATEGORIES_BROWSED,\n  b.FAVORITE_BROWSE_CATEGORY,\n  COALESCE(b.AVG_BROWSED_PRICE, 0) AS AVG_BROWSED_PRICE,\n  COALESCE(ct.ADD_TO_CART_COUNT, 0) AS ADD_TO_CART_COUNT,\n  COALESCE(ct.ABANDONED_CART_COUNT, 0) AS ABANDONED_CART_COUNT,\n  CASE WHEN COALESCE(ct.ADD_TO_CART_COUNT, 0) > 0\n    THEN ROUND(COALESCE(ct.ABANDONED_CART_COUNT, 0) * 1.0 / ct.ADD_TO_CART_COUNT, 3)\n    ELSE 0 END AS CART_ABANDONMENT_RATE,\n  ch.PREFERRED_CHANNEL,\n  ch.PREFERRED_DEVICE,\n  COALESCE(ch.STORE_RATIO, 0) AS STORE_RATIO,\n  COALESCE(l.TOTAL_POINTS_EARNED, 0) AS TOTAL_POINTS_EARNED,\n  COALESCE(l.TOTAL_POINTS_REDEEMED, 0) AS TOTAL_POINTS_REDEEMED,\n  fpc.FAVORITE_PURCHASE_CATEGORY,\n  CASE\n    WHEN COALESCE(p.DAYS_SINCE_PURCHASE, 999) <= 30 AND COALESCE(p.TOTAL_ORDERS, 0) >= 5 AND COALESCE(p.LIFETIME_SPEND, 0) >= 500 THEN 'Champion'\n    WHEN COALESCE(p.TOTAL_ORDERS, 0) >= 3 AND COALESCE(p.LIFETIME_SPEND, 0) >= 200 THEN 'Loyal'\n    WHEN COALESCE(p.DAYS_SINCE_PURCHASE, 999) <= 60 THEN 'Active'\n    WHEN COALESCE(p.DAYS_SINCE_PURCHASE, 999) <= 180 THEN 'At_Risk'\n    ELSE 'Dormant'\n  END AS RFM_SEGMENT,\n  ROUND(\n    LEAST(100,\n      COALESCE(b.TOTAL_SESSIONS, 0) * 0.3 +\n      COALESCE(p.TOTAL_ORDERS, 0) * 5 +\n      COALESCE(l.TOTAL_POINTS_EARNED, 0) * 0.01 +\n      CASE WHEN c.HAS_APP THEN 10 ELSE 0 END +\n      CASE WHEN c.EMAIL_SUBSCRIBER THEN 5 ELSE 0 END\n    ), 1\n  ) AS ENGAGEMENT_SCORE\nFROM RETAIL_RECOMMENDATION_QS.RAW.CUSTOMERS c\nLEFT JOIN purchase_stats p ON c.CUSTOMER_ID = p.CUSTOMER_ID\nLEFT JOIN browse_stats b ON c.CUSTOMER_ID = b.CUSTOMER_ID\nLEFT JOIN cart_stats ct ON c.CUSTOMER_ID = ct.CUSTOMER_ID\nLEFT JOIN channel_stats ch ON c.CUSTOMER_ID = ch.CUSTOMER_ID\nLEFT JOIN loyalty_stats l ON c.CUSTOMER_ID = l.CUSTOMER_ID\nLEFT JOIN favorite_product_cat fpc ON c.CUSTOMER_ID = fpc.CUSTOMER_ID;\n\nSELECT RFM_SEGMENT, COUNT(*) AS CNT, ROUND(AVG(ENGAGEMENT_SCORE), 1) AS AVG_ENGAGEMENT\nFROM CUSTOMER_360 GROUP BY RFM_SEGMENT ORDER BY CNT DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Snowflake may select FULL refresh mode for this Dynamic Table because of \u003Ccode\u003EAVG()\u003C/code\u003E aggregations on FLOAT columns combined with JOINs. This does not affect correctness — it simply means each refresh recomputes the full table rather than incrementally updating. For production, consider casting FLOAT columns to \u003Ccode\u003EDECIMAL(18,4)\u003C/code\u003E to enable incremental refresh.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFeature Store, Model Training, and Registration\u003C/h2\u003E\n","\u003Cp\u003EDuration: 15\u003C/p\u003E\n","\u003Cp\u003EThis step uses a Snowflake Notebook to:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESet up the Feature Store\u003C/strong\u003E with two feature views (customer features and product features) for training data retrieval\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETrain the propensity model\u003C/strong\u003E (XGBoost) — predicts customer affinity per product category, run as daily batch\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETrain the ranking model\u003C/strong\u003E (XGBoost) — scores customer-product pairs in real-time\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERegister both models\u003C/strong\u003E in the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDeploy the ranking model\u003C/strong\u003E to SPCS for real-time inference\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERunning the Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/retail-realtime-recommendations/notebooks/01_train_and_register.ipynb\"\u003E\u003Ccode\u003E01_train_and_register.ipynb\u003C/code\u003E\u003C/a\u003E from the companion repository\u003C/li\u003E\u003Cli\u003EOpen Snowsight and navigate to \u003Cstrong\u003ENotebooks\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Notebook\u003C/strong\u003E → \u003Cstrong\u003EImport .ipynb file\u003C/strong\u003E and upload the downloaded notebook\u003C/li\u003E\u003Cli\u003EBefore running, add the required packages via the \u003Cstrong\u003EPackages\u003C/strong\u003E button in the top-right:\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Esnowflake-ml-python\u003C/code\u003E (>= 1.7.0)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Exgboost\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Escikit-learn\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ESet the role to \u003Ccode\u003ERETAIL_QS_ROLE\u003C/code\u003E and warehouse to \u003Ccode\u003ERETAIL_QS_WH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ERun all cells sequentially\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This notebook must be run interactively in Snowsight — it cannot be executed headlessly via \u003Ccode\u003EEXECUTE NOTEBOOK\u003C/code\u003E because the required packages must be added through the UI package selector.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAfter the notebook completes, verify the models are registered:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE RETAIL_QS_ROLE;\nSHOW MODELS IN SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see two models:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ECATEGORY_PROPENSITY_MODEL\u003C/code\u003E — batch propensity (target_platforms: WAREHOUSE)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EPRODUCT_RANKING_MODEL\u003C/code\u003E — real-time ranking (target_platforms: SNOWPARK_CONTAINER_SERVICES)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERunning Batch Propensity Scoring\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Cp\u003EAfter training, run the propensity model in batch mode to score all customers. This creates a table of per-customer propensity scores.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Model inference calls (\u003Ccode\u003EMODEL!PREDICT_PROBA\u003C/code\u003E) are not currently supported inside Dynamic Table definitions. We use a CTAS statement instead, which you can schedule with a Snowflake Task for daily refresh.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE RETAIL_QS_ROLE;\nUSE DATABASE RETAIL_RECOMMENDATION_QS;\nUSE SCHEMA GOLD;\nUSE WAREHOUSE RETAIL_QS_WH;\n\nCREATE OR REPLACE TABLE CUSTOMER_PROPENSITY AS\nWITH customer_features AS (\n  SELECT\n    CUSTOMER_ID,\n    AGE,\n    TENURE_MONTHS,\n    CASE LOYALTY_TIER\n      WHEN 'Bronze' THEN 0 WHEN 'Silver' THEN 1\n      WHEN 'Gold' THEN 2 WHEN 'Platinum' THEN 3\n    END AS LOYALTY_TIER_NUM,\n    TOTAL_ORDERS,\n    LIFETIME_SPEND,\n    AVG_ORDER_VALUE,\n    DAYS_SINCE_PURCHASE,\n    CATEGORIES_PURCHASED,\n    TOTAL_BROWSE_EVENTS,\n    TOTAL_SESSIONS,\n    CART_ABANDONMENT_RATE,\n    ENGAGEMENT_SCORE\n  FROM RETAIL_RECOMMENDATION_QS.GOLD.CUSTOMER_360\n),\ncategories AS (\n  SELECT CATEGORY_ID FROM RETAIL_RECOMMENDATION_QS.RAW.PRODUCT_CATEGORIES\n)\nSELECT\n  cf.CUSTOMER_ID,\n  cat.CATEGORY_ID,\n  RETAIL_RECOMMENDATION_QS.ML_REGISTRY.CATEGORY_PROPENSITY_MODEL!PREDICT_PROBA(\n    cf.AGE, cf.TENURE_MONTHS, cf.LOYALTY_TIER_NUM, cf.TOTAL_ORDERS,\n    cf.LIFETIME_SPEND, cf.AVG_ORDER_VALUE, cf.DAYS_SINCE_PURCHASE,\n    cf.CATEGORIES_PURCHASED, cf.TOTAL_BROWSE_EVENTS, cf.TOTAL_SESSIONS,\n    cf.CART_ABANDONMENT_RATE, cf.ENGAGEMENT_SCORE\n  ):output_feature_1::FLOAT AS PROPENSITY_SCORE\nFROM customer_features cf\nCROSS JOIN categories cat;\n\nSELECT * FROM CUSTOMER_PROPENSITY\nWHERE CUSTOMER_ID = 1000000\nORDER BY PROPENSITY_SCORE DESC;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The argument order must match the model's function signature exactly. Run \u003Ccode\u003ESHOW FUNCTIONS IN MODEL RETAIL_RECOMMENDATION_QS.ML_REGISTRY.CATEGORY_PROPENSITY_MODEL;\u003C/code\u003E to verify. The \u003Ccode\u003EPREDICT_PROBA\u003C/code\u003E call uses the model's default version.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetting Up Snowflake Postgres for Low-Latency Serving\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003ETo achieve sub-100ms end-to-end latency for the recommendation API, we use \u003Cstrong\u003ESnowflake Postgres\u003C/strong\u003E — a managed PostgreSQL instance that runs in the same AWS region as your SPCS services, providing single-digit-millisecond lookups. Without Postgres, the orchestrator falls back to Snowflake warehouse queries which add ~500-600ms of overhead.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Snowflake Postgres instances are account-level objects (not database-scoped). Network rules for Postgres instances must use \u003Ccode\u003EMODE = POSTGRES_INGRESS\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Postgres Instance\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE POSTGRES INSTANCE IF NOT EXISTS RETAIL_QS_PG\n  COMPUTE_FAMILY = 'STANDARD_M'\n  STORAGE_SIZE_GB = 50\n  AUTHENTICATION_AUTHORITY = POSTGRES;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E The \u003Ccode\u003ECREATE\u003C/code\u003E output returns the admin password in the \u003Ccode\u003Eaccess_roles\u003C/code\u003E JSON. \u003Cstrong\u003ESave it immediately\u003C/strong\u003E — it cannot be retrieved later. Note the \u003Ccode\u003Ehost\u003C/code\u003E value as well; you will need both for data loading and the orchestrator service.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWait for the instance to reach \u003Ccode\u003EREADY\u003C/code\u003E state:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW POSTGRES INSTANCES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Network Rules and Access Policies\u003C/h3\u003E\n","\u003Cp\u003EThe Postgres instance needs an ingress network rule to allow connections, and the SPCS orchestrator needs an egress rule to reach it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Allow connections to the Postgres instance (POSTGRES_INGRESS mode required)\nCREATE OR REPLACE NETWORK RULE RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_PG_INGRESS\n  TYPE = IPV4\n  MODE = POSTGRES_INGRESS\n  VALUE_LIST = ('0.0.0.0/0');\n\nCREATE OR REPLACE NETWORK POLICY RETAIL_QS_PG_POLICY\n  ALLOWED_NETWORK_RULE_LIST = ('RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_PG_INGRESS');\n\nALTER POSTGRES INSTANCE RETAIL_QS_PG SET NETWORK_POLICY = RETAIL_QS_PG_POLICY;\n\n-- Egress rule for SPCS orchestrator to reach Postgres\nCREATE OR REPLACE NETWORK RULE RETAIL_RECOMMENDATION_QS.RAW.PG_EGRESS_RULE\n  TYPE = HOST_PORT\n  MODE = EGRESS\n  VALUE_LIST = ('<your-pg-host>:5432');\n\n-- Store the Postgres password as a secret for SPCS\nCREATE OR REPLACE SECRET RETAIL_RECOMMENDATION_QS.RAW.PG_PASSWORD\n  TYPE = GENERIC_STRING\n  SECRET_STRING = '<your-admin-password>';\n\n-- External access integration for SPCS\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION PG_ACCESS_INTEGRATION\n  ALLOWED_NETWORK_RULES = (RETAIL_RECOMMENDATION_QS.RAW.PG_EGRESS_RULE)\n  ALLOWED_AUTHENTICATION_SECRETS = (RETAIL_RECOMMENDATION_QS.RAW.PG_PASSWORD)\n  ENABLED = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Replace \u003Ccode\u003E<your-pg-host>\u003C/code\u003E with the host from \u003Ccode\u003EDESCRIBE POSTGRES INSTANCE RETAIL_QS_PG\u003C/code\u003E and \u003Ccode\u003E<your-admin-password>\u003C/code\u003E with the \u003Ccode\u003Eadmin_password\u003C/code\u003E value.