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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EData practitioners often face a frustrating reality: to answer even simple business questions, they must write complex SQL queries that span multiple tables, require intricate \u003Ccode\u003EJOIN\u003C/code\u003E logic, and repeat the same aggregation formulas across different reports. A question like \u003Cem\u003E"What were our sales by store manager last year?"\u003C/em\u003E might require 20+ lines of SQL, knowledge of the exact table schema, and the correct \u003Ccode\u003EJOIN\u003C/code\u003E conditions. This complexity creates barriers: business analysts depend on data engineers for every query, metrics become inconsistent across teams, and onboarding new team members takes weeks instead of days.\u003C/p\u003E\n","\u003Cp\u003ESnowflake Semantic Views solve this by creating a business-friendly abstraction layer over complex database schemas. You define relationships between tables once, create reusable metrics with consistent definitions, and enable anyone to write simpler, more intuitive queries. The same 20 line query becomes 5 lines of Semantic SQL that reads like a business question.\u003C/p\u003E\n","\u003Cp\u003EIn this tutorial, you'll learn how to transform complex SQL queries into simple, business-friendly Semantic SQL using Snowflake's Semantic Views. We'll use the industry-standard TPC-DS benchmark dataset to demonstrate how Semantic Views can dramatically simplify your data analytics workflow.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create Semantic Views in Snowflake\u003C/li\u003E\u003Cli\u003EThe difference between Traditional SQL and Semantic SQL\u003C/li\u003E\u003Cli\u003EHow to define table relationships, dimensions, facts, and metrics\u003C/li\u003E\u003Cli\u003EQuery patterns for varying complexity levels\u003C/li\u003E\u003Cli\u003EBest practices for building business-ready analytics\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA comprehensive Semantic View over the TPC-DS dataset that enables simplified querying across multiple sales channels (store, web, catalog), customer demographics, and inventory data.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EAccess to \u003Ccode\u003ESNOWFLAKE_SAMPLE_DATA\u003C/code\u003E database\u003C/li\u003E\u003Cli\u003EAccess to \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role (required for creating semantic views)\u003C/li\u003E\u003Cli\u003EBasic SQL knowledge\u003C/li\u003E\u003Cli\u003EFamiliarity with data modeling concepts\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAbout the TPC-DS Dataset\u003C/h2\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ETPC-DS (Transaction Processing Performance Council - Decision Support)\u003C/strong\u003E benchmark is the industry-standard dataset for modeling complex decision support systems. It simulates a global retail empire with multiple sales channels including:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStore sales\u003C/strong\u003E: Traditional brick-and-mortar retail transactions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWeb sales\u003C/strong\u003E: E-commerce transactions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECatalog sales\u003C/strong\u003E: Mail-order catalog purchases\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStore returns\u003C/strong\u003E: Product returns and exchanges\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe dataset includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDimension tables\u003C/strong\u003E: Store, Item, Customer, Date, Warehouse, Ship Mode, and more\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFact tables\u003C/strong\u003E: Store Sales, Web Sales, Catalog Sales, Inventory, and Returns\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EScale Factor\u003C/strong\u003E: We're using the SF10TCL scale (10TB scale factor) from Snowflake's sample data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ETraditional SQL vs Semantic SQL\u003C/h3\u003E\n","\u003Cp\u003EWe'll compare two approaches to querying the data:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETraditional SQL\u003C/strong\u003E: Requires explicit \u003Ccode\u003EJOIN\u003C/code\u003E clauses, fully-qualified table references, and manual aggregation logic\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic SQL\u003C/strong\u003E: Uses the semantic view to abstract complexity, making queries shorter and more business-focused\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EQuery Complexity Framework\u003C/h3\u003E\n","\u003Cp\u003ETo systematically explore how Semantic Views simplify queries, we organize examples by complexity level:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComplexity Level\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Means\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESimple Questions\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESimple filters on 1-2 tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBasic \u003Ccode\u003ESELECT\u003C/code\u003E with \u003Ccode\u003EWHERE\u003C/code\u003E clauses\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESimple Questions with Aggregations\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAggregations and metrics on 1-2 tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EGROUP BY\u003C/code\u003E, \u003Ccode\u003ESUM()\u003C/code\u003E, \u003Ccode\u003ECOUNT()\u003C/code\u003E on simple schemas\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAdvanced Questions\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESimple filters across 3+ tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEasy question, but complex table relationships\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAdvanced Questions with Aggregations\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAggregations across 3+ tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EComplex calculations across many tables\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Insight:\u003C/strong\u003E Semantic Views provide progressively more value as query complexity increases. Simple queries remain simple, but complex multi-table aggregations see dramatic improvements, reducing from 25+ lines of Traditional SQL to 10-12 lines of Semantic SQL.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Ch3\u003ENotebook\u003C/h3\u003E\n","\u003Cp\u003EYou can follow along this quickstart using the \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Snowflake_Semantic_View_Business_Ready_Queries/build-business-ready-queries-with-snowflake-semantic-views.ipynb\"\u003Ebuild-business-ready-queries-with-snowflake-semantic-views.ipynb\u003C/a\u003E notebook file.\u003C/p\u003E\n","\u003Ch3\u003ECreate Semantic Views\u003C/h3\u003E\n","\u003Cp\u003ESemantic Views are created using the \u003Ccode\u003ECREATE SEMANTIC VIEW\u003C/code\u003E statement, which defines five key components:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComponent\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETABLES\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERegister source tables with their primary keys\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Estore AS SNOWFLAKE_SAMPLE_DATA...store PRIMARY KEY (s_store_sk)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ERELATIONSHIPS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefine foreign key relationships between tables\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Esales_to_store AS store_sales (ss_store_sk) REFERENCES store\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFACTS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefine row-level expressions and computed columns (non-aggregated)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ef_net_profit_tier AS CASE WHEN ss_net_profit > 25000 THEN...