bridge

Bridge Data Product

Bridge gives brands and distributors direct visibility into their sell-through performance at retail. By connecting point-of-sale data from retail partners, Bridge provides a real-time view of how your products move through stores — from receiving through to final sale.

What's Included

Bridge delivers six secure views:

View	Description
STORES	Store locations, addresses, and metadata for every retail partner carrying your products
PRODUCTS	Your product catalog as seen at retail — names, brands, categories, strains, potency, and current stock levels
SALES	Line-item transaction data including quantities, pricing, discounts, taxes, revenue, and customer demographics
INVENTORY_HISTORY	Daily snapshots of stock levels and average cost/price per unit for each product at each store
INVENTORY_BATCHES	Batch-level inventory detail including vendor, receive dates, quantities, cost, test results, and METRC/package data
PRODUCT_LATEST_PACKAGE	The most recent METRC/package ID associated with each product at each store

Data Relationships

The views connect through shared keys, primarily STORE_ID and PRODUCT_ID:

erDiagram
    STORES ||--o{ PRODUCTS : "STORE_ID"
    STORES ||--o{ SALES : "STORE_ID"
    STORES ||--o{ INVENTORY_HISTORY : "STORE_ID"
    STORES ||--o{ INVENTORY_BATCHES : "STORE_ID"
    STORES ||--o{ PRODUCT_LATEST_PACKAGE : "STORE_ID"
    PRODUCTS ||--o{ SALES : "PRODUCT_ID"
    PRODUCTS ||--o{ INVENTORY_HISTORY : "PRODUCT_ID"
    PRODUCTS ||--o{ PRODUCT_LATEST_PACKAGE : "PRODUCT_ID"
    PRODUCTS ||--o{ INVENTORY_BATCHES : "POS_PRODUCT_ID"

    STORES {
        NUMBER STORE_ID PK
        TEXT STORE_NAME
        TEXT ADDRESS
        TEXT CITY
        TEXT STATE
    }
    PRODUCTS {
        TEXT PRODUCT_ID PK
        NUMBER STORE_ID FK
        TEXT POS_PRODUCT_ID
        TEXT PRODUCT_NAME
        TEXT BRAND
        TEXT CATEGORY
    }
    SALES {
        NUMBER RECEIPT_ITEM_ID PK
        NUMBER STORE_ID FK
        TEXT PRODUCT_ID FK
        TIMESTAMP_NTZ SOLD_DATE
        FLOAT QUANTITY
        NUMBER REVENUE
    }
    INVENTORY_HISTORY {
        NUMBER STORE_ID FK
        TEXT PRODUCT_ID FK
        DATE STOCK_DATE
        NUMBER CURRENT_QUANTITY_IN_STOCK
        FLOAT AVG_PRICE_PER_UNIT
    }
    INVENTORY_BATCHES {
        NUMBER INVENTORY_EVENT_ID PK
        NUMBER STORE_ID FK
        TEXT POS_PRODUCT_ID FK
        TEXT BATCH_ID
        NUMBER QUANTITY
        NUMBER QUANTITY_IN_STOCK
    }
    PRODUCT_LATEST_PACKAGE {
        NUMBER STORE_ID FK
        TEXT PRODUCT_ID FK
        TEXT LATEST_PACKAGE_ID
    }

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Common Use Cases

• Sell-through reporting — Track unit and dollar sales of your products across retail partners over time
• Inventory monitoring — See current stock levels and receive alerts when products are running low at specific stores
• Batch and package tracking — Trace METRC package IDs and batch-level test results through the supply chain
• Pricing analysis — Compare base price, discounts, and revenue across stores and products
• Distribution coverage — Understand which stores carry your products and identify expansion opportunities

Example Queries

Ready-to-run SQL organized by topic:

• Sales — Revenue by store, top products, weekly trends, YoY comparisons, discount impact, demographics
• Inventory — Current stock, low-stock alerts, days of supply, sell-through rate, inventory value
• Batches and Compliance — METRC package tracking, test results, batch traceability, expiration monitoring
• Products and Stores — Distribution coverage, new launches, stale products, store performance

Further Reading

• Schema Reference — Full column definitions and data types for every view
• Snowflake Basics — Connection setup and authentication
• Glossary — Definitions of key terms