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoading Data into Postgres\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EWith the Postgres instance running, connect via \u003Ccode\u003Epsql\u003C/code\u003E to create the serving tables and load data from Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EConnect to Postgres\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epsql "host=<your-pg-host> port=5432 dbname=postgres user=snowflake_admin password=<your-admin-password> sslmode=require"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate Tables\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE IF NOT EXISTS customer_features (\n  customer_id BIGINT PRIMARY KEY,\n  age INT, tenure_months INT, loyalty_tier_num INT,\n  total_orders INT, lifetime_spend DOUBLE PRECISION,\n  avg_order_value DOUBLE PRECISION, days_since_purchase INT,\n  categories_purchased INT, total_browse_events INT,\n  total_sessions INT, avg_browsed_price DOUBLE PRECISION,\n  add_to_cart_count INT, cart_abandonment_rate DOUBLE PRECISION,\n  store_ratio DOUBLE PRECISION, engagement_score DOUBLE PRECISION\n);\n\nCREATE TABLE IF NOT EXISTS customer_propensity (\n  customer_id BIGINT,\n  category_id TEXT,\n  propensity_score DOUBLE PRECISION,\n  PRIMARY KEY (customer_id, category_id)\n);\n\nCREATE TABLE IF NOT EXISTS inventory (\n  product_id BIGINT,\n  region TEXT,\n  warehouse_name TEXT,\n  qty_on_hand INT,\n  est_delivery_days INT,\n  PRIMARY KEY (region, product_id)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis creates three tables:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERows\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPrimary Key\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustomer_features\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E50K\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustomer_id\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer 360 features (1 row per customer)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustomer_propensity\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E600K\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E(customer_id, category_id)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECategory propensity scores (12 rows per customer)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Einventory\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E~8.5K\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E(region, product_id)\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERegional stock levels\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDesign Note:\u003C/strong\u003E Customer features and propensity scores are kept in separate tables because they have different cardinality (1:1 vs 1:12), different query patterns (point lookup vs top-N ranked), and different refresh cadences. Denormalizing would bloat the table 12x with no benefit.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ELoad Data from Snowflake\u003C/h3\u003E\n","\u003Cp\u003EDownload \u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/retail-realtime-recommendations/load_pg.py\"\u003E\u003Ccode\u003Eload_pg.py\u003C/code\u003E\u003C/a\u003E from the companion repository. This script exports data from Snowflake and loads it into Postgres using \u003Ccode\u003Esnowflake-connector-python\u003C/code\u003E and \u003Ccode\u003Epsycopg2\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake-connector-python psycopg2-binary\nexport SNOWFLAKE_CONNECTION_NAME=<your-connection-name>\nexport PG_HOST=<your-pg-host>\nexport PG_PASSWORD=<your-admin-password>\npython load_pg.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Row Counts\u003C/h3\u003E\n","\u003Cp\u003EIn \u003Ccode\u003Epsql\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'customer_features' AS tbl, COUNT(*) FROM customer_features\nUNION ALL SELECT 'customer_propensity', COUNT(*) FROM customer_propensity\nUNION ALL SELECT 'inventory', COUNT(*) FROM inventory;\n-- Expected: 50000, 600000, ~8500\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploying the Ranking Model Service\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Cp\u003EDeploy the real-time ranking model as an SPCS service. This creates a REST endpoint that the orchestrator will call.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE RETAIL_QS_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe ranking model was registered with \u003Ccode\u003Etarget_platforms=["SNOWPARK_CONTAINER_SERVICES"]\u003C/code\u003E in the notebook. Deploy it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Run this in the notebook (cell at the end) or in a Snowflake Python worksheet:\nfrom snowflake.ml.registry import Registry\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nreg = Registry(session=session, database_name="RETAIL_RECOMMENDATION_QS",\n               schema_name="ML_REGISTRY")\nmv = reg.get_model("PRODUCT_RANKING_MODEL").default\nmv.create_service(\n    service_name="RANKING_MODEL_SERVICE",\n    service_compute_pool="RETAIL_QS_COMPUTE_POOL",\n    ingress_enabled=True\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait for the service to start, then verify:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SYSTEM$GET_SERVICE_STATUS('RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE');\nSHOW ENDPOINTS IN SERVICE RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote the \u003Ccode\u003Eingress_url\u003C/code\u003E — this is the external endpoint for the ranking model.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuilding and Deploying the Orchestrator\u003C/h2\u003E\n","\u003Cp\u003EDuration: 8\u003C/p\u003E\n","\u003Cp\u003EThe orchestrator is a FastAPI service that coordinates the recommendation pipeline: fetches customer features, propensity scores, and inventory from Snowflake Postgres in parallel, computes pair features, calls the ranking model, and returns top-N recommendations. It falls back to Snowflake warehouse queries if Postgres is not configured.\u003C/p\u003E\n","\u003Ch3\u003EGet the Orchestrator Code\u003C/h3\u003E\n","\u003Cp\u003EDownload the orchestrator files from the \u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/retail-realtime-recommendations/orchestrator\"\u003Ecompanion repository\u003C/a\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sf-samples.git\ncd sf-samples/samples/retail-realtime-recommendations/orchestrator\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EReview the Orchestrator Code\u003C/h3\u003E\n","\u003Cp\u003EThe orchestrator (\u003Ccode\u003Eapp.py\u003C/code\u003E) handles the following flow per request:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReceives \u003Ccode\u003EPOST /recommend {"customer_id": int, "zip_code": str, "top_n": 5}\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFetches customer features from Postgres (falls back to Snowflake warehouse query)\u003C/li\u003E\u003Cli\u003EFetches propensity scores from Postgres (falls back to Snowflake warehouse query)\u003C/li\u003E\u003Cli\u003EQueries available products from Postgres inventory (falls back to Snowflake warehouse query)\u003C/li\u003E\u003Cli\u003EAll three fetches run in parallel using a thread pool\u003C/li\u003E\u003Cli\u003EComputes customer-product pair features\u003C/li\u003E\u003Cli\u003ECalls the ranking model service\u003C/li\u003E\u003Cli\u003EReturns top-N ranked products with details and estimated delivery\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EBuild the Container\u003C/h3\u003E\n","\u003Cp\u003EFirst, get the image repository URL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW IMAGE REPOSITORIES IN SCHEMA RETAIL_RECOMMENDATION_QS.RAW;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen build and push from your terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003EREPO_URL=<repository_url from above>\n\ndocker login $REPO_URL --username <SNOWFLAKE_USER>\ndocker build --platform linux/amd64 -t $REPO_URL/retail_orchestrator:latest .\ndocker push $REPO_URL/retail_orchestrator:latest\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E If you modify \u003Ccode\u003Eapp.py\u003C/code\u003E and rebuild, Docker may cache layers. Use \u003Ccode\u003Edocker build --platform linux/amd64 --no-cache\u003C/code\u003E to force a clean rebuild and ensure the pushed image reflects your changes.