\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDIMENSIONS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefine categorical attributes for grouping and filtering\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Estore.s_city AS store.s_city comment='City where store is located'\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMETRICS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDefine reusable aggregate expressions (\u003Ccode\u003ESUM\u003C/code\u003E, \u003Ccode\u003ECOUNT\u003C/code\u003E, \u003Ccode\u003EAVG\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Etotal_sales AS SUM(ss_sales_price * ss_quantity)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFor more details, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/sql\"\u003ESnowflake Semantic Views documentation\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ERun the following SQL to create the Semantic View for the TPC-DS dataset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE SNOWFLAKE_LEARNING_DB;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE SEMANTIC VIEW tpcds_nlq_view\n  TABLES (\n    store AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.store PRIMARY KEY (s_store_sk),\n    store_sales AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.store_sales PRIMARY KEY (ss_item_sk, ss_ticket_number),\n    web_sales AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.web_sales PRIMARY KEY (ws_item_sk, ws_order_number),\n    catalog_sales AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.catalog_sales PRIMARY KEY (cs_item_sk, cs_order_number),\n    store_returns AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.store_returns PRIMARY KEY (sr_item_sk, sr_ticket_number),\n    item AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.item PRIMARY KEY (i_item_sk),\n    returned_item AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.item PRIMARY KEY (i_item_sk) COMMENT = 'Dimension for returned items',\n    customer AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer PRIMARY KEY (c_customer_sk),\n    customer_address AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer_address PRIMARY KEY (ca_address_sk),\n    current_customer_demographics AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer_demographics PRIMARY KEY (cd_demo_sk) COMMENT = 'Dimension for Current customer demographics',\n    customer_demographics AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer_demographics PRIMARY KEY (cd_demo_sk) COMMENT = 'Dimension for Customer demographics at the time of sale',\n    date_dim AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.date_dim PRIMARY KEY (d_date_sk),\n    hd AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.household_demographics PRIMARY KEY (hd_demo_sk),\n    income_band AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.income_band PRIMARY KEY (ib_income_band_sk),\n    web_site AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.web_site PRIMARY KEY (web_site_sk),\n    inventory AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.inventory PRIMARY KEY (inv_date_sk, inv_item_sk, inv_warehouse_sk),\n    ship_mode AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.ship_mode PRIMARY KEY (sm_ship_mode_sk),\n    warehouse AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.warehouse PRIMARY KEY (w_warehouse_sk)\n  )\n  RELATIONSHIPS (\n    sales_to_store AS store_sales (ss_store_sk) REFERENCES store,\n    sales_to_customer AS store_sales (ss_customer_sk) REFERENCES customer,\n    sales_to_date AS store_sales (ss_sold_date_sk) REFERENCES date_dim,\n    sales_to_customer_demo AS store_sales (ss_cdemo_sk) REFERENCES customer_demographics,\n    sales_to_item AS store_sales (ss_item_sk) REFERENCES item,\n    web_sales_to_bill_customer AS web_sales (ws_bill_customer_sk) REFERENCES customer,\n    web_sales_to_sold_date AS web_sales (ws_sold_date_sk) REFERENCES date_dim,\n    web_sales_to_bill_customer_demo AS web_sales (ws_bill_cdemo_sk) REFERENCES customer_demographics,\n    web_sales_to_item AS web_sales (ws_item_sk) REFERENCES item (i_item_sk),\n    web_sales_to_web_site AS web_sales (ws_web_site_sk) REFERENCES web_site,\n    catalog_sales_to_bill_customer AS catalog_sales (cs_bill_customer_sk) REFERENCES customer,\n    catalog_sales_to_sold_date AS catalog_sales (cs_sold_date_sk) REFERENCES date_dim,\n    catalog_sales_to_bill_customer_demo AS catalog_sales (cs_bill_cdemo_sk) REFERENCES customer_demographics,\n    catalog_sales_to_item AS catalog_sales (cs_item_sk) REFERENCES item,\n    sales_returns_to_item AS store_returns (sr_item_sk) REFERENCES returned_item (i_item_sk),\n    sales_returns_to_sales AS store_returns (sr_ticket_number, sr_item_sk, sr_customer_sk) REFERENCES store_sales (ss_ticket_number, ss_item_sk, ss_customer_sk),\n    customer_to_customer_address AS customer (c_current_addr_sk) REFERENCES customer_address (ca_address_sk),\n    customer_to_household_demo AS customer (c_current_hdemo_sk) REFERENCES hd,\n    customer_to_customer_demo AS customer (c_current_cdemo_sk) REFERENCES current_customer_demographics (cd_demo_sk),\n    household_demo_to_income_band AS hd (hd_income_band_sk) REFERENCES income_band,\n    inventory_to_item AS inventory (inv_item_sk) REFERENCES item,\n    inventory_to_date AS inventory (inv_date_sk) REFERENCES date_dim,\n    catalog_sales_to_ship_mode AS catalog_sales (cs_ship_mode_sk) REFERENCES ship_mode,\n    web_sales_to_ship_mode AS web_sales (ws_ship_mode_sk) REFERENCES ship_mode\n  )\n  FACTS (\n    store_sales.f_ss_item_sk AS ss_item_sk\n      COMMENT = 'Item SKU (Stock Keeping Unit) for each sale',\n    store_sales.f_net_profit_tier AS CASE\n      WHEN store_sales.ss_net_profit > 25000 THEN 'More than 25000'\n      WHEN store_sales.ss_net_profit BETWEEN 3000 AND 25000 THEN '3000-25000'\n      WHEN store_sales.ss_net_profit BETWEEN 2000 AND 3000 THEN '2000-3000'\n      WHEN store_sales.ss_net_profit BETWEEN 300 AND 2000 THEN '300-2000'\n      WHEN store_sales.ss_net_profit BETWEEN 250 AND 300 THEN '250-300'\n      WHEN store_sales.ss_net_profit BETWEEN 200 AND 250 THEN '200-250'\n      WHEN store_sales.ss_net_profit BETWEEN 150 AND 200 THEN '150-200'\n      WHEN store_sales.ss_net_profit BETWEEN 100 AND 150 THEN '100-150'\n      WHEN store_sales.ss_net_profit BETWEEN 50 AND 100 THEN ' 50-100'\n      WHEN store_sales.ss_net_profit BETWEEN 0 AND 50 THEN '  0- 50'\n      ELSE ' 50 or Less'\n    END\n      COMMENT = 'Tier labels for net profit from store sales',\n    date_dim.f_year AS date_dim.d_year\n      COMMENT = 'Year of Date',\n    store_returns.f_ss_has_sales AS IFF(store_sales.f_ss_item_sk IS NOT NULL, TRUE, FALSE)\n      COMMENT = 'Boolean indicating whether valid store sales item was returned'\n  )\n  DIMENSIONS (\n    store.s_store_sk AS store.s_store_sk\n      COMMENT = 'Store SKU (Stock Keeping Unit)',\n    store.s_city AS store.s_city\n      COMMENT = 'City where the store is located',\n    date_dim.d_year AS date_dim.d_year\n      COMMENT = 'Year of Date',\n    date_dim.d_date AS date_dim.d_date\n      COMMENT = 'Date of the day',\n    customer.c_first_name AS customer.c_first_name\n      COMMENT = 'First name of the customer',\n    customer.c_last_name AS customer.c_last_name\n      COMMENT = 'Last name of the customer',\n    current_customer_demographics.cd_dep_count AS current_customer_demographics.cd_dep_count\n      COMMENT = 'Current number of dependents for the customer',\n    customer_demographics.cd_dep_count AS customer_demographics.cd_dep_count\n      COMMENT = 'Number of dependents for the customer at the time of sale',\n    store.s_store_name AS store.s_store_name\n      COMMENT = 'the names of stores, likely a list of store names in a retail or commercial setting',\n    store_sales.ss_sale_year AS date_dim.d_year\n      COMMENT = 'Year of store sale',\n    store.s_manager AS store.s_manager\n      COMMENT = 'Store manager',\n    store.s_floor_space AS store.s_floor_space\n      COMMENT = 'Total floor space in square feet',\n    store.s_store_id AS store.s_store_id\n      COMMENT = 'Unique identifier for each store',\n    item.i_brand AS item.i_brand\n      COMMENT = 'Brands of export items',\n    item.i_product_name AS item.i_product_name\n      COMMENT = 'Product names',\n    item.i_manufact AS item.i_manufact\n      COMMENT = 'Manufacturing items, including antibarable, n stbarpri, and barationese',\n    customer_address.ca_state AS ca_state\n      COMMENT = 'State where the customer address is located',\n    item.i_item_id AS item.i_item_id\n      COMMENT = 'Unique item identifiers',\n    item.i_item_sk AS i_item_sk\n      COMMENT = 'Item identifier SKU (Stock Keeping Unit)',\n    customer.c_state AS customer_address.ca_state\n      COMMENT = 'Customer state abbreviation',\n    hd.hd_vehicle_count AS hd.hd_vehicle_count\n      COMMENT = 'Number of vehicles owned by the household',\n    customer.c_customer_id AS customer.c_customer_id\n      COMMENT = 'Customer identifier',\n    income_band.ib_income_band_sk AS income_band.ib_income_band_sk\n      COMMENT = 'Income Band Identifier',\n    customer_demographics.gender AS customer_demographics.cd_gender\n      COMMENT = 'Gender of the customer at the time of sale',\n    current_customer_demographics.gender AS current_customer_demographics.cd_gender\n      COMMENT = 'Gender of the customer',\n    store_sales.ss_net_profit_tier AS f_net_profit_tier\n      COMMENT = 'Tier labels for net profit from store sales',\n    store_sales.ss_customer_sk AS store_sales.ss_customer_sk\n      COMMENT = 'Customer ID',\n    store_sales.ss_store_sk AS store_sales.ss_store_sk\n      COMMENT = 'Store''s SKU (Stock Keeping Unit) where sales happened',\n    income_band.ib_lower_bound AS income_band.ib_lower_bound\n      COMMENT = 'Lower bound of income bands',\n    income_band.ib_upper_bound AS income_band.ib_upper_bound\n      COMMENT = 'Upper bound of income bands',\n    hd.hd_buy_potential AS hd.hd_buy_potential\n      COMMENT = 'Household buying potential',\n    customer_address.ca_city AS ca_city\n      COMMENT = 'City where the customer address is located',\n    customer.c_city AS customer_address.ca_city\n      COMMENT = 'City where the customer is located',\n    item.i_item_size AS i_size\n      COMMENT = 'Item size',\n    store_sales.ss_item_id AS item.i_item_sk\n      COMMENT = 'Identifier of item that was sold through store',\n    store_sales.ss_product_name AS item.i_product_name\n      COMMENT = 'Product name of item that was sold through store',\n    store_sales.ss_item_size AS item.i_item_size\n      COMMENT = 'Size of item that was sold through store',\n    web_sales.ws_item_id AS item.i_item_sk\n      COMMENT = 'Identifier of item that was sold through web',\n    web_sales.ws_product_name AS item.i_product_name\n      COMMENT = 'Product name of item that was sold through web',\n    web_sales.ws_item_size AS item.i_item_size\n      COMMENT = 'Size of item that was sold through web',\n    catalog_sales.cs_item_id AS item.i_item_sk\n      COMMENT = 'Identifier of item that was sold through catalog',\n    catalog_sales.cs_product_name AS item.i_product_name\n      COMMENT = 'Product name of item that was sold through catalog',\n    catalog_sales.cs_item_size AS item.i_item_size\n      COMMENT = 'Size of item that was sold through catalog',\n    customer.c_customer_sk AS c_customer_sk\n      COMMENT = 'Customer unique identifier',\n    web_site.web_site_sk AS web_site.web_site_sk\n      COMMENT = 'Unique identifier for each web site',\n    web_site.web_name AS web_site.web_name\n      COMMENT = 'Web site name',\n    item.i_brand_id AS i_brand_id\n      COMMENT = 'Brand ID for items',\n    item.i_category AS item.i_category\n      COMMENT = 'Product categories',\n    item.i_color AS i_color\n      COMMENT = 'Color options',\n    store.s_hours AS s_hours\n      COMMENT = 'Store hours',\n    store.s_state AS s_state\n      COMMENT = 'Store state',\n    customer_address.ca_zip AS ca_zip\n      COMMENT = 'Customer zip code',\n    customer.ca_zip AS customer_address.ca_zip\n      COMMENT = 'Customer zip code',\n    customer.ca_state AS customer_address.ca_state\n      COMMENT = 'State where the customer address is located',\n    ship_mode.sm_type AS sm_type\n      COMMENT = 'Shipping mode type',\n    ship_mode.sm_carrier AS sm_carrier\n      COMMENT = 'Shipping mode carrier',\n    warehouse.w_warehouse_name AS w_warehouse_name\n      COMMENT = 'Warehouse name',\n    warehouse.w_city AS w_city\n      COMMENT = 'Warehouse city',\n    warehouse.w_warehouse_sq_ft AS w_warehouse_sq_ft\n      COMMENT = 'Warehouse square footage',\n    item.i_current_price AS i_current_price\n      COMMENT = 'Current price of the item',\n    store.s_number_employees AS s_number_employees\n      COMMENT = 'Number of employees in the store',\n    customer.c_birth_country AS c_birth_country\n      COMMENT = 'Country where the customer was born',\n    catalog_sales.cs_ship_mode_sk AS catalog_sales.cs_ship_mode_sk\n      COMMENT = 'Unique Identifier for Shipping mode  for catalog sales',\n    web_sales.ws_ship_mode_sk AS web_sales.ws_ship_mode_sk\n      COMMENT = 'Unique Identifier for Shipping mode for web sales',\n    hd.hd_income_band_sk AS hd.hd_income_band_sk\n      COMMENT = 'Unique Identifier for Household income band ',\n    catalog_sales.cs_item_sk AS catalog_sales.cs_item_sk\n      COMMENT = 'Unique Identifier for catalog sales item',\n    store_sales.ss_item_sk AS store_sales.ss_item_sk\n      COMMENT = 'Unique Identifier for store sales item',\n    web_sales.ws_item_sk AS web_sales.ws_item_sk\n      COMMENT = 'Unique Identifier for web sales item'\n  )\n  METRICS (\n    customer.customer_count AS COUNT(DISTINCT c_customer_sk)\n      COMMENT = 'Count of distinct customer identifiers',\n    item.product_count AS (COUNT(DISTINCT i_item_sk))\n      COMMENT = 'Count of distinct products',\n    store_returns.ss_store_returns AS COUNT_IF(f_ss_has_sales)\n      COMMENT = 'Count of records that have a valid store sales item returned',\n    web_sales.total_sales AS SUM(CAST(ws_ext_sales_price * ws_quantity AS DECIMAL(38, 2)))\n      COMMENT = 'Sum of the revenue (sales price multiplied by quantity) from web sales',\n    store_sales.start_date AS MIN(date_dim.d_date)\n      COMMENT = 'Min date (start date) of the store sales',\n    store_sales.end_date AS MAX(date_dim.d_date)\n      COMMENT = 'Max date (end date) of the store sales',\n    web_sales.w_net_profit AS SUM(ws_net_profit)\n      COMMENT = 'Sum of net profit through web sales',\n    catalog_sales.c_net_profit AS SUM(cs_net_profit)\n      COMMENT = 'Sum of net profit through catalog sales',\n    store_sales.s_net_profit AS SUM(ss_net_profit)\n      COMMENT = 'Sum of net profit through store sales',\n    catalog_sales.total_sales AS SUM(CAST(cs_sales_price * cs_quantity AS DECIMAL(38, 2)))\n      COMMENT = 'Sum of revenue (sales price multiplied by quantity) from catalog sales',\n    store_sales.ss_customer_count AS COUNT(ss_customer_sk)\n      COMMENT = 'Count of customers who purchased through store sales',\n    store_sales.ss_average_sale_quantity AS CASE\n      WHEN COUNT(ss_quantity) = 0 THEN NULL\n      ELSE CAST((SUM(ss_quantity) / COUNT(ss_quantity)) AS DOUBLE)\n    END\n      COMMENT = 'Average store sale quantity calculated as sum of sold quantity divided by number of rows',\n    store_sales.total_sales AS SUM(ss_sales_price * ss_quantity)\n      COMMENT = 'Sum of the revenue (sales price multiplied by quantity) from store sales',\n    web_sales.total_quantity_sold AS COALESCE(SUM(ws_quantity), 0)\n      COMMENT = 'Sum of number of items sold through web sales',\n    web_sales.total_shipping_cost AS SUM(ws_ext_ship_cost)\n      COMMENT = 'Sum of the shipping cost',\n    store_sales.ss_average_store_net_profit AS CASE\n      WHEN (SUM(ss_quantity) = 0) THEN NULL\n      ELSE CAST(CAST(SUM(ss_net_profit) AS DECIMAL(17, 2)) / SUM(ss_quantity) AS DECIMAL(37, 22))\n    END\n      COMMENT = 'Average profit sold through store',\n    inventory.total_inventory_on_hand AS SUM(inv_quantity_on_hand)\n      COMMENT = 'Total inventory on hand for a given item',\n    store_sales.total_quantity_sold AS COALESCE(SUM(ss_quantity), 0)\n      COMMENT = 'Total quantity sold for a given item in a store',\n    store_returns.total_quantity_returned AS COALESCE(SUM(sr_return_quantity), 0)\n      COMMENT = 'Total quantity returned for a given item in a store',\n    catalog_sales.start_date AS MIN(date_dim.d_date)\n      COMMENT = 'Min date for catalog sales',\n    catalog_sales.end_date AS MAX(date_dim.d_date)\n      COMMENT = 'Max date for catalog sales',\n    catalog_sales.unique_catalog_customers AS COUNT(DISTINCT cs_bill_customer_sk)\n      COMMENT = 'Unique customers who made a purchase through catalog sales',\n    catalog_sales.total_quantity_sold AS COALESCE(SUM(cs_quantity), 0)\n      COMMENT = 'Sum of number of items sold through catalog sales'\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter running the above SQL, you should see the following output confirming the semantic view was created successfully:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+----------------------------------------------------+\n|                       status                       |\n+----------------------------------------------------+\n| Semantic view TPCDS_NLQ_VIEW successfully created. |\n+----------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETraditional vs Semantic SQL\u003C/h2\u003E\n","\u003Cp\u003ENow that we've created our semantic view, let's compare how traditional SQL and semantic SQL handle queries of varying complexity.\u003C/p\u003E\n","\u003Cp\u003EWe'll use the TPC-DS benchmark to demonstrate queries ranging from simple filters to complex multi-table aggregations. Each example will show:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ETraditional SQL\u003C/strong\u003E: The standard approach with explicit \u003Ccode\u003EJOIN\u003C/code\u003Es and aggregations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESemantic SQL\u003C/strong\u003E: The simplified approach using our semantic view\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESimple Questions\u003C/h2\u003E\n","\u003Cp\u003EThese queries demonstrate simple filtering and selection operations on single tables or simple joins. They represent straightforward business questions that can be answered with basic SQL operations like \u003Ccode\u003EWHERE\u003C/code\u003E clauses and simple aggregations.