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EDeploy the Orchestrator Service\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nUSE DATABASE RETAIL_RECOMMENDATION_QS;\nUSE SCHEMA RAW;\n\nCREATE SERVICE ORCHESTRATOR_SERVICE\n  IN COMPUTE POOL RETAIL_QS_COMPUTE_POOL\n  EXTERNAL_ACCESS_INTEGRATIONS = (PG_ACCESS_INTEGRATION)\n  MIN_INSTANCES = 1\n  MAX_INSTANCES = 1\n  FROM SPECIFICATION $$\nspec:\n  containers:\n  - name: orchestrator\n    image: /retail_recommendation_qs/raw/retail_qs_images/retail_orchestrator:latest\n    env:\n      SNOWFLAKE_WAREHOUSE: RETAIL_QS_WH\n      PYTHONUNBUFFERED: "1"\n      PG_HOST: <your-pg-host>\n      PG_USER: snowflake_admin\n      PG_DB: postgres\n      PG_PORT: "5432"\n    secrets:\n    - snowflakeSecret: RETAIL_RECOMMENDATION_QS.RAW.PG_PASSWORD\n      secretKeyRef: secret_string\n      envVarName: PG_PASSWORD\n    resources:\n      requests:\n        cpu: 0.5\n        memory: 512M\n      limits:\n        cpu: 2\n        memory: 2G\n  endpoints:\n  - name: ui\n    port: 8000\n    public: true\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Replace \u003Ccode\u003E<your-pg-host>\u003C/code\u003E with your Postgres instance hostname. The service must be created with \u003Ccode\u003EEXTERNAL_ACCESS_INTEGRATIONS\u003C/code\u003E to allow egress to Postgres — this requires \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWait for the service to start:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL SYSTEM$GET_SERVICE_STATUS('ORCHESTRATOR_SERVICE');\nSELECT SYSTEM$GET_SERVICE_LOGS('RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_SERVICE', 0, 'orchestrator', 20);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see in the logs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EPostgres connected: 50000 features, 600000 propensity, 8474 inventory rows\nReady - 2000 products cached, ..., backend=postgres\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGet the public endpoint:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW ENDPOINTS IN SERVICE ORCHESTRATOR_SERVICE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETesting the Recommendations\u003C/h2\u003E\n","\u003Cp\u003EDuration: 3\u003C/p\u003E\n","\u003Ch3\u003ETest via Interactive UI\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003Ehttps://{ORCHESTRATOR_INGRESS_URL}/\u003C/code\u003E in your browser (you will need to authenticate with your Snowflake credentials via OAuth). This loads a self-contained test page where you can enter a Customer ID, Zip Code, and Top N, then click \u003Cstrong\u003EGet Recommendations\u003C/strong\u003E to call the API.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The default FastAPI Swagger UI (\u003Ccode\u003E/docs\u003C/code\u003E) does not work in SPCS because the ingress proxy blocks the external CDN assets it requires. The built-in test page at \u003Ccode\u003E/\u003C/code\u003E is self-contained and works within SPCS.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ETest via Health Check\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Ccode\u003Ehttps://{ORCHESTRATOR_INGRESS_URL}/health\u003C/code\u003E to verify the service is running. You should see:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{"status": "ok", "products_cached": 2000, "backend": "postgres"}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Ebackend\u003C/code\u003E field confirms whether the service is using Postgres (\u003Ccode\u003Epostgres\u003C/code\u003E) or falling back to Snowflake warehouse queries (\u003Ccode\u003Esnowflake\u003C/code\u003E).\u003C/p\u003E\n","\u003Ch3\u003ETest via cURL\u003C/h3\u003E\n","\u003Cp\u003ECreate a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/programmatic-access-tokens\"\u003Eprogrammatic access token (PAT)\u003C/a\u003E for authenticating to the SPCS ingress endpoint:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER USER ADD PAT ORCHESTRATOR_PAT DAYS_TO_EXPIRY = 7;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECopy the \u003Ccode\u003Etoken_secret\u003C/code\u003E from the output and use it in the cURL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -s "https://{ORCHESTRATOR_INGRESS_URL}/recommend?customer_id=1000000&zip_code=10001&top_n=5" \\\n  -H 'Authorization: Snowflake Token="<PAT_TOKEN>"' | python3 -m json.tool\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETest via GET URL\u003C/h3\u003E\n","\u003Cp\u003EYou can also test directly in your browser URL bar:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ehttps://{ORCHESTRATOR_INGRESS_URL}/recommend?customer_id=1000000&zip_code=10001&top_n=5\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExpected recommendation response:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "customer_id": 1000000,\n  "zip_code": "10001",\n  "region": "Northeast",\n  "recommendations": [\n    {\n      "rank": 1,\n      "product_id": 42,\n      "product_name": "NorthStyle Premium Blouse",\n      "category": "Womens Clothing",\n      "department": "Apparel",\n      "price": 89.99,\n      "score": 0.9234,\n      "in_stock_qty": 145,\n      "est_delivery_days": 2\n    }\n  ],\n  "timings": {\n    "features_ms": 15.5,\n    "propensity_ms": 16.9,\n    "inventory_ms": 19.6,\n    "candidates": 1723,\n    "pair_features_ms": 1.4,\n    "model_ms": 56.2,\n    "model_ok": true,\n    "total_ms": 77.7,\n    "backend": "postgres"\n  }\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPerformance:\u003C/strong\u003E With Snowflake Postgres, all three data lookups (features, propensity, inventory) complete in parallel in under 20ms. The ranking model inference (~56ms) is the primary latency contributor, bringing total server-side latency to ~78ms — a \u003Cstrong\u003E91% reduction\u003C/strong\u003E from the ~858ms baseline using direct Snowflake warehouse queries.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Cp\u003ETo fully remove everything created in this quickstart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\n-- Services\nALTER SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_SERVICE SUSPEND;\nDROP SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_SERVICE;\nALTER SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE SUSPEND;\nDROP SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE;\n\n-- Compute pool\nDROP COMPUTE POOL IF EXISTS RETAIL_QS_COMPUTE_POOL;\n\n-- Postgres instance and networking\nDROP POSTGRES INSTANCE IF EXISTS RETAIL_QS_PG;\nDROP NETWORK POLICY IF EXISTS RETAIL_QS_PG_POLICY;\nDROP INTEGRATION IF EXISTS PG_ACCESS_INTEGRATION;\n\n-- Database (drops all schemas, tables, models, feature views, dynamic tables,\n-- network rules, secrets, and stages)\nDROP DATABASE IF EXISTS RETAIL_RECOMMENDATION_QS;\n\n-- User and role\nDROP USER IF EXISTS RETAIL_QS_SVC_USER;\nDROP ROLE IF EXISTS RETAIL_QS_ROLE;\n\n-- Warehouse\nDROP WAREHOUSE IF EXISTS RETAIL_QS_WH;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E\n","\u003Cp\u003EDuration: 2\u003C/p\u003E\n","\u003Ch3\u003EWhat You Built\u003C/h3\u003E\n","\u003Cp\u003EYou built a complete real-time product recommendation engine for retail, powered entirely by Snowflake:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESynthetic data layer\u003C/strong\u003E with 50K customers, ~4M events, 2K products, and regional inventory\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomer 360 gold layer\u003C/strong\u003E using Dynamic Tables for automatic refresh\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeature Store\u003C/strong\u003E for offline training data retrieval\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETwo XGBoost models\u003C/strong\u003E: batch propensity (daily, warehouse) and real-time ranking (SPCS)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Postgres\u003C/strong\u003E for low-latency operational serving (~15ms lookups vs ~575ms from warehouse)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESPCS orchestrator\u003C/strong\u003E that fetches data from Postgres in parallel, calls the ranking model, and returns recommendations in ~78ms end-to-end (91% faster than the warehouse-only baseline of ~858ms)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Snowflake UDTFs to generate realistic synthetic retail data\u003C/li\u003E\u003Cli\u003EHow to build a Customer 360 with Dynamic Tables\u003C/li\u003E\u003Cli\u003EHow to use the Feature Store for training data management\u003C/li\u003E\u003Cli\u003EHow to train and register models in the Snowflake Model Registry\u003C/li\u003E\u003Cli\u003EHow to deploy ML models and custom services on Snowpark Container Services\u003C/li\u003E\u003Cli\u003EHow to use Snowflake Postgres for low-latency operational serving alongside SPCS\u003C/li\u003E\u003Cli\u003EThe hybrid batch + real-time pattern for production ML systems\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAutomated Postgres Sync\u003C/strong\u003E: Use Snowflake Tasks to periodically refresh Postgres tables from the Feature Store and batch propensity outputs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECatBoost Delivery Estimation\u003C/strong\u003E: Replace the deterministic inventory filter with a CatBoost model trained on real logistics data (carrier performance, distance, demand signals) for accurate delivery date predictions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIdentity Resolution\u003C/strong\u003E: Add a Cortex Search-based identity resolution layer to unify customer profiles across channels\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgentic Commerce\u003C/strong\u003E: Build a Cortex Agent on top of the recommendation API to enable conversational product discovery\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EActivation\u003C/strong\u003E: Connect recommendations to GrowthLoop or HighTouch for audience activation and campaign orchestration\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA/B Testing\u003C/strong\u003E: Implement model version aliasing to A/B test different ranking strategies\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/overview\"\u003ESnowflake Feature Store Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/postgres/overview\"\u003ESnowflake Postgres Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/programmatic-access-tokens\"\u003EProgrammatic Access Tokens\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/retail-realtime-recommendations\"\u003ECompanion Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build Real-Time Product Recommendations for Retail with Snowflake",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\nDuration: 5\n\nModern retailers need to deliver personalized product recommendations in real-time to drive conversion, increase average order value, and improve customer satisfaction. This quickstart guide walks through building a complete real-time recommendation engine powered entirely by Snowflake.\n\nYou will build a hybrid batch + real-time ML pipeline that:\n1. Generates synthetic retail customer, event, product catalog, and inventory data\n2. Builds a Customer 360 gold layer using Dynamic Tables\n3. Registers features in the Snowflake Feature Store for training data retrieval\n4. Trains two XGBoost models: a batch propensity model (daily) and a real-time product ranking model\n5. Deploys the ranking model to Snowpark Container Services (SPCS) for real-time inference\n6. Sets up Snowflake Postgres for low-latency operational serving of features, propensity scores, and inventory\n7. Builds a FastAPI orchestrator service that fetches data from Postgres, calls the ranking model, and returns top-N recommendations\n\nThe architecture follows a pattern discussed in the context of retail Customer 360: the intersection of customer intelligence and product catalog data, positioned as a precursor to agentic commerce.\n\n\u003E **Extension Note:** The availability/delivery estimation step in this quickstart uses a deterministic SQL filter on inventory. For retailers with real logistics data (carrier performance, distance to warehouse, seasonal demand), this filter can be replaced with a CatBoost delivery estimation model to boost customer satisfaction and reduce returns.\n\n### Prerequisites\n- Snowflake Enterprise Edition account\n- `ACCOUNTADMIN` role or privileges to create databases, warehouses, compute pools, image repositories, services, and Postgres instances\n- SPCS enabled on your account\n- Snowflake Postgres enabled on your account\n- Basic knowledge of Python and ML concepts\n\n### What You'll Learn\n- How to generate synthetic retail data using Snowflake UDTFs\n- How to build a Customer 360 gold layer with Dynamic Tables\n- How to use the Snowflake Feature Store for offline training data retrieval\n- How to train and register ML models in the Snowflake Model Registry\n- How to deploy a model to SPCS and build a FastAPI orchestrator for real-time recommendations\n- How to use Snowflake Postgres for low-latency operational serving\n\n### What You'll Need\n- [Snowflake](https://snowflake.com) Enterprise Edition account\n- [Docker](https://www.docker.com/) (for building and pushing the orchestrator container)\n\n### What You'll Build\n- Real-time product recommendation API powered by Snowflake ML\n\n\u003C!-- ------------------------ --\u003E\n## Architecture Overview\nDuration: 3\n\nThe solution uses a hybrid batch + real-time architecture:\n\n**Batch Path (Daily):**\n- Customer 360 Dynamic Table aggregates raw events into customer features\n- Propensity model scores each customer's affinity per product category\n- Results are stored in the GOLD schema\n- Customer features, propensity scores, and inventory are synced to Snowflake Postgres for low-latency serving\n\n**Real-Time Path (Per Request):**\n- Orchestrator receives a recommendation request with `customer_id` and `zip_code`\n- Customer features, propensity scores, and inventory are fetched from Snowflake Postgres in parallel (~15ms)\n- Customer-product pair features are computed and sent to the ranking model service\n- Top-N recommendations are returned with product details and estimated delivery\n- Falls back to Snowflake warehouse queries if Postgres is not configured\n\n**Key Design Decisions:**\n- Customer propensity is batch because it changes slowly (loyalty tier, purchase history)\n- Product ranking is real-time because it depends on current inventory and request context\n- Inventory availability is deterministic (SQL filter) rather than ML-based\n- XGBoost is used for speed — lightweight tree models keep inference under 200ms\n- Snowflake Postgres provides single-digit-millisecond lookups for operational serving, reducing end-to-end latency from ~850ms to ~80ms\n- Customer features and propensity scores are kept in separate Postgres tables due to different cardinality (1 row/customer vs 12 rows/customer), query patterns, and refresh cadences\n\n\u003C!-- ------------------------ --\u003E\n## Setting Up Infrastructure\nDuration: 3\n\nConnect to Snowflake and run the following SQL to create the database, schemas, warehouse, role, compute pool, and image repository.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Database and schemas\nCREATE OR REPLACE DATABASE RETAIL_RECOMMENDATION_QS;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.RAW;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.GOLD;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE;\nCREATE SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY;\n\n-- Warehouse\nCREATE OR REPLACE WAREHOUSE RETAIL_QS_WH\n  WITH WAREHOUSE_SIZE = 'MEDIUM'\n  AUTO_SUSPEND = 120\n  AUTO_RESUME = TRUE;\n\n-- Role\nCREATE OR REPLACE ROLE RETAIL_QS_ROLE;\nGRANT USAGE ON DATABASE RETAIL_RECOMMENDATION_QS TO ROLE RETAIL_QS_ROLE;\nGRANT USAGE ON ALL SCHEMAS IN DATABASE RETAIL_RECOMMENDATION_QS TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.GOLD TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE TO ROLE RETAIL_QS_ROLE;\nGRANT ALL ON SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY TO ROLE RETAIL_QS_ROLE;\nGRANT USAGE ON WAREHOUSE RETAIL_QS_WH TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE DYNAMIC TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.GOLD TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE DYNAMIC TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.GOLD TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE TABLE ON SCHEMA RETAIL_RECOMMENDATION_QS.FEATURE_STORE TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE MODEL ON SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE FUNCTION ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT ROLE RETAIL_QS_ROLE TO ROLE ACCOUNTADMIN;\n\n-- Compute pool for SPCS\nCREATE COMPUTE POOL IF NOT EXISTS RETAIL_QS_COMPUTE_POOL\n  MIN_NODES = 1\n  MAX_NODES = 3\n  INSTANCE_FAMILY = CPU_X64_S;\n\nGRANT USAGE ON COMPUTE POOL RETAIL_QS_COMPUTE_POOL TO ROLE RETAIL_QS_ROLE;\nGRANT MONITOR ON COMPUTE POOL RETAIL_QS_COMPUTE_POOL TO ROLE RETAIL_QS_ROLE;\n\n-- Image repository\nCREATE IMAGE REPOSITORY IF NOT EXISTS RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_IMAGES;\nGRANT READ ON IMAGE REPOSITORY RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_IMAGES TO ROLE RETAIL_QS_ROLE;\n\n-- Stage for orchestrator spec\nCREATE STAGE IF NOT EXISTS RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_STAGE;\nGRANT ALL ON STAGE RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_STAGE TO ROLE RETAIL_QS_ROLE;\n\n-- SPCS permissions\nGRANT BIND SERVICE ENDPOINT ON ACCOUNT TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE SERVICE ON SCHEMA RETAIL_RECOMMENDATION_QS.RAW TO ROLE RETAIL_QS_ROLE;\nGRANT CREATE SERVICE ON SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY TO ROLE RETAIL_QS_ROLE;\n\nSHOW IMAGE REPOSITORIES IN SCHEMA RETAIL_RECOMMENDATION_QS.RAW;\n```\n\nNote the `repository_url` from the last command — you will need it when pushing the orchestrator container later.\n\n\u003C!-- ------------------------ --\u003E\n## Creating Reference Tables\nDuration: 2\n\nThese reference tables provide the foundation for synthetic data generation: store locations, product categories, loyalty tiers, and promotions.\n\n```sql\nUSE ROLE RETAIL_QS_ROLE;\nUSE DATABASE RETAIL_RECOMMENDATION_QS;\nUSE SCHEMA RAW;\nUSE WAREHOUSE RETAIL_QS_WH;\n\n-- Store locations\nCREATE OR REPLACE TABLE STORES AS\nSELECT\n  SEQ4() AS STORE_ID,\n  CONCAT('Store_', LPAD(SEQ4()::STRING, 4, '0')) AS STORE_CODE,\n  CASE (ABS(HASH(SEQ4())) % 4)\n    WHEN 0 THEN 'Flagship' WHEN 1 THEN 'Standard'\n    WHEN 2 THEN 'Outlet' ELSE 'Express' END AS STORE_TYPE,\n  CASE (ABS(HASH(SEQ4(), 1)) % 10)\n    WHEN 0 THEN 'New York' WHEN 1 THEN 'Los Angeles' WHEN 2 THEN 'Chicago'\n    WHEN 3 THEN 'Houston' WHEN 4 THEN 'Phoenix' WHEN 5 THEN 'Dallas'\n    WHEN 6 THEN 'Seattle' WHEN 7 THEN 'Miami' WHEN 8 THEN 'Atlanta'\n    ELSE 'Denver' END AS CITY,\n  CASE (ABS(HASH(SEQ4(), 1)) % 10)\n    WHEN 0 THEN 'NY' WHEN 1 THEN 'CA' WHEN 2 THEN 'IL'\n    WHEN 3 THEN 'TX' WHEN 4 THEN 'AZ' WHEN 5 THEN 'TX'\n    WHEN 6 THEN 'WA' WHEN 7 THEN 'FL' WHEN 8 THEN 'GA'\n    ELSE 'CO' END AS STATE,\n  CASE (ABS(HASH(SEQ4(), 1)) % 10)\n    WHEN 0 THEN 'Northeast' WHEN 1 THEN 'West' WHEN 2 THEN 'Midwest'\n    WHEN 3 THEN 'South' WHEN 4 THEN 'West' WHEN 5 THEN 'South'\n    WHEN 6 THEN 'West' WHEN 7 THEN 'Southeast' WHEN 8 THEN 'Southeast'\n    ELSE 'West' END AS REGION,\n  CASE (ABS(HASH(SEQ4(), 2)) % 5)\n    WHEN 0 THEN 'Mall' WHEN 1 THEN 'Strip Center'\n    WHEN 2 THEN 'Standalone' WHEN 3 THEN 'Downtown'\n    ELSE 'Suburban' END AS LOCATION_TYPE,\n  (ABS(HASH(SEQ4(), 3)) % 30000 + 5000) AS SQUARE_FEET\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 500));\n\n-- Product categories\nCREATE OR REPLACE TABLE PRODUCT_CATEGORIES AS\nSELECT * FROM VALUES\n  ('CAT-001', 'Apparel', 'Womens Clothing', 25.00, 250.00),\n  ('CAT-002', 'Apparel', 'Mens Clothing', 30.00, 200.00),\n  ('CAT-003', 'Apparel', 'Kids Clothing', 15.00, 80.00),\n  ('CAT-004', 'Footwear', 'Athletic Shoes', 60.00, 200.00),\n  ('CAT-005', 'Footwear', 'Casual Shoes', 40.00, 150.00),\n  ('CAT-006', 'Accessories', 'Bags & Wallets', 30.00, 400.00),\n  ('CAT-007', 'Accessories', 'Jewelry', 20.00, 500.00),\n  ('CAT-008', 'Home', 'Bedding', 50.00, 300.00),\n  ('CAT-009', 'Home', 'Kitchen', 15.00, 200.00),\n  ('CAT-010', 'Beauty', 'Skincare', 15.00, 150.00),\n  ('CAT-011', 'Beauty', 'Makeup', 10.00, 80.00),\n  ('CAT-012', 'Electronics', 'Audio', 30.00, 400.00)\nAS c(CATEGORY_ID, DEPARTMENT, CATEGORY_NAME, MIN_PRICE, MAX_PRICE);\n\n-- Loyalty tiers\nCREATE OR REPLACE TABLE LOYALTY_TIERS AS\nSELECT * FROM VALUES\n  ('TIER-001', 'Bronze', 0, 499, 1.0, 0),\n  ('TIER-002', 'Silver', 500, 1499, 1.25, 5),\n  ('TIER-003', 'Gold', 1500, 4999, 1.5, 10),\n  ('TIER-004', 'Platinum', 5000, 99999, 2.0, 15)\nAS t(TIER_ID, TIER_NAME, MIN_ANNUAL_SPEND, MAX_ANNUAL_SPEND, POINTS_MULTIPLIER, DISCOUNT_PCT);\n```\n\n\u003C!-- ------------------------ --\u003E\n## Creating the Product Catalog\nDuration: 2\n\nThe product catalog contains 2,000 products across the 12 categories defined above. Each product has a name, brand, price, and category assignment. This UDTF generates realistic retail product data.