\u003C/p\u003E\n","\u003Ch3\u003EWhat are all of the unique store numbers in Tennessee?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Queries the store table directly with a \u003Ccode\u003EWHERE\u003C/code\u003E filter on state.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    DISTINCT s_store_sk\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store\nWHERE\n    s_state = 'TN'\n    AND s_store_sk IS NOT NULL\nORDER BY s_store_sk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EDIMENSIONS\u003C/code\u003E to select the store identifier directly. No need to specify the full table path, the semantic view knows where \u003Ccode\u003Es_store_sk\u003C/code\u003E lives.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    DIMENSIONS store.s_store_sk\n    WHERE s_state='TN'\n)\nORDER BY s_store_sk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns unique store IDs for Tennessee stores, ordered by store number.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+------------+\n| S_STORE_SK |\n+------------+\n|          1 |\n|         22 |\n|         52 |\n|         76 |\n|         96 |\n|        102 |\n|        112 |\n|        140 |\n|        ... |\n+------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat are the first and last names of all customers that have more than 5 dependents?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EJOIN\u003C/code\u003E on customer and customer_demographics tables with a \u003Ccode\u003EWHERE\u003C/code\u003E filter on dependent count.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    customer.c_first_name,\n    customer.c_last_name\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.customer\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_demographics AS current_customer_demographics\n    ON customer.c_current_cdemo_sk = current_customer_demographics.cd_demo_sk\nWHERE\n    current_customer_demographics.cd_dep_count > 5\nORDER BY\n    c_first_name ASC, c_last_name ASC\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EFACTS\u003C/code\u003E to retrieve customer attributes. The semantic view handles the \u003Ccode\u003EJOIN\u003C/code\u003E between \u003Ccode\u003Ecustomer\u003C/code\u003E and \u003Ccode\u003Ecustomer_demographics\u003C/code\u003E automatically, so no explicit \u003Ccode\u003EJOIN\u003C/code\u003E is needed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    FACTS customer.c_first_name first_name, customer.c_last_name last_name\n    WHERE current_customer_demographics.cd_dep_count > 5\n)\nORDER BY first_name ASC, last_name ASC\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns customer names sorted alphabetically, filtered to those with more than 5 dependents.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+------------+------------+\n| FIRST_NAME | LAST_NAME  |\n+------------+------------+\n| Aaron      | Aaron      |\n| Aaron      | Aaron      |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abel       |\n| Aaron      | Abel       |\n| Aaron      | Abel       |\n| ...        | ...        |\n+------------+------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat is the name, manager and floor space of each store in the city of Midway?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003ESELECT\u003C/code\u003E on multiple columns from the store table with a \u003Ccode\u003EWHERE\u003C/code\u003E city filter.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    s_store_name,\n    s_manager,\n    s_floor_space\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store\nWHERE\n    s_city = 'Midway'\nORDER BY s_store_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EFACTS\u003C/code\u003E to select multiple store attributes in one call. Clean, readable syntax without needing to reference the full table path.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    FACTS s_store_name, s_manager, s_floor_space\n    WHERE s_city = 'Midway'\n)\nORDER BY s_store_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns store details for all stores located in Midway, sorted by store name.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+--------------+-------------------+--------------+\n| S_STORE_NAME | S_MANAGER         | S_FLOOR_SPACE|\n+--------------+-------------------+--------------+\n| ation        | Harry Harkins     |      6849804 |\n| bar          | Christopher Garris|      7610137 |\n| eing         | Stephen Garcia    |      6101180 |\n| eing         | Brian Strickland  |      6396268 |\n| eing         | Robert Tyson      |      5903931 |\n| ese          | Dean Patel        |      9875948 |\n| ought        | Robert Yost       |      5945154 |\n+--------------+-------------------+--------------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESimple Questions with Aggregations\u003C/h2\u003E\n","\u003Cp\u003EThese queries introduce aggregations, grouping, and metrics while maintaining relatively simple table relationships. The semantic view's pre-defined metrics significantly reduce query complexity.\u003C/p\u003E\n","\u003Ch3\u003EWhat is the total count of customers for each customer home state?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EJOIN\u003C/code\u003E on customer and address tables, then \u003Ccode\u003EGROUP BY\u003C/code\u003E state with \u003Ccode\u003ECOUNT(DISTINCT ...)\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    ca_state,\n    COUNT(DISTINCT c_customer_sk) AS customer_count\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.customer\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_address\n    ON customer.c_current_addr_sk = customer_address.ca_address_sk\nGROUP BY\n    ca_state\nORDER BY\n    customer_count DESC, ca_state\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EDIMENSIONS\u003C/code\u003E for grouping and \u003Ccode\u003EMETRICS\u003C/code\u003E for the pre-defined \u003Ccode\u003Ecustomer_count\u003C/code\u003E aggregation. The \u003Ccode\u003EJOIN\u003C/code\u003E between \u003Ccode\u003Ecustomer\u003C/code\u003E and \u003Ccode\u003Ecustomer_address\u003C/code\u003E is handled automatically by the semantic view's relationships.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    DIMENSIONS customer_address.ca_state AS ca_state\n    METRICS customer.customer_count\n)\nORDER BY customer_count DESC\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns customer counts by state, with Texas (TX) having the most customers at over 5 million.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+----------+----------------+\n| CA_STATE | CUSTOMER_COUNT |\n+----------+----------------+\n| TX       |        5154196 |\n| GA       |        3225083 |\n| VA       |        2735265 |\n| KY       |        2431364 |\n| MO       |        2213416 |\n| KS       |        2131894 |\n| IL       |        2048115 |\n| NC       |        2034567 |\n| ...      |            ... |\n+----------+----------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat was the overall web sales for the year 2002?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EJOIN\u003C/code\u003E on web_sales with date_dim and calculates total revenue using \u003Ccode\u003ESUM()\u003C/code\u003E with \u003Ccode\u003ECAST()\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    SUM(CAST(ws_ext_sales_price * ws_quantity AS DECIMAL(38, 2))) AS total_sales\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.web_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON web_sales.ws_sold_date_sk = date_dim.d_date_sk\nWHERE\n    date_dim.d_year = 2002;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EMETRICS\u003C/code\u003E to call the pre-defined \u003Ccode\u003Etotal_sales\u003C/code\u003E calculation. No need to write the complex \u003Ccode\u003ESUM(CAST(...))\u003C/code\u003E formula since it's already defined in the semantic view. The \u003Ccode\u003EJOIN\u003C/code\u003E to \u003Ccode\u003Edate_dim\u003C/code\u003E is automatic.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    METRICS web_sales.total_sales\n    WHERE date_dim.d_year = 2002\n)\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns total web sales for 2002: approximately $2.46 quadrillion (TPC-DS scale factor).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+----------------------+\n|     TOTAL_SALES      |\n+----------------------+\n| 2458971149461555.79  |\n+----------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat is the count of products in each product category?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EGROUP BY\u003C/code\u003E category and \u003Ccode\u003ECOUNT(DISTINCT ...)\u003C/code\u003E to count products per category.