\n\n```sql\nUSE SCHEMA RAW;\n\nCREATE OR REPLACE FUNCTION GENERATE_PRODUCT(SEED INT)\nRETURNS TABLE (\n  PRODUCT_ID NUMBER,\n  PRODUCT_NAME STRING,\n  CATEGORY_ID STRING,\n  DEPARTMENT STRING,\n  CATEGORY_NAME STRING,\n  BRAND STRING,\n  PRICE FLOAT,\n  WEIGHT_LBS FLOAT,\n  IS_PERISHABLE BOOLEAN,\n  PRODUCT_TYPE STRING\n)\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nHANDLER = 'Generator'\nAS $$\nimport random\n\nclass Generator:\n    BRANDS = {\n        'Apparel': ['NorthStyle', 'UrbanThread', 'ClassicCo', 'TrendLine', 'EverWear'],\n        'Footwear': ['StridePro', 'WalkEasy', 'RunFast', 'ComfortStep', 'TrailBlaze'],\n        'Accessories': ['LuxCarry', 'DailyGem', 'TrendSet', 'FineCraft', 'StyleVault'],\n        'Home': ['HomeNest', 'CozyLife', 'ModernLiv', 'PureLinen', 'CraftHome'],\n        'Beauty': ['GlowUp', 'PureSkin', 'FreshFace', 'NaturalGlow', 'LuxBeauty'],\n        'Electronics': ['SoundMax', 'AudioPeak', 'BeatWave', 'ClearTone', 'BassDrop'],\n    }\n    CATEGORIES = [\n        ('CAT-001', 'Apparel', 'Womens Clothing', 25.0, 250.0),\n        ('CAT-002', 'Apparel', 'Mens Clothing', 30.0, 200.0),\n        ('CAT-003', 'Apparel', 'Kids Clothing', 15.0, 80.0),\n        ('CAT-004', 'Footwear', 'Athletic Shoes', 60.0, 200.0),\n        ('CAT-005', 'Footwear', 'Casual Shoes', 40.0, 150.0),\n        ('CAT-006', 'Accessories', 'Bags & Wallets', 30.0, 400.0),\n        ('CAT-007', 'Accessories', 'Jewelry', 20.0, 500.0),\n        ('CAT-008', 'Home', 'Bedding', 50.0, 300.0),\n        ('CAT-009', 'Home', 'Kitchen', 15.0, 200.0),\n        ('CAT-010', 'Beauty', 'Skincare', 15.0, 150.0),\n        ('CAT-011', 'Beauty', 'Makeup', 10.0, 80.0),\n        ('CAT-012', 'Electronics', 'Audio', 30.0, 400.0),\n    ]\n    ADJECTIVES = ['Premium', 'Classic', 'Modern', 'Essential', 'Deluxe', 'Ultra',\n                  'Pro', 'Signature', 'Everyday', 'Performance']\n    NOUNS = {\n        'Womens Clothing': ['Blouse', 'Dress', 'Jacket', 'Skirt', 'Cardigan'],\n        'Mens Clothing': ['Shirt', 'Polo', 'Chinos', 'Blazer', 'Sweater'],\n        'Kids Clothing': ['Tee', 'Hoodie', 'Shorts', 'Romper', 'Jacket'],\n        'Athletic Shoes': ['Runner', 'Trainer', 'Court Shoe', 'Cleats', 'Trail Shoe'],\n        'Casual Shoes': ['Loafer', 'Sneaker', 'Sandal', 'Boot', 'Slip-On'],\n        'Bags & Wallets': ['Tote', 'Crossbody', 'Wallet', 'Backpack', 'Clutch'],\n        'Jewelry': ['Necklace', 'Bracelet', 'Ring', 'Earrings', 'Watch'],\n        'Bedding': ['Sheet Set', 'Comforter', 'Pillow', 'Duvet', 'Blanket'],\n        'Kitchen': ['Mug Set', 'Cutting Board', 'Knife Set', 'Pan', 'Mixer'],\n        'Skincare': ['Moisturizer', 'Serum', 'Cleanser', 'Sunscreen', 'Mask'],\n        'Makeup': ['Foundation', 'Lipstick', 'Mascara', 'Palette', 'Blush'],\n        'Audio': ['Headphones', 'Earbuds', 'Speaker', 'Soundbar', 'Turntable'],\n    }\n\n    def process(self, seed):\n        rng = random.Random(seed)\n        cat_id, dept, cat_name, min_p, max_p = rng.choice(self.CATEGORIES)\n        brand = rng.choice(self.BRANDS[dept])\n        adj = rng.choice(self.ADJECTIVES)\n        noun = rng.choice(self.NOUNS[cat_name])\n        name = f\"{brand} {adj} {noun}\"\n        price = round(rng.uniform(min_p, max_p), 2)\n        weight = round(rng.uniform(0.2, 15.0), 1)\n        is_perishable = dept == 'Beauty' and rng.random() \u003C 0.3\n        ptype = 'daily_staple' if price \u003C 50 else ('mid_range' if price \u003C 150 else 'premium')\n        yield (seed, name, cat_id, dept, cat_name, brand, price, weight, is_perishable, ptype)\n$$;\n\nCREATE OR REPLACE TABLE PRODUCT_CATALOG AS\nSELECT p.*\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 2000)) g,\nTABLE(GENERATE_PRODUCT(SEQ4()::INT)) p;\n\nSELECT DEPARTMENT, COUNT(*) AS CNT, ROUND(AVG(PRICE), 2) AS AVG_PRICE\nFROM PRODUCT_CATALOG GROUP BY DEPARTMENT ORDER BY CNT DESC;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Creating the Inventory Table\nDuration: 1\n\nThe inventory table tracks stock levels per product per warehouse region. This is the deterministic availability layer — no ML needed for \"is it in stock?\"\n\n```sql\nUSE SCHEMA RAW;\n\nCREATE OR REPLACE TABLE INVENTORY AS\nSELECT\n  p.PRODUCT_ID,\n  regions.REGION,\n  regions.WAREHOUSE_NAME,\n  CASE\n    WHEN UNIFORM(0, 100, RANDOM()) \u003C 85 THEN UNIFORM(5, 500, RANDOM())\n    ELSE 0\n  END AS QTY_ON_HAND,\n  CASE regions.REGION\n    WHEN 'Northeast' THEN UNIFORM(1, 3, RANDOM())\n    WHEN 'Southeast' THEN UNIFORM(1, 4, RANDOM())\n    WHEN 'Midwest' THEN UNIFORM(2, 4, RANDOM())\n    WHEN 'South' THEN UNIFORM(2, 5, RANDOM())\n    WHEN 'West' THEN UNIFORM(2, 5, RANDOM())\n  END AS EST_DELIVERY_DAYS,\n  CURRENT_TIMESTAMP() AS LAST_UPDATED\nFROM PRODUCT_CATALOG p\nCROSS JOIN (\n  SELECT COLUMN1 AS REGION, COLUMN2 AS WAREHOUSE_NAME FROM VALUES\n    ('Northeast', 'WH-NE-01'),\n    ('Southeast', 'WH-SE-01'),\n    ('Midwest', 'WH-MW-01'),\n    ('South', 'WH-SO-01'),\n    ('West', 'WH-WE-01')\n) AS regions;\n\nSELECT REGION, COUNT(*) AS PRODUCTS, SUM(CASE WHEN QTY_ON_HAND \u003E 0 THEN 1 ELSE 0 END) AS IN_STOCK\nFROM INVENTORY GROUP BY REGION;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Generating Synthetic Customers\nDuration: 3\n\nThis UDTF generates 50,000 synthetic retail customers with demographics, loyalty tier assignment, and channel preferences.\n\n```sql\nUSE SCHEMA RAW;\n\nCREATE OR REPLACE FUNCTION GENERATE_RETAIL_CUSTOMER(SEED INT)\nRETURNS TABLE (\n  CUSTOMER_ID NUMBER,\n  EMAIL STRING,\n  PHONE STRING,\n  FIRST_NAME STRING,\n  LAST_NAME STRING,\n  DATE_OF_BIRTH DATE,\n  GENDER STRING,\n  STREET_ADDRESS STRING,\n  CITY STRING,\n  STATE STRING,\n  ZIP_CODE STRING,\n  CUSTOMER_SINCE_DATE DATE,\n  LOYALTY_TIER STRING,\n  HAS_APP BOOLEAN,\n  EMAIL_SUBSCRIBER BOOLEAN,\n  SMS_SUBSCRIBER BOOLEAN\n)\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nPACKAGES = ('faker')\nHANDLER = 'Generator'\nAS $$\nimport random\nfrom faker import Faker\n\nclass Generator:\n    TIERS = ['Bronze', 'Silver', 'Gold', 'Platinum']\n    TIER_WEIGHTS = [0.40, 0.30, 0.20, 0.10]\n\n    def process(self, seed):\n        fake = Faker('en_US')\n        Faker.seed(seed)\n        rng = random.Random(seed)\n        cid = seed + 1000000\n        gender = rng.choices(['Female', 'Male', 'Non-binary'], weights=[55, 40, 5])[0]\n        first = fake.first_name_female() if gender == 'Female' else fake.first_name_male() if gender == 'Male' else fake.first_name()\n        last = fake.last_name()\n        email = f\"{first.lower()}.{last.lower()}{rng.randint(1,999)}@{rng.choice(['gmail.com','yahoo.com','outlook.com','aol.com'])}\"\n        phone = fake.phone_number()\n        dob = fake.date_of_birth(minimum_age=18, maximum_age=75)\n        addr = fake.street_address()\n        city = fake.city()\n        state = fake.state_abbr()\n        zipcode = fake.zipcode()\n        since = fake.date_between(start_date='-10y', end_date='today')\n        tier = rng.choices(self.TIERS, weights=self.TIER_WEIGHTS)[0]\n        has_app = rng.random() \u003C 0.45\n        email_sub = rng.random() \u003C 0.70\n        sms_sub = rng.random() \u003C 0.35\n        yield (cid, email, phone, first, last, dob, gender, addr, city, state, zipcode, since, tier, has_app, email_sub, sms_sub)\n$$;\n\nCREATE OR REPLACE TABLE CUSTOMERS AS\nSELECT c.* FROM TABLE(GENERATOR(ROWCOUNT =\u003E 50000)) g,\nTABLE(GENERATE_RETAIL_CUSTOMER(SEQ4()::INT)) c;\n\nSELECT LOYALTY_TIER, COUNT(*) AS CNT,\n  ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (), 1) AS PCT\nFROM CUSTOMERS GROUP BY LOYALTY_TIER ORDER BY CNT DESC;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Generating Synthetic Events\nDuration: 5\n\nThis UDTF generates a realistic event stream for each customer: browsing, cart activity, purchases, post-purchase events, loyalty events, and in-store visits. Events include product IDs from the catalog to enable recommendation model training.\n\n```sql\nUSE SCHEMA RAW;\n\nCREATE OR REPLACE FUNCTION GENERATE_RETAIL_EVENTS(\n  CUSTOMER_ID INT, HAS_APP BOOLEAN, CITY STRING, STATE STRING, LOYALTY_TIER STRING\n)\nRETURNS TABLE (\n  EVENT_ID STRING,\n  CUSTOMER_ID NUMBER,\n  EVENT_TYPE STRING,\n  EVENT_TIMESTAMP TIMESTAMP_NTZ,\n  CHANNEL STRING,\n  SESSION_ID STRING,\n  DEVICE_TYPE STRING,\n  PRODUCT_ID NUMBER,\n  PRODUCT_CATEGORY STRING,\n  PRODUCT_PRICE FLOAT,\n  QUANTITY INT,\n  CART_VALUE FLOAT,\n  ORDER_ID STRING,\n  ORDER_TOTAL FLOAT,\n  STORE_ID INT,\n  PROMO_CODE STRING,\n  POINTS_EARNED INT,\n  POINTS_REDEEMED INT\n)\nLANGUAGE PYTHON\nRUNTIME_VERSION = '3.11'\nHANDLER = 'Generator'\nAS $$\nimport random\nimport uuid\nfrom datetime import datetime, timedelta\n\nclass Generator:\n    EVENT_TYPES = {\n        'browse': ['page_view', 'product_view', 'category_browse', 'search', 'add_to_wishlist'],\n        'cart': ['add_to_cart', 'remove_from_cart', 'cart_view', 'abandoned_cart'],\n        'purchase': ['checkout_start', 'payment', 'order_complete'],\n        'post': ['order_shipped', 'order_delivered', 'return_initiated', 'review_submitted'],\n        'loyalty': ['points_earned', 'points_redeemed', 'tier_upgrade'],\n        'store': ['store_visit', 'pos_purchase'],\n    }\n    CATEGORIES = ['CAT-001','CAT-002','CAT-003','CAT-004','CAT-005','CAT-006',\n                  'CAT-007','CAT-008','CAT-009','CAT-010','CAT-011','CAT-012']\n    PROMOS = ['SPRING20', 'SUMMER40', 'BTS25', 'BLACKFRI', 'HOLIDAY15', 'NEWYEAR30', 'VIPONLY', 'FREESHIP']\n    TIER_ACTIVITY = {'Bronze': 40, 'Silver': 70, 'Gold': 110, 'Platinum': 160}\n\n    def process(self, customer_id, has_app, city, state, loyalty_tier):\n        rng = random.Random(customer_id)\n        num_events = self.TIER_ACTIVITY.get(loyalty_tier, 60) + rng.randint(-20, 30)\n        num_events = max(10, num_events)\n        base_date = datetime.now() - timedelta(days=365)\n        sessions = rng.randint(5, num_events // 3 + 1)\n        fav_cats = rng.sample(self.CATEGORIES, k=min(3, len(self.CATEGORIES)))\n        order_counter = 0\n\n        for _ in range(num_events):\n            ts = base_date + timedelta(seconds=rng.randint(0, 365 * 86400))\n            session_id = f\"S-{customer_id}-{rng.randint(1, sessions)}\"\n            group = rng.choices(\n                ['browse', 'cart', 'purchase', 'post', 'loyalty', 'store'],\n                weights=[35, 15, 8, 5, 5, 12 if not has_app else 7]\n            )[0]\n            evt = rng.choice(self.EVENT_TYPES[group])\n\n            if has_app:\n                channel = rng.choices(['Web', 'App', 'Store'], weights=[40, 40, 20])[0]\n                device = rng.choice(['Mobile', 'Desktop', 'Tablet']) if channel != 'Store' else 'POS'\n            else:\n                channel = rng.choices(['Web', 'Store'], weights=[55, 45])[0]\n                device = rng.choice(['Mobile', 'Desktop']) if channel != 'Store' else 'POS'\n\n            cat = rng.choices(self.CATEGORIES, weights=[3 if c in fav_cats else 1 for c in self.CATEGORIES])[0]\n            cat_idx = self.CATEGORIES.index(cat)\n            prod_id = cat_idx * 200 + rng.randint(0, 160)\n            price = round(rng.uniform(10, 300), 2)\n            qty = rng.choice([1, 1, 1, 2, 3]) if evt in ('add_to_cart', 'order_complete', 'pos_purchase') else 0\n            cart_val = round(price * qty, 2) if qty \u003E 0 else None\n            order_id = None\n            order_total = None\n            store_id = rng.randint(0, 499) if channel == 'Store' else None\n            promo = rng.choice(self.PROMOS) if evt == 'order_complete' and rng.random() \u003C 0.15 else None\n            pts_earned = rng.randint(10, 200) if evt == 'points_earned' else 0\n            pts_redeemed = rng.randint(50, 500) if evt == 'points_redeemed' else 0\n\n            if evt in ('order_complete', 'pos_purchase'):\n                order_counter += 1\n                order_id = f\"ORD-{customer_id}-{order_counter}\"\n                order_total = round(price * max(qty, 1) * rng.uniform(0.85, 1.15), 2)\n\n            yield (\n                str(uuid.UUID(int=rng.getrandbits(128))),\n                customer_id, evt, ts, channel, session_id, device,\n                prod_id, cat, price, qty, cart_val, order_id, order_total,\n                store_id, promo, pts_earned, pts_redeemed\n            )\n$$;\n\nCREATE OR REPLACE TABLE EVENT_STREAM AS\nSELECT e.