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    i_category AS product_category,\n    COUNT(DISTINCT i_item_sk) AS product_count\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.item\nWHERE\n    i_category IS NOT NULL\n    AND i_item_sk IS NOT NULL\nGROUP BY\n    i_category\nORDER BY\n    i_category\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Combines \u003Ccode\u003EDIMENSIONS\u003C/code\u003E for grouping by category and \u003Ccode\u003EMETRICS\u003C/code\u003E for the pre-defined \u003Ccode\u003Eproduct_count\u003C/code\u003E. The aggregation logic (\u003Ccode\u003ECOUNT(DISTINCT i_item_sk)\u003C/code\u003E) is encapsulated in the metric definition.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM semantic_view(tpcds_nlq_view\n    DIMENSIONS item.i_category\n    METRICS item.product_count\n)\nORDER BY i_category\nlimit 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns all 10 product categories with approximately 40,000 products each.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+------------------+---------------+\n| PRODUCT_CATEGORY | PRODUCT_COUNT |\n+------------------+---------------+\n| Books            |         39643 |\n| Children         |         40406 |\n| Electronics      |         40172 |\n| Home             |         40124 |\n| Jewelry          |         40114 |\n| Men              |         39892 |\n| Music            |         40342 |\n| Shoes            |         40158 |\n| Sports           |         40315 |\n| Women            |         39871 |\n+------------------+---------------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdvanced Questions\u003C/h2\u003E\n","\u003Cp\u003EThese queries involve simple filters but require joining multiple tables across complex relationships. The semantic view dramatically simplifies these queries by hiding the complex join logic.\u003C/p\u003E\n","\u003Ch3\u003EWhat is the customer id and vehicle count for every customer in income band 9?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses multiple \u003Ccode\u003EJOIN\u003C/code\u003E clauses across customer, household_demographics, and income_band tables with a \u003Ccode\u003EWHERE\u003C/code\u003E filter on income band.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT\n    customer.c_customer_id,\n    hd.hd_vehicle_count\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.customer\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.household_demographics hd\n    ON customer.c_current_hdemo_sk = hd.hd_demo_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.income_band\n    ON hd.hd_income_band_sk = income_band.ib_income_band_sk\nWHERE\n    income_band.ib_income_band_sk = 9\n    AND customer.c_customer_id IS NOT NULL\nORDER BY\n    customer.c_customer_id\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EFACTS\u003C/code\u003E to access attributes across 3 related tables (\u003Ccode\u003Ecustomer\u003C/code\u003E, \u003Ccode\u003Ehousehold_demographics\u003C/code\u003E, \u003Ccode\u003Eincome_band\u003C/code\u003E). The semantic view's pre-defined relationships handle all the \u003Ccode\u003EJOIN\u003C/code\u003Es automatically. What was 3 \u003Ccode\u003EJOIN\u003C/code\u003Es becomes a single semantic query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT c_customer_id, hd_vehicle_count FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        FACTS customer.c_customer_id, hd.hd_vehicle_count\n        WHERE ib_income_band_sk = 9 AND c_customer_id IS NOT NULL\n    )\n)\nORDER BY c_customer_id\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns customer IDs and their vehicle counts for income band 9, with vehicle counts ranging from 0-4.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+------------------+------------------+\n| C_CUSTOMER_ID    | HD_VEHICLE_COUNT |\n+------------------+------------------+\n| AAAAAAAAAAAALAA  |                2 |\n| AAAAAAAAAAAAMAA  |                0 |\n| AAAAAAAAAAAPCA   |                3 |\n| AAAAAAAAAABCBA   |                4 |\n| AAAAAAAAAABDBA   |                4 |\n| AAAAAAAAABJDA    |                2 |\n| AAAAAAAAAACJDA   |                3 |\n| AAAAAAAAAACKAA   |                4 |\n| AAAAAAAAAACKBA   |                0 |\n| AAAAAAAAAACMDA   |                1 |\n| ...              |              ... |\n+------------------+------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat is the net profit tier for each store name and gender in the 2002 sales year?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses 4 \u003Ccode\u003EJOIN\u003C/code\u003E clauses and a complex \u003Ccode\u003ECASE\u003C/code\u003E statement to categorize profit into tiers.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT\n    store.s_store_name,\n    customer_demographics.cd_gender,\n    CASE\n        WHEN store_sales.ss_net_profit > 25000 THEN 'More than 25000'\n        WHEN store_sales.ss_net_profit BETWEEN 3000 AND 25000 THEN '3000-25000'\n        WHEN store_sales.ss_net_profit BETWEEN 2000 AND 3000 THEN '2000-3000'\n        WHEN store_sales.ss_net_profit BETWEEN 300 AND 2000 THEN '300-2000'\n        WHEN store_sales.ss_net_profit BETWEEN 250 AND 300 THEN '250-300'\n        WHEN store_sales.ss_net_profit BETWEEN 200 AND 250 THEN '200-250'\n        WHEN store_sales.ss_net_profit BETWEEN 150 AND 200 THEN '150-200'\n        WHEN store_sales.ss_net_profit BETWEEN 100 AND 150 THEN '100-150'\n        WHEN store_sales.ss_net_profit BETWEEN 50 AND 100 THEN ' 50-100'\n        WHEN store_sales.ss_net_profit BETWEEN 0 AND 50 THEN '  0- 50'\n        ELSE ' 50 or Less'\n    END AS net_profit_tier\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_demographics\n    ON store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    date_dim.d_year = 2002\nORDER BY\n    s_store_name, cd_gender, net_profit_tier\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EFACTS\u003C/code\u003E with the pre-defined \u003Ccode\u003Ef_net_profit_tier\u003C/code\u003E calculation. The complex \u003Ccode\u003ECASE\u003C/code\u003E statement is encapsulated in the semantic view, so there's no need to rewrite the tiering logic. \u003Ccode\u003EJOIN\u003C/code\u003Es across 4 tables are automatic.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT s_store_name, gender, f_net_profit_tier FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        FACTS store_sales.f_net_profit_tier, \n              store.s_store_name, \n              customer_demographics.gender,\n              date_dim.d_year\n    )\n)\nWHERE d_year = 2002\n      AND NOT gender IS NULL\nORDER BY s_store_name, gender, f_net_profit_tier\nLIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns distinct store/gender/tier combinations, showing profit tiers from "$0-50" up to "$3000-25000".\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+--------------+-----------+-----------------+\n| S_STORE_NAME | CD_GENDER | NET_PROFIT_TIER |\n+--------------+-----------+-----------------+\n| able         | F         | 0- 50           |\n| able         | F         | 50 or Less      |\n| able         | F         | 50-100          |\n| able         | F         | 100-150         |\n| able         | F         | 150-200         |\n| able         | F         | 200-250         |\n| able         | F         | 2000-3000       |\n| able         | F         | 250-300         |\n| able         | F         | 300-2000        |\n| able         | F         | 3000-25000      |\n| ...          | ...       | ...             |\n+--------------+-----------+-----------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat was the first name and gender of each customer that shopped in the store named 'ese' in 2001?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses 5 \u003Ccode\u003EJOIN\u003C/code\u003E clauses to link store_sales to customer details with \u003Ccode\u003EWHERE\u003C/code\u003E filters on store name and year.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT\n    customer.c_first_name,\n    customer_demographics.cd_gender\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer\n    ON store_sales.ss_customer_sk = customer.c_customer_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_demographics\n    ON store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    store.s_store_name = 'ese'\n    AND date_dim.d_year = 2001\nORDER BY\n    c_first_name, cd_gender\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EFACTS\u003C/code\u003E to access attributes from 5 related tables (\u003Ccode\u003Estore_sales\u003C/code\u003E, \u003Ccode\u003Ecustomer\u003C/code\u003E, \u003Ccode\u003Ecustomer_demographics\u003C/code\u003E, \u003Ccode\u003Estore\u003C/code\u003E, \u003Ccode\u003Edate_dim\u003C/code\u003E). The semantic view handles all \u003Ccode\u003EJOIN\u003C/code\u003Es through pre-defined relationships. What was 4 explicit \u003Ccode\u003EJOIN\u003C/code\u003Es becomes implicit.