* FROM CUSTOMERS c,\nTABLE(GENERATE_RETAIL_EVENTS(\n  c.CUSTOMER_ID, c.HAS_APP, c.CITY, c.STATE, c.LOYALTY_TIER\n)) e;\n\nSELECT EVENT_TYPE, COUNT(*) AS CNT\nFROM EVENT_STREAM GROUP BY EVENT_TYPE ORDER BY CNT DESC LIMIT 15;\n```\n\nThis will generate approximately 3-5 million events depending on tier distribution. The query should take 2-4 minutes on a MEDIUM warehouse.\n\n\u003C!-- ------------------------ --\u003E\n## Building the Customer 360 Gold Layer\nDuration: 3\n\nThe Customer 360 Dynamic Table aggregates raw events into a rich customer feature set. It refreshes automatically as new data arrives.\n\n```sql\nUSE SCHEMA GOLD;\n\nCREATE OR REPLACE DYNAMIC TABLE CUSTOMER_360\n  TARGET_LAG = '30 minutes'\n  WAREHOUSE = RETAIL_QS_WH\nAS\nWITH purchase_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    COUNT(DISTINCT ORDER_ID) AS TOTAL_ORDERS,\n    SUM(ORDER_TOTAL) AS LIFETIME_SPEND,\n    AVG(ORDER_TOTAL) AS AVG_ORDER_VALUE,\n    MAX(EVENT_TIMESTAMP) AS LAST_PURCHASE_DATE,\n    COUNT(DISTINCT PRODUCT_CATEGORY) AS CATEGORIES_PURCHASED,\n    DATEDIFF('day', MAX(EVENT_TIMESTAMP), CURRENT_TIMESTAMP()) AS DAYS_SINCE_PURCHASE\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('order_complete', 'pos_purchase')\n  GROUP BY CUSTOMER_ID\n),\nbrowse_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    COUNT(*) AS TOTAL_BROWSE_EVENTS,\n    COUNT(DISTINCT SESSION_ID) AS TOTAL_SESSIONS,\n    COUNT(DISTINCT PRODUCT_CATEGORY) AS CATEGORIES_BROWSED,\n    MODE(PRODUCT_CATEGORY) AS FAVORITE_BROWSE_CATEGORY,\n    AVG(PRODUCT_PRICE) AS AVG_BROWSED_PRICE\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('product_view', 'category_browse', 'search')\n  GROUP BY CUSTOMER_ID\n),\ncart_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    SUM(CASE WHEN EVENT_TYPE = 'add_to_cart' THEN 1 ELSE 0 END) AS ADD_TO_CART_COUNT,\n    SUM(CASE WHEN EVENT_TYPE = 'abandoned_cart' THEN 1 ELSE 0 END) AS ABANDONED_CART_COUNT\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('add_to_cart', 'abandoned_cart')\n  GROUP BY CUSTOMER_ID\n),\nchannel_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    MODE(CHANNEL) AS PREFERRED_CHANNEL,\n    MODE(DEVICE_TYPE) AS PREFERRED_DEVICE,\n    SUM(CASE WHEN CHANNEL = 'Store' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS STORE_RATIO\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  GROUP BY CUSTOMER_ID\n),\nloyalty_stats AS (\n  SELECT\n    CUSTOMER_ID,\n    SUM(POINTS_EARNED) AS TOTAL_POINTS_EARNED,\n    SUM(POINTS_REDEEMED) AS TOTAL_POINTS_REDEEMED\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('points_earned', 'points_redeemed')\n  GROUP BY CUSTOMER_ID\n),\nfavorite_product_cat AS (\n  SELECT\n    CUSTOMER_ID,\n    MODE(PRODUCT_CATEGORY) AS FAVORITE_PURCHASE_CATEGORY\n  FROM RETAIL_RECOMMENDATION_QS.RAW.EVENT_STREAM\n  WHERE EVENT_TYPE IN ('order_complete', 'pos_purchase')\n  GROUP BY CUSTOMER_ID\n)\nSELECT\n  c.CUSTOMER_ID,\n  c.FIRST_NAME,\n  c.LAST_NAME,\n  c.EMAIL,\n  c.GENDER,\n  c.DATE_OF_BIRTH,\n  DATEDIFF('year', c.DATE_OF_BIRTH, CURRENT_DATE()) AS AGE,\n  c.CITY,\n  c.STATE,\n  c.ZIP_CODE,\n  c.CUSTOMER_SINCE_DATE,\n  DATEDIFF('month', c.CUSTOMER_SINCE_DATE, CURRENT_DATE()) AS TENURE_MONTHS,\n  c.LOYALTY_TIER,\n  c.HAS_APP,\n  c.EMAIL_SUBSCRIBER,\n  c.SMS_SUBSCRIBER,\n  COALESCE(p.TOTAL_ORDERS, 0) AS TOTAL_ORDERS,\n  COALESCE(p.LIFETIME_SPEND, 0) AS LIFETIME_SPEND,\n  COALESCE(p.AVG_ORDER_VALUE, 0) AS AVG_ORDER_VALUE,\n  p.LAST_PURCHASE_DATE,\n  COALESCE(p.DAYS_SINCE_PURCHASE, 999) AS DAYS_SINCE_PURCHASE,\n  COALESCE(p.CATEGORIES_PURCHASED, 0) AS CATEGORIES_PURCHASED,\n  COALESCE(b.TOTAL_BROWSE_EVENTS, 0) AS TOTAL_BROWSE_EVENTS,\n  COALESCE(b.TOTAL_SESSIONS, 0) AS TOTAL_SESSIONS,\n  COALESCE(b.CATEGORIES_BROWSED, 0) AS CATEGORIES_BROWSED,\n  b.FAVORITE_BROWSE_CATEGORY,\n  COALESCE(b.AVG_BROWSED_PRICE, 0) AS AVG_BROWSED_PRICE,\n  COALESCE(ct.ADD_TO_CART_COUNT, 0) AS ADD_TO_CART_COUNT,\n  COALESCE(ct.ABANDONED_CART_COUNT, 0) AS ABANDONED_CART_COUNT,\n  CASE WHEN COALESCE(ct.ADD_TO_CART_COUNT, 0) \u003E 0\n    THEN ROUND(COALESCE(ct.ABANDONED_CART_COUNT, 0) * 1.0 / ct.ADD_TO_CART_COUNT, 3)\n    ELSE 0 END AS CART_ABANDONMENT_RATE,\n  ch.PREFERRED_CHANNEL,\n  ch.PREFERRED_DEVICE,\n  COALESCE(ch.STORE_RATIO, 0) AS STORE_RATIO,\n  COALESCE(l.TOTAL_POINTS_EARNED, 0) AS TOTAL_POINTS_EARNED,\n  COALESCE(l.TOTAL_POINTS_REDEEMED, 0) AS TOTAL_POINTS_REDEEMED,\n  fpc.FAVORITE_PURCHASE_CATEGORY,\n  CASE\n    WHEN COALESCE(p.DAYS_SINCE_PURCHASE, 999) \u003C= 30 AND COALESCE(p.TOTAL_ORDERS, 0) \u003E= 5 AND COALESCE(p.LIFETIME_SPEND, 0) \u003E= 500 THEN 'Champion'\n    WHEN COALESCE(p.TOTAL_ORDERS, 0) \u003E= 3 AND COALESCE(p.LIFETIME_SPEND, 0) \u003E= 200 THEN 'Loyal'\n    WHEN COALESCE(p.DAYS_SINCE_PURCHASE, 999) \u003C= 60 THEN 'Active'\n    WHEN COALESCE(p.DAYS_SINCE_PURCHASE, 999) \u003C= 180 THEN 'At_Risk'\n    ELSE 'Dormant'\n  END AS RFM_SEGMENT,\n  ROUND(\n    LEAST(100,\n      COALESCE(b.TOTAL_SESSIONS, 0) * 0.3 +\n      COALESCE(p.TOTAL_ORDERS, 0) * 5 +\n      COALESCE(l.TOTAL_POINTS_EARNED, 0) * 0.01 +\n      CASE WHEN c.HAS_APP THEN 10 ELSE 0 END +\n      CASE WHEN c.EMAIL_SUBSCRIBER THEN 5 ELSE 0 END\n    ), 1\n  ) AS ENGAGEMENT_SCORE\nFROM RETAIL_RECOMMENDATION_QS.RAW.CUSTOMERS c\nLEFT JOIN purchase_stats p ON c.CUSTOMER_ID = p.CUSTOMER_ID\nLEFT JOIN browse_stats b ON c.CUSTOMER_ID = b.CUSTOMER_ID\nLEFT JOIN cart_stats ct ON c.CUSTOMER_ID = ct.CUSTOMER_ID\nLEFT JOIN channel_stats ch ON c.CUSTOMER_ID = ch.CUSTOMER_ID\nLEFT JOIN loyalty_stats l ON c.CUSTOMER_ID = l.CUSTOMER_ID\nLEFT JOIN favorite_product_cat fpc ON c.CUSTOMER_ID = fpc.CUSTOMER_ID;\n\nSELECT RFM_SEGMENT, COUNT(*) AS CNT, ROUND(AVG(ENGAGEMENT_SCORE), 1) AS AVG_ENGAGEMENT\nFROM CUSTOMER_360 GROUP BY RFM_SEGMENT ORDER BY CNT DESC;\n```\n\n\u003E **Note:** Snowflake may select FULL refresh mode for this Dynamic Table because of `AVG()` aggregations on FLOAT columns combined with JOINs. This does not affect correctness — it simply means each refresh recomputes the full table rather than incrementally updating. For production, consider casting FLOAT columns to `DECIMAL(18,4)` to enable incremental refresh.\n\n\u003C!-- ------------------------ --\u003E\n## Feature Store, Model Training, and Registration\nDuration: 15\n\nThis step uses a Snowflake Notebook to:\n\n1. **Set up the Feature Store** with two feature views (customer features and product features) for training data retrieval\n2. **Train the propensity model** (XGBoost) — predicts customer affinity per product category, run as daily batch\n3. **Train the ranking model** (XGBoost) — scores customer-product pairs in real-time\n4. **Register both models** in the Snowflake Model Registry\n5. **Deploy the ranking model** to SPCS for real-time inference\n\n### Running the Notebook\n\n1. Download [`01_train_and_register.ipynb`](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/retail-realtime-recommendations/notebooks/01_train_and_register.ipynb) from the companion repository\n2. Open Snowsight and navigate to **Notebooks**\n3. Click **+ Notebook** → **Import .ipynb file** and upload the downloaded notebook\n4. Before running, add the required packages via the **Packages** button in the top-right:\n   - `snowflake-ml-python` (\u003E= 1.7.0)\n   - `xgboost`\n   - `scikit-learn`\n5. Set the role to `RETAIL_QS_ROLE` and warehouse to `RETAIL_QS_WH`\n6. Run all cells sequentially\n\n\u003E **Note:** This notebook must be run interactively in Snowsight — it cannot be executed headlessly via `EXECUTE NOTEBOOK` because the required packages must be added through the UI package selector.\n\nAfter the notebook completes, verify the models are registered:\n\n```sql\nUSE ROLE RETAIL_QS_ROLE;\nSHOW MODELS IN SCHEMA RETAIL_RECOMMENDATION_QS.ML_REGISTRY;\n```\n\nYou should see two models:\n- `CATEGORY_PROPENSITY_MODEL` — batch propensity (target_platforms: WAREHOUSE)\n- `PRODUCT_RANKING_MODEL` — real-time ranking (target_platforms: SNOWPARK_CONTAINER_SERVICES)\n\n\u003C!-- ------------------------ --\u003E\n## Running Batch Propensity Scoring\nDuration: 3\n\nAfter training, run the propensity model in batch mode to score all customers. This creates a table of per-customer propensity scores.\n\n\u003E **Note:** Model inference calls (`MODEL!PREDICT_PROBA`) are not currently supported inside Dynamic Table definitions. We use a CTAS statement instead, which you can schedule with a Snowflake Task for daily refresh.\n\n```sql\nUSE ROLE RETAIL_QS_ROLE;\nUSE DATABASE RETAIL_RECOMMENDATION_QS;\nUSE SCHEMA GOLD;\nUSE WAREHOUSE RETAIL_QS_WH;\n\nCREATE OR REPLACE TABLE CUSTOMER_PROPENSITY AS\nWITH customer_features AS (\n  SELECT\n    CUSTOMER_ID,\n    AGE,\n    TENURE_MONTHS,\n    CASE LOYALTY_TIER\n      WHEN 'Bronze' THEN 0 WHEN 'Silver' THEN 1\n      WHEN 'Gold' THEN 2 WHEN 'Platinum' THEN 3\n    END AS LOYALTY_TIER_NUM,\n    TOTAL_ORDERS,\n    LIFETIME_SPEND,\n    AVG_ORDER_VALUE,\n    DAYS_SINCE_PURCHASE,\n    CATEGORIES_PURCHASED,\n    TOTAL_BROWSE_EVENTS,\n    TOTAL_SESSIONS,\n    CART_ABANDONMENT_RATE,\n    ENGAGEMENT_SCORE\n  FROM RETAIL_RECOMMENDATION_QS.GOLD.CUSTOMER_360\n),\ncategories AS (\n  SELECT CATEGORY_ID FROM RETAIL_RECOMMENDATION_QS.RAW.PRODUCT_CATEGORIES\n)\nSELECT\n  cf.CUSTOMER_ID,\n  cat.CATEGORY_ID,\n  RETAIL_RECOMMENDATION_QS.ML_REGISTRY.CATEGORY_PROPENSITY_MODEL!PREDICT_PROBA(\n    cf.AGE, cf.TENURE_MONTHS, cf.LOYALTY_TIER_NUM, cf.TOTAL_ORDERS,\n    cf.LIFETIME_SPEND, cf.AVG_ORDER_VALUE, cf.DAYS_SINCE_PURCHASE,\n    cf.CATEGORIES_PURCHASED, cf.TOTAL_BROWSE_EVENTS, cf.TOTAL_SESSIONS,\n    cf.CART_ABANDONMENT_RATE, cf.ENGAGEMENT_SCORE\n  ):output_feature_1::FLOAT AS PROPENSITY_SCORE\nFROM customer_features cf\nCROSS JOIN categories cat;\n\nSELECT * FROM CUSTOMER_PROPENSITY\nWHERE CUSTOMER_ID = 1000000\nORDER BY PROPENSITY_SCORE DESC;\n```\n\n\u003E **Note:** The argument order must match the model's function signature exactly. Run `SHOW FUNCTIONS IN MODEL RETAIL_RECOMMENDATION_QS.ML_REGISTRY.CATEGORY_PROPENSITY_MODEL;` to verify. The `PREDICT_PROBA` call uses the model's default version.