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT DISTINCT c_first_name, gender FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        FACTS store_sales.ss_customer_sk,\n              customer.c_first_name,\n              customer_demographics.gender,\n              date_dim.d_year,\n              store.s_store_name\n    )\n)\nWHERE s_store_name = 'ese'\n    AND d_year = 2001\n    AND NOT gender IS NULL\nORDER BY c_first_name, gender\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns unique first name and gender combinations for customers who shopped at store 'ese' in 2001.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+--------------+-----------+\n| C_FIRST_NAME | CD_GENDER |\n+--------------+-----------+\n| Aaron        | F         |\n| Aaron        | M         |\n| Abbey        | F         |\n| Abbey        | M         |\n| Abbie        | F         |\n| Abbie        | M         |\n| Abby         | F         |\n| Abby         | M         |\n| Abdul        | F         |\n| Abdul        | M         |\n| ...          | ...       |\n+--------------+-----------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdvanced Questions with Aggregations\u003C/h2\u003E\n","\u003Cp\u003EThese queries represent the most challenging scenarios, combining complex aggregations with intricate multi-table joins. The semantic view provides the greatest value here by abstracting both the complex relationships and pre-computing metrics.\u003C/p\u003E\n","\u003Ch3\u003EFor each store state in the year 2002, what was the count of store customers and the average sales quantity?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EJOIN\u003C/code\u003E on store_sales with store and date_dim, then \u003Ccode\u003EGROUP BY\u003C/code\u003E with \u003Ccode\u003ECOUNT()\u003C/code\u003E and \u003Ccode\u003ECASE\u003C/code\u003E for null-safe average calculation.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    store.s_state,\n    COUNT(store_sales.ss_customer_sk) AS store_customer_count,\n    CASE \n        WHEN COUNT(store_sales.ss_quantity) = 0 THEN NULL \n        ELSE CAST((SUM(store_sales.ss_quantity) / COUNT(store_sales.ss_quantity)) AS DOUBLE) \n    END AS average_store_sales_quantity\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    date_dim.d_year = 2002\n    AND store.s_state IS NOT NULL\nGROUP BY\n    store.s_state\nORDER BY\n    store.s_state\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EDIMENSIONS\u003C/code\u003E for grouping and multiple \u003Ccode\u003EMETRICS\u003C/code\u003E (\u003Ccode\u003Ess_customer_count\u003C/code\u003E, \u003Ccode\u003Ess_average_sale_quantity\u003C/code\u003E). The complex \u003Ccode\u003ECASE\u003C/code\u003E statement for average calculation and all \u003Ccode\u003EJOIN\u003C/code\u003Es are encapsulated in the semantic view.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    DIMENSIONS store.s_state\n    METRICS\n        store_sales.ss_customer_count,\n        store_sales.ss_average_sale_quantity\n    WHERE date_dim.d_year = 2002\n) AS R(store_state, store_customer_count, average_store_sales_quantity)\nWHERE store_state IS NOT NULL\nORDER BY store_state\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns metrics per state for 2002. Georgia (GA) leads with over 500 million customers, average quantity ~50 units.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+---------+----------------------+------------------------------+\n| S_STATE | STORE_CUSTOMER_COUNT | AVERAGE_STORE_SALES_QUANTITY |\n+---------+----------------------+------------------------------+\n| AL      |            148448445 |                    50.508838 |\n| CA      |            176854713 |                    50.503677 |\n| CO      |             63727493 |                     50.49999 |\n| FL      |             49548871 |                     50.51083 |\n| GA      |            502313211 |                    50.504699 |\n| IA      |             99133835 |                    50.491451 |\n| IL      |             77971044 |                     50.50326 |\n| IN      |            198222807 |                    50.500988 |\n| KS      |            155749285 |                    50.494168 |\n| KY      |            141595491 |                    50.503138 |\n| ...     |                  ... |                          ... |\n+---------+----------------------+------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat were the store sales in 2002 for each store manager in the state of Tennessee?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EJOIN\u003C/code\u003E on store_sales with store and date_dim, \u003Ccode\u003EGROUP BY\u003C/code\u003E manager with \u003Ccode\u003ESUM()\u003C/code\u003E and \u003Ccode\u003EWHERE\u003C/code\u003E filters on state/year.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    store.s_manager,\n    SUM(store_sales.ss_sales_price * store_sales.ss_quantity) AS total_sales\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    store.s_state = 'TN'\n    AND date_dim.d_year = 2002\n    AND store.s_manager IS NOT NULL\nGROUP BY\n    store.s_manager\nORDER BY\n    total_sales DESC NULLS LAST\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Combines \u003Ccode\u003EDIMENSIONS\u003C/code\u003E for grouping by manager and \u003Ccode\u003EMETRICS\u003C/code\u003E for the pre-defined \u003Ccode\u003Etotal_sales\u003C/code\u003E calculation. The \u003Ccode\u003ESUM\u003C/code\u003E formula and all table \u003Ccode\u003EJOIN\u003C/code\u003Es are handled by the semantic view.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT s_manager, total_sales FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        DIMENSIONS store.s_manager\n        METRICS store_sales.total_sales\n        WHERE store.s_state = 'TN' AND d_year = 2002 \n              AND store.s_manager IS NOT NULL\n    )\n)\nORDER BY total_sales DESC NULLS LAST\nLIMIT 5000;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns sales per TN manager in 2002. Top performer Robert Young at ~$13.5 billion, all managers close in performance.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+------------------+----------------+\n| S_MANAGER        | TOTAL_SALES    |\n+------------------+----------------+\n| Robert Young     | 13540376902.13 |\n| Donald Dodson    | 13531825145.25 |\n| Jesus Dickinson  | 13520973098.82 |\n| Russell Pedigo   | 13506626925.90 |\n| Norman Gould     | 13503720972.50 |\n| Jesse Nielson    | 13493375137.32 |\n| Armando Vasquez  | 13489216257.07 |\n| John Fogle       | 13482056094.88 |\n| Daniel Slaton    | 13479913604.98 |\n| Frederick Bunn   | 13479143024.52 |\n| ...              |            ... |\n+------------------+----------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat were the web sales by site in New Jersey?\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuestion:\u003C/strong\u003E For each website, what is the quantity sold and total shipping cost for customers with a shipping address in the state of New Jersey?\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETraditional SQL:\u003C/strong\u003E Uses \u003Ccode\u003EJOIN\u003C/code\u003E on web_sales with web_site and customer_address, \u003Ccode\u003EGROUP BY\u003C/code\u003E site with \u003Ccode\u003ESUM()\u003C/code\u003E for NJ shipments.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  ws.ws_web_site_sk AS "web_site_sk",\n  web.web_name AS "web_name",\n  SUM(ws.ws_quantity) AS "total_quantity_sold",\n  SUM(ws.ws_ext_ship_cost) AS "total_shipping_cost"\nFROM\n  snowflake_sample_data.tpcds_sf10tcl.web_sales AS ws\n  JOIN snowflake_sample_data.tpcds_sf10tcl.web_site AS web ON ws.ws_web_site_sk = web.web_site_sk\n  JOIN snowflake_sample_data.tpcds_sf10tcl.customer_address AS ca ON ws.ws_ship_addr_sk = ca.ca_address_sk\nWHERE\n  ca.ca_state='NJ'\n  AND web.web_name IS NOT NULL\n  AND ws.ws_quantity IS NOT NULL\n  AND ws.ws_ext_ship_cost IS NOT NULL\nGROUP BY\n  ws.ws_web_site_sk,\n  web.web_name\nORDER BY\n  ws.ws_web_site_sk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic SQL:\u003C/strong\u003E Uses \u003Ccode\u003EDIMENSIONS\u003C/code\u003E for grouping by website and \u003Ccode\u003EMETRICS\u003C/code\u003E for pre-defined \u003Ccode\u003Etotal_quantity_sold\u003C/code\u003E and \u003Ccode\u003Etotal_shipping_cost\u003C/code\u003E. The \u003Ccode\u003EJOIN\u003C/code\u003Es between \u003Ccode\u003Eweb_sales\u003C/code\u003E, \u003Ccode\u003Eweb_site\u003C/code\u003E, and \u003Ccode\u003Ecustomer_address\u003C/code\u003E are handled automatically.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SEMANTIC_VIEW(\n    tpcds_nlq_view\n    DIMENSIONS web_site.web_site_sk, web_site.web_name\n    METRICS web_sales.total_quantity_sold, web_sales.total_shipping_cost\n    WHERE customer_address.ca_state='NJ'\n)\nWHERE web_name IS NOT NULL\n    AND total_quantity_sold IS NOT NULL\n    AND total_shipping_cost IS NOT NULL\nORDER BY web_site_sk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E Returns web sales metrics for NJ shipments. Site_0 (ID 1) leads with ~61M items sold and ~$1.5B in shipping.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E+-------------+----------+---------------------+---------------------+\n| WEB_SITE_SK | WEB_NAME | TOTAL_QUANTITY_SOLD | TOTAL_SHIPPING_COST |\n+-------------+----------+---------------------+---------------------+\n|           1 | site_0   |            61028703 |       1542231372.61 |\n|           2 | site_0   |            36583068 |        924147129.20 |\n|           3 | site_0   |            24539919 |        618876530.14 |\n|           4 | site_0   |            24398128 |        614892075.27 |\n|           5 | site_0   |            24353870 |        614586576.94 |\n|           6 | site_0   |            12364943 |        312510257.79 |\n|           7 | site_1   |            61102675 |       1538701306.86 |\n|           8 | site_1   |            36505051 |        923039800.32 |\n|           9 | site_1   |            24509946 |        619652299.83 |\n|          10 | site_1   |            24399101 |        616355076.94 |\n|         ... | ...      |                 ... |                 ... |\n+-------------+----------+---------------------+---------------------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELessons Learned\u003C/h2\u003E\n","\u003Cp\u003EThroughout this tutorial, we discovered key patterns for simplifying queries with Semantic Views:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESemantic Parameter\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat It Does\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhen to Use\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDIMENSIONS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESelects categorical attributes for grouping\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhen you need to \u003Ccode\u003EGROUP BY\u003C/code\u003E or select descriptive data\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMETRICS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECalls pre-defined aggregations (\u003Ccode\u003ESUM\u003C/code\u003E, \u003Ccode\u003ECOUNT\u003C/code\u003E, \u003Ccode\u003EAVG\u003C/code\u003E)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhen you need calculated measures without writing formulas\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFACTS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERetrieves row-level computed values\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhen you need detailed or derived data from the semantic model\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EWHERE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFilters data using any attribute\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESame as traditional SQL \u003Ccode\u003EWHERE\u003C/code\u003E clause\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EComplexity Comparison: Key Findings\u003C/h3\u003E\n","\u003Cp\u003EWe explored queries across four complexity levels:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EComplexity Level\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETraditional SQL\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESemantic SQL\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EImprovement\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESimple Questions\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E8-13 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6-7 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModest: simpler syntax\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESimple Questions with Aggregations\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9-13 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6-7 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EModerate: metrics eliminate formulas\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAdvanced Questions\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E17-23 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E9-15 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESignificant: auto \u003Ccode\u003EJOIN\u003C/code\u003Es\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAdvanced Questions with Aggregations\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E19-32 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10-13 lines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDramatic: biggest ROI\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBottom Line:\u003C/strong\u003E Semantic Views provide the greatest value for advanced questions with aggregations. These are exactly the queries that cause the most pain in traditional SQL development.\u003C/p\u003E\n","\u003Ch3\u003EKey Takeaways\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003EJOIN\u003C/code\u003Es become automatic.\u003C/strong\u003E Define relationships once in the semantic view, and Snowflake handles the joins. What was 4-5 explicit \u003Ccode\u003EJOIN\u003C/code\u003Es becomes implicit.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EMetrics are reusable.\u003C/strong\u003E Complex calculations (like \u003Ccode\u003ESUM(price * quantity)\u003C/code\u003E or \u003Ccode\u003ECASE\u003C/code\u003E statements) are defined once and called by name. No copy-pasting formulas.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBusiness-friendly naming.\u003C/strong\u003E Use semantic aliases that make sense to analysts (e.g., \u003Ccode\u003Etotal_sales\u003C/code\u003E instead of \u003Ccode\u003ESUM(ss_sales_price * ss_quantity)\u003C/code\u003E).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EQuery complexity scales.\u003C/strong\u003E Simple queries stay simple, but complex multi-table aggregations see the biggest improvement (from 20+ lines to 5-10 lines).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESame results, less code.\u003C/strong\u003E Semantic SQL produces identical results to traditional SQL with significantly less code to write and maintain.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built a comprehensive Semantic View over the TPC-DS dataset and learned how to transform complex SQL queries into simple, business-friendly Semantic SQL. You've seen how Semantic Views can dramatically reduce query complexity while maintaining full analytical power.\u003C/p\u003E\n","\u003Cp\u003EHappy querying!\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create Semantic Views with tables, relationships, dimensions, facts, and metrics\u003C/li\u003E\u003Cli\u003EThe difference between Traditional SQL and Semantic SQL approaches\u003C/li\u003E\u003Cli\u003EHow Semantic Views abstract complex join logic and pre-define reusable metrics\u003C/li\u003E\u003Cli\u003EQuery patterns across different complexity levels\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/overview\"\u003ESemantic Views Overview\u003C/a\u003E - Comprehensive overview of semantic views in Snowflake\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view\"\u003ECREATE SEMANTIC VIEW Reference\u003C/a\u003E - SQL command reference for creating semantic views\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/querying\"\u003EQuerying Semantic Views\u003C/a\u003E - How to query semantic views using SEMANTIC_VIEW construct\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-dev\"\u003EBest practices for semantic views\u003C/a\u003E - Best practices for working with semantic models\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/getting_started_with_cortex_analyst/\"\u003ECortex Analyst Getting Started\u003C/a\u003E - Step-by-step tutorial for building semantic models\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETPC-DS Resources:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://www.tpc.org/tpcds/\"\u003ETPC-DS Benchmark Specification\u003C/a\u003E - Official TPC-DS specification and documentation\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/NLQBenchmarks/TPCDS_Benchmark\"\u003ETPCDS NLQ Benchmark\u003C/a\u003E - Open benchmark for evaluating Text-to-SQL solutions with 40 questions\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/sample-data-tpcds\"\u003ESnowflake Sample Data: TPC-DS\u003C/a\u003E - Information about the TPC-DS sample data available in Snowflake\u003C/li\u003E\u003C/ul\u003E"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nData practitioners often face a frustrating reality: to answer even simple business questions, they must write complex SQL queries that span multiple tables, require intricate `JOIN` logic, and repeat the same aggregation formulas across different reports. A question like *\"What were our sales by store manager last year?