\n\n\u003C!-- ------------------------ --\u003E\n## Setting Up Snowflake Postgres for Low-Latency Serving\nDuration: 5\n\nTo achieve sub-100ms end-to-end latency for the recommendation API, we use **Snowflake Postgres** — a managed PostgreSQL instance that runs in the same AWS region as your SPCS services, providing single-digit-millisecond lookups. Without Postgres, the orchestrator falls back to Snowflake warehouse queries which add ~500-600ms of overhead.\n\n\u003E **Note:** Snowflake Postgres instances are account-level objects (not database-scoped). Network rules for Postgres instances must use `MODE = POSTGRES_INGRESS`.\n\n### Create the Postgres Instance\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE POSTGRES INSTANCE IF NOT EXISTS RETAIL_QS_PG\n  COMPUTE_FAMILY = 'STANDARD_M'\n  STORAGE_SIZE_GB = 50\n  AUTHENTICATION_AUTHORITY = POSTGRES;\n```\n\n\u003E **Important:** The `CREATE` output returns the admin password in the `access_roles` JSON. **Save it immediately** — it cannot be retrieved later. Note the `host` value as well; you will need both for data loading and the orchestrator service.\n\nWait for the instance to reach `READY` state:\n\n```sql\nSHOW POSTGRES INSTANCES;\n```\n\n### Create Network Rules and Access Policies\n\nThe Postgres instance needs an ingress network rule to allow connections, and the SPCS orchestrator needs an egress rule to reach it:\n\n```sql\n-- Allow connections to the Postgres instance (POSTGRES_INGRESS mode required)\nCREATE OR REPLACE NETWORK RULE RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_PG_INGRESS\n  TYPE = IPV4\n  MODE = POSTGRES_INGRESS\n  VALUE_LIST = ('0.0.0.0/0');\n\nCREATE OR REPLACE NETWORK POLICY RETAIL_QS_PG_POLICY\n  ALLOWED_NETWORK_RULE_LIST = ('RETAIL_RECOMMENDATION_QS.RAW.RETAIL_QS_PG_INGRESS');\n\nALTER POSTGRES INSTANCE RETAIL_QS_PG SET NETWORK_POLICY = RETAIL_QS_PG_POLICY;\n\n-- Egress rule for SPCS orchestrator to reach Postgres\nCREATE OR REPLACE NETWORK RULE RETAIL_RECOMMENDATION_QS.RAW.PG_EGRESS_RULE\n  TYPE = HOST_PORT\n  MODE = EGRESS\n  VALUE_LIST = ('\u003Cyour-pg-host\u003E:5432');\n\n-- Store the Postgres password as a secret for SPCS\nCREATE OR REPLACE SECRET RETAIL_RECOMMENDATION_QS.RAW.PG_PASSWORD\n  TYPE = GENERIC_STRING\n  SECRET_STRING = '\u003Cyour-admin-password\u003E';\n\n-- External access integration for SPCS\nCREATE OR REPLACE EXTERNAL ACCESS INTEGRATION PG_ACCESS_INTEGRATION\n  ALLOWED_NETWORK_RULES = (RETAIL_RECOMMENDATION_QS.RAW.PG_EGRESS_RULE)\n  ALLOWED_AUTHENTICATION_SECRETS = (RETAIL_RECOMMENDATION_QS.RAW.PG_PASSWORD)\n  ENABLED = TRUE;\n```\n\n\u003E **Important:** Replace `\u003Cyour-pg-host\u003E` with the host from `DESCRIBE POSTGRES INSTANCE RETAIL_QS_PG` and `\u003Cyour-admin-password\u003E` with the `admin_password` value.\n\n\u003C!-- ------------------------ --\u003E\n## Loading Data into Postgres\nDuration: 5\n\nWith the Postgres instance running, connect via `psql` to create the serving tables and load data from Snowflake.\n\n### Connect to Postgres\n\n```bash\npsql \"host=\u003Cyour-pg-host\u003E port=5432 dbname=postgres user=snowflake_admin password=\u003Cyour-admin-password\u003E sslmode=require\"\n```\n\n### Create Tables\n\n```sql\nCREATE TABLE IF NOT EXISTS customer_features (\n  customer_id BIGINT PRIMARY KEY,\n  age INT, tenure_months INT, loyalty_tier_num INT,\n  total_orders INT, lifetime_spend DOUBLE PRECISION,\n  avg_order_value DOUBLE PRECISION, days_since_purchase INT,\n  categories_purchased INT, total_browse_events INT,\n  total_sessions INT, avg_browsed_price DOUBLE PRECISION,\n  add_to_cart_count INT, cart_abandonment_rate DOUBLE PRECISION,\n  store_ratio DOUBLE PRECISION, engagement_score DOUBLE PRECISION\n);\n\nCREATE TABLE IF NOT EXISTS customer_propensity (\n  customer_id BIGINT,\n  category_id TEXT,\n  propensity_score DOUBLE PRECISION,\n  PRIMARY KEY (customer_id, category_id)\n);\n\nCREATE TABLE IF NOT EXISTS inventory (\n  product_id BIGINT,\n  region TEXT,\n  warehouse_name TEXT,\n  qty_on_hand INT,\n  est_delivery_days INT,\n  PRIMARY KEY (region, product_id)\n);\n```\n\nThis creates three tables:\n\n| Table | Rows | Primary Key | Purpose |\n|-------|------|-------------|---------|\n| `customer_features` | 50K | `customer_id` | Customer 360 features (1 row per customer) |\n| `customer_propensity` | 600K | `(customer_id, category_id)` | Category propensity scores (12 rows per customer) |\n| `inventory` | ~8.5K | `(region, product_id)` | Regional stock levels |\n\n\u003E **Design Note:** Customer features and propensity scores are kept in separate tables because they have different cardinality (1:1 vs 1:12), different query patterns (point lookup vs top-N ranked), and different refresh cadences. Denormalizing would bloat the table 12x with no benefit.\n\n### Load Data from Snowflake\n\nDownload [`load_pg.py`](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/retail-realtime-recommendations/load_pg.py) from the companion repository. This script exports data from Snowflake and loads it into Postgres using `snowflake-connector-python` and `psycopg2`:\n\n```bash\npip install snowflake-connector-python psycopg2-binary\nexport SNOWFLAKE_CONNECTION_NAME=\u003Cyour-connection-name\u003E\nexport PG_HOST=\u003Cyour-pg-host\u003E\nexport PG_PASSWORD=\u003Cyour-admin-password\u003E\npython load_pg.py\n```\n\n### Verify Row Counts\n\nIn `psql`:\n\n```sql\nSELECT 'customer_features' AS tbl, COUNT(*) FROM customer_features\nUNION ALL SELECT 'customer_propensity', COUNT(*) FROM customer_propensity\nUNION ALL SELECT 'inventory', COUNT(*) FROM inventory;\n-- Expected: 50000, 600000, ~8500\n```\n\n\u003C!-- ------------------------ --\u003E\n## Deploying the Ranking Model Service\nDuration: 5\n\nDeploy the real-time ranking model as an SPCS service. This creates a REST endpoint that the orchestrator will call.\n\n```sql\nUSE ROLE RETAIL_QS_ROLE;\n```\n\nThe ranking model was registered with `target_platforms=[\"SNOWPARK_CONTAINER_SERVICES\"]` in the notebook. Deploy it:\n\n```python\n# Run this in the notebook (cell at the end) or in a Snowflake Python worksheet:\nfrom snowflake.ml.registry import Registry\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nreg = Registry(session=session, database_name=\"RETAIL_RECOMMENDATION_QS\",\n               schema_name=\"ML_REGISTRY\")\nmv = reg.get_model(\"PRODUCT_RANKING_MODEL\").default\nmv.create_service(\n    service_name=\"RANKING_MODEL_SERVICE\",\n    service_compute_pool=\"RETAIL_QS_COMPUTE_POOL\",\n    ingress_enabled=True\n)\n```\n\nWait for the service to start, then verify:\n\n```sql\nCALL SYSTEM$GET_SERVICE_STATUS('RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE');\nSHOW ENDPOINTS IN SERVICE RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE;\n```\n\nNote the `ingress_url` — this is the external endpoint for the ranking model.\n\n\u003C!-- ------------------------ --\u003E\n## Building and Deploying the Orchestrator\nDuration: 8\n\nThe orchestrator is a FastAPI service that coordinates the recommendation pipeline: fetches customer features, propensity scores, and inventory from Snowflake Postgres in parallel, computes pair features, calls the ranking model, and returns top-N recommendations. It falls back to Snowflake warehouse queries if Postgres is not configured.\n\n### Get the Orchestrator Code\n\nDownload the orchestrator files from the [companion repository](https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/retail-realtime-recommendations/orchestrator):\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sf-samples.git\ncd sf-samples/samples/retail-realtime-recommendations/orchestrator\n```\n\n### Review the Orchestrator Code\n\nThe orchestrator (`app.py`) handles the following flow per request:\n\n1. Receives `POST /recommend {\"customer_id\": int, \"zip_code\": str, \"top_n\": 5}`\n2. Fetches customer features from Postgres (falls back to Snowflake warehouse query)\n3. Fetches propensity scores from Postgres (falls back to Snowflake warehouse query)\n4. Queries available products from Postgres inventory (falls back to Snowflake warehouse query)\n5. All three fetches run in parallel using a thread pool\n6. Computes customer-product pair features\n7. Calls the ranking model service\n8. Returns top-N ranked products with details and estimated delivery\n\n### Build the Container\n\nFirst, get the image repository URL:\n\n```sql\nSHOW IMAGE REPOSITORIES IN SCHEMA RETAIL_RECOMMENDATION_QS.RAW;\n```\n\nThen build and push from your terminal:\n\n```bash\nREPO_URL=\u003Crepository_url from above\u003E\n\ndocker login $REPO_URL --username \u003CSNOWFLAKE_USER\u003E\ndocker build --platform linux/amd64 -t $REPO_URL/retail_orchestrator:latest .\ndocker push $REPO_URL/retail_orchestrator:latest\n```\n\n\u003E **Tip:** If you modify `app.py` and rebuild, Docker may cache layers. Use `docker build --platform linux/amd64 --no-cache` to force a clean rebuild and ensure the pushed image reflects your changes.\n\n### Deploy the Orchestrator Service\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nUSE DATABASE RETAIL_RECOMMENDATION_QS;\nUSE SCHEMA RAW;\n\nCREATE SERVICE ORCHESTRATOR_SERVICE\n  IN COMPUTE POOL RETAIL_QS_COMPUTE_POOL\n  EXTERNAL_ACCESS_INTEGRATIONS = (PG_ACCESS_INTEGRATION)\n  MIN_INSTANCES = 1\n  MAX_INSTANCES = 1\n  FROM SPECIFICATION $$\nspec:\n  containers:\n  - name: orchestrator\n    image: /retail_recommendation_qs/raw/retail_qs_images/retail_orchestrator:latest\n    env:\n      SNOWFLAKE_WAREHOUSE: RETAIL_QS_WH\n      PYTHONUNBUFFERED: \"1\"\n      PG_HOST: \u003Cyour-pg-host\u003E\n      PG_USER: snowflake_admin\n      PG_DB: postgres\n      PG_PORT: \"5432\"\n    secrets:\n    - snowflakeSecret: RETAIL_RECOMMENDATION_QS.RAW.PG_PASSWORD\n      secretKeyRef: secret_string\n      envVarName: PG_PASSWORD\n    resources:\n      requests:\n        cpu: 0.5\n        memory: 512M\n      limits:\n        cpu: 2\n        memory: 2G\n  endpoints:\n  - name: ui\n    port: 8000\n    public: true\n$$;\n```\n\n\u003E **Important:** Replace `\u003Cyour-pg-host\u003E` with your Postgres instance hostname. The service must be created with `EXTERNAL_ACCESS_INTEGRATIONS` to allow egress to Postgres — this requires `ACCOUNTADMIN` role.