\"* might require 20+ lines of SQL, knowledge of the exact table schema, and the correct `JOIN` conditions. This complexity creates barriers: business analysts depend on data engineers for every query, metrics become inconsistent across teams, and onboarding new team members takes weeks instead of days.\n\nSnowflake Semantic Views solve this by creating a business-friendly abstraction layer over complex database schemas. You define relationships between tables once, create reusable metrics with consistent definitions, and enable anyone to write simpler, more intuitive queries. The same 20 line query becomes 5 lines of Semantic SQL that reads like a business question.\n\nIn this tutorial, you'll learn how to transform complex SQL queries into simple, business-friendly Semantic SQL using Snowflake's Semantic Views. We'll use the industry-standard TPC-DS benchmark dataset to demonstrate how Semantic Views can dramatically simplify your data analytics workflow.\n\n### What You'll Learn\n- How to create Semantic Views in Snowflake\n- The difference between Traditional SQL and Semantic SQL\n- How to define table relationships, dimensions, facts, and metrics\n- Query patterns for varying complexity levels\n- Best practices for building business-ready analytics\n\n### What You'll Build\nA comprehensive Semantic View over the TPC-DS dataset that enables simplified querying across multiple sales channels (store, web, catalog), customer demographics, and inventory data.\n\n### Prerequisites\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- Access to `SNOWFLAKE_SAMPLE_DATA` database\n- Access to `ACCOUNTADMIN` role (required for creating semantic views)\n- Basic SQL knowledge\n- Familiarity with data modeling concepts\n\n\u003C!-- ------------------------ --\u003E\n## About the TPC-DS Dataset\n\nThe **TPC-DS (Transaction Processing Performance Council - Decision Support)** benchmark is the industry-standard dataset for modeling complex decision support systems. It simulates a global retail empire with multiple sales channels including:\n\n- **Store sales**: Traditional brick-and-mortar retail transactions\n- **Web sales**: E-commerce transactions\n- **Catalog sales**: Mail-order catalog purchases\n- **Store returns**: Product returns and exchanges\n\nThe dataset includes:\n- **Dimension tables**: Store, Item, Customer, Date, Warehouse, Ship Mode, and more\n- **Fact tables**: Store Sales, Web Sales, Catalog Sales, Inventory, and Returns\n- **Scale Factor**: We're using the SF10TCL scale (10TB scale factor) from Snowflake's sample data\n\n### Traditional SQL vs Semantic SQL\n\nWe'll compare two approaches to querying the data:\n\n- **Traditional SQL**: Requires explicit `JOIN` clauses, fully-qualified table references, and manual aggregation logic\n- **Semantic SQL**: Uses the semantic view to abstract complexity, making queries shorter and more business-focused\n\n### Query Complexity Framework\n\nTo systematically explore how Semantic Views simplify queries, we organize examples by complexity level:\n\n| Complexity Level | Description | What It Means |\n|-----------------|-------------|---------------|\n| **Simple Questions** | Simple filters on 1-2 tables | Basic `SELECT` with `WHERE` clauses |\n| **Simple Questions with Aggregations** | Aggregations and metrics on 1-2 tables | `GROUP BY`, `SUM()`, `COUNT()` on simple schemas |\n| **Advanced Questions** | Simple filters across 3+ tables | Easy question, but complex table relationships |\n| **Advanced Questions with Aggregations** | Aggregations across 3+ tables | Complex calculations across many tables |\n\n**Key Insight:** Semantic Views provide progressively more value as query complexity increases. Simple queries remain simple, but complex multi-table aggregations see dramatic improvements, reducing from 25+ lines of Traditional SQL to 10-12 lines of Semantic SQL.\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n### Notebook\n\nYou can follow along this quickstart using the [build-business-ready-queries-with-snowflake-semantic-views.ipynb](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Snowflake_Semantic_View_Business_Ready_Queries/build-business-ready-queries-with-snowflake-semantic-views.ipynb) notebook file.\n\n### Create Semantic Views\n\nSemantic Views are created using the `CREATE SEMANTIC VIEW` statement, which defines five key components:\n\n| Component | Purpose | Example |\n|-----------|---------|---------|\n| `TABLES` | Register source tables with their primary keys | `store AS SNOWFLAKE_SAMPLE_DATA...store PRIMARY KEY (s_store_sk)` |\n| `RELATIONSHIPS` | Define foreign key relationships between tables | `sales_to_store AS store_sales (ss_store_sk) REFERENCES store` |\n| `FACTS` | Define row-level expressions and computed columns (non-aggregated) | `f_net_profit_tier AS CASE WHEN ss_net_profit \u003E 25000 THEN...` |\n| `DIMENSIONS` | Define categorical attributes for grouping and filtering | `store.s_city AS store.s_city comment='City where store is located'` |\n| `METRICS` | Define reusable aggregate expressions (`SUM`, `COUNT`, `AVG`) | `total_sales AS SUM(ss_sales_price * ss_quantity)` |\n\nFor more details, see the [Snowflake Semantic Views documentation](https://docs.snowflake.com/en/user-guide/views-semantic/sql).\n\nRun the following SQL to create the Semantic View for the TPC-DS dataset:\n\n```sql\nUSE DATABASE SNOWFLAKE_LEARNING_DB;\nUSE SCHEMA PUBLIC;\n\nCREATE OR REPLACE SEMANTIC VIEW tpcds_nlq_view\n  TABLES (\n    store AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.store PRIMARY KEY (s_store_sk),\n    store_sales AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.store_sales PRIMARY KEY (ss_item_sk, ss_ticket_number),\n    web_sales AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.web_sales PRIMARY KEY (ws_item_sk, ws_order_number),\n    catalog_sales AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.catalog_sales PRIMARY KEY (cs_item_sk, cs_order_number),\n    store_returns AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.store_returns PRIMARY KEY (sr_item_sk, sr_ticket_number),\n    item AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.item PRIMARY KEY (i_item_sk),\n    returned_item AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.item PRIMARY KEY (i_item_sk) COMMENT = 'Dimension for returned items',\n    customer AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer PRIMARY KEY (c_customer_sk),\n    customer_address AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer_address PRIMARY KEY (ca_address_sk),\n    current_customer_demographics AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer_demographics PRIMARY KEY (cd_demo_sk) COMMENT = 'Dimension for Current customer demographics',\n    customer_demographics AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.customer_demographics PRIMARY KEY (cd_demo_sk) COMMENT = 'Dimension for Customer demographics at the time of sale',\n    date_dim AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.date_dim PRIMARY KEY (d_date_sk),\n    hd AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.household_demographics PRIMARY KEY (hd_demo_sk),\n    income_band AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.income_band PRIMARY KEY (ib_income_band_sk),\n    web_site AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.web_site PRIMARY KEY (web_site_sk),\n    inventory AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.inventory PRIMARY KEY (inv_date_sk, inv_item_sk, inv_warehouse_sk),\n    ship_mode AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.ship_mode PRIMARY KEY (sm_ship_mode_sk),\n    warehouse AS SNOWFLAKE_SAMPLE_DATA.TPCDS_SF10TCL.warehouse PRIMARY KEY (w_warehouse_sk)\n  )\n  RELATIONSHIPS (\n    sales_to_store AS store_sales (ss_store_sk) REFERENCES store,\n    sales_to_customer AS store_sales (ss_customer_sk) REFERENCES customer,\n    sales_to_date AS store_sales (ss_sold_date_sk) REFERENCES date_dim,\n    sales_to_customer_demo AS store_sales (ss_cdemo_sk) REFERENCES customer_demographics,\n    sales_to_item AS store_sales (ss_item_sk) REFERENCES item,\n    web_sales_to_bill_customer AS web_sales (ws_bill_customer_sk) REFERENCES customer,\n    web_sales_to_sold_date AS web_sales (ws_sold_date_sk) REFERENCES date_dim,\n    web_sales_to_bill_customer_demo AS web_sales (ws_bill_cdemo_sk) REFERENCES customer_demographics,\n    web_sales_to_item AS web_sales (ws_item_sk) REFERENCES item (i_item_sk),\n    web_sales_to_web_site AS web_sales (ws_web_site_sk) REFERENCES web_site,\n    catalog_sales_to_bill_customer AS catalog_sales (cs_bill_customer_sk) REFERENCES customer,\n    catalog_sales_to_sold_date AS catalog_sales (cs