\n\nWait for the service to start:\n\n```sql\nCALL SYSTEM$GET_SERVICE_STATUS('ORCHESTRATOR_SERVICE');\nSELECT SYSTEM$GET_SERVICE_LOGS('RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_SERVICE', 0, 'orchestrator', 20);\n```\n\nYou should see in the logs:\n```\nPostgres connected: 50000 features, 600000 propensity, 8474 inventory rows\nReady - 2000 products cached, ..., backend=postgres\n```\n\nGet the public endpoint:\n\n```sql\nSHOW ENDPOINTS IN SERVICE ORCHESTRATOR_SERVICE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Testing the Recommendations\nDuration: 3\n\n### Test via Interactive UI\n\nNavigate to `https://{ORCHESTRATOR_INGRESS_URL}/` in your browser (you will need to authenticate with your Snowflake credentials via OAuth). This loads a self-contained test page where you can enter a Customer ID, Zip Code, and Top N, then click **Get Recommendations** to call the API.\n\n\u003E **Note:** The default FastAPI Swagger UI (`/docs`) does not work in SPCS because the ingress proxy blocks the external CDN assets it requires. The built-in test page at `/` is self-contained and works within SPCS.\n\n### Test via Health Check\n\nNavigate to `https://{ORCHESTRATOR_INGRESS_URL}/health` to verify the service is running. You should see:\n\n```json\n{\"status\": \"ok\", \"products_cached\": 2000, \"backend\": \"postgres\"}\n```\n\nThe `backend` field confirms whether the service is using Postgres (`postgres`) or falling back to Snowflake warehouse queries (`snowflake`).\n\n### Test via cURL\n\nCreate a [programmatic access token (PAT)](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens) for authenticating to the SPCS ingress endpoint:\n\n```sql\nALTER USER ADD PAT ORCHESTRATOR_PAT DAYS_TO_EXPIRY = 7;\n```\n\nCopy the `token_secret` from the output and use it in the cURL:\n\n```bash\ncurl -s \"https://{ORCHESTRATOR_INGRESS_URL}/recommend?customer_id=1000000&zip_code=10001&top_n=5\" \\\n  -H 'Authorization: Snowflake Token=\"\u003CPAT_TOKEN\u003E\"' | python3 -m json.tool\n```\n\n### Test via GET URL\n\nYou can also test directly in your browser URL bar:\n\n```\nhttps://{ORCHESTRATOR_INGRESS_URL}/recommend?customer_id=1000000&zip_code=10001&top_n=5\n```\n\nExpected recommendation response:\n\n```json\n{\n  \"customer_id\": 1000000,\n  \"zip_code\": \"10001\",\n  \"region\": \"Northeast\",\n  \"recommendations\": [\n    {\n      \"rank\": 1,\n      \"product_id\": 42,\n      \"product_name\": \"NorthStyle Premium Blouse\",\n      \"category\": \"Womens Clothing\",\n      \"department\": \"Apparel\",\n      \"price\": 89.99,\n      \"score\": 0.9234,\n      \"in_stock_qty\": 145,\n      \"est_delivery_days\": 2\n    }\n  ],\n  \"timings\": {\n    \"features_ms\": 15.5,\n    \"propensity_ms\": 16.9,\n    \"inventory_ms\": 19.6,\n    \"candidates\": 1723,\n    \"pair_features_ms\": 1.4,\n    \"model_ms\": 56.2,\n    \"model_ok\": true,\n    \"total_ms\": 77.7,\n    \"backend\": \"postgres\"\n  }\n}\n```\n\n\u003E **Performance:** With Snowflake Postgres, all three data lookups (features, propensity, inventory) complete in parallel in under 20ms. The ranking model inference (~56ms) is the primary latency contributor, bringing total server-side latency to ~78ms — a **91% reduction** from the ~858ms baseline using direct Snowflake warehouse queries.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\nDuration: 2\n\nTo fully remove everything created in this quickstart:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Services\nALTER SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_SERVICE SUSPEND;\nDROP SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.RAW.ORCHESTRATOR_SERVICE;\nALTER SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE SUSPEND;\nDROP SERVICE IF EXISTS RETAIL_RECOMMENDATION_QS.ML_REGISTRY.RANKING_MODEL_SERVICE;\n\n-- Compute pool\nDROP COMPUTE POOL IF EXISTS RETAIL_QS_COMPUTE_POOL;\n\n-- Postgres instance and networking\nDROP POSTGRES INSTANCE IF EXISTS RETAIL_QS_PG;\nDROP NETWORK POLICY IF EXISTS RETAIL_QS_PG_POLICY;\nDROP INTEGRATION IF EXISTS PG_ACCESS_INTEGRATION;\n\n-- Database (drops all schemas, tables, models, feature views, dynamic tables,\n-- network rules, secrets, and stages)\nDROP DATABASE IF EXISTS RETAIL_RECOMMENDATION_QS;\n\n-- User and role\nDROP USER IF EXISTS RETAIL_QS_SVC_USER;\nDROP ROLE IF EXISTS RETAIL_QS_ROLE;\n\n-- Warehouse\nDROP WAREHOUSE IF EXISTS RETAIL_QS_WH;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Next Steps\nDuration: 2\n\n### What You Built\n\nYou built a complete real-time product recommendation engine for retail, powered entirely by Snowflake:\n\n- **Synthetic data layer** with 50K customers, ~4M events, 2K products, and regional inventory\n- **Customer 360 gold layer** using Dynamic Tables for automatic refresh\n- **Feature Store** for offline training data retrieval\n- **Two XGBoost models**: batch propensity (daily, warehouse) and real-time ranking (SPCS)\n- **Snowflake Postgres** for low-latency operational serving (~15ms lookups vs ~575ms from warehouse)\n- **SPCS orchestrator** that fetches data from Postgres in parallel, calls the ranking model, and returns recommendations in ~78ms end-to-end (91% faster than the warehouse-only baseline of ~858ms)\n\n### What You Learned\n\n- How to use Snowflake UDTFs to generate realistic synthetic retail data\n- How to build a Customer 360 with Dynamic Tables\n- How to use the Feature Store for training data management\n- How to train and register models in the Snowflake Model Registry\n- How to deploy ML models and custom services on Snowpark Container Services\n- How to use Snowflake Postgres for low-latency operational serving alongside SPCS\n- The hybrid batch + real-time pattern for production ML systems\n\n### Next Steps\n\n- **Automated Postgres Sync**: Use Snowflake Tasks to periodically refresh Postgres tables from the Feature Store and batch propensity outputs\n- **CatBoost Delivery Estimation**: Replace the deterministic inventory filter with a CatBoost model trained on real logistics data (carrier performance, distance, demand signals) for accurate delivery date predictions\n- **Identity Resolution**: Add a Cortex Search-based identity resolution layer to unify customer profiles across channels\n- **Agentic Commerce**: Build a Cortex Agent on top of the recommendation API to enable conversational product discovery\n- **Activation**: Connect recommendations to GrowthLoop or HighTouch for audience activation and campaign orchestration\n- **A/B Testing**: Implement model version aliasing to A/B test different ranking strategies\n\n### Resources\n\n- [Snowflake Feature Store Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-ml/feature-store/overview)\n- [Snowflake Model Registry Documentation](https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview)\n- [Snowpark Container Services Documentation](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview)\n- [Snowflake Postgres Documentation](https://docs.snowflake.com/en/user-guide/postgres/overview)\n- [Programmatic Access Tokens](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens)\n- [Companion Code on GitHub](https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/retail-realtime-recommendations)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-2432be81cb","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-6bf45f1f00",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-a137d5521c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-e8937a5338","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-3ad4ff4c3e",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-a6a211d4c1",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-93dfc885fa","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-aedde72d3b","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/retail-realtime-recommendations",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EArchitecture Overview\u003C/h2\u003E","\u003Ch2\u003ESetting Up Infrastructure\u003C/h2\u003E","\u003Ch2\u003ECreating Reference Tables\u003C/h2\u003E","\u003Ch2\u003ECreating the Product Catalog\u003C/h2\u003E","\u003Ch2\u003ECreating the Inventory Table\u003C/h2\u003E","\u003Ch2\u003EGenerating Synthetic Customers\u003C/h2\u003E","\u003Ch2\u003EGenerating Synthetic Events\u003C/h2\u003E","\u003Ch2\u003EBuilding the Customer 360 Gold Layer\u003C/h2\u003E","\u003Ch2\u003EFeature Store, Model Training, and Registration\u003C/h2\u003E","\u003Ch2\u003ERunning Batch Propensity Scoring\u003C/h2\u003E","\u003Ch2\u003ESetting Up Snowflake Postgres for Low-Latency Serving\u003C/h2\u003E","\u003Ch2\u003ELoading Data into Postgres\u003C/h2\u003E","\u003Ch2\u003EDeploying the Ranking Model Service\u003C/h2\u003E","\u003Ch2\u003EBuilding and Deploying the Orchestrator\u003C/h2\u003E","\u003Ch2\u003ETesting the Recommendations\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-492208e2a8","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/retail-realtime-recommendations",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"],":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container"}},":itemsOrder":["quickstart_table_of_"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"markup_editor":{"id":"markup-editor-cb887d0105","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E 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Entertainment\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/financial-services/\"\u003EFinancial Services\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/healthcare-and-life-sciences/\"\u003EHealthcare & Life Sciences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/manufacturing/\"\u003EManufacturing\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/public-sector/\"\u003EPublic Sector\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/retail-consumer-goods/\"\u003ERetail & Consumer Goods\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/telecom/\"\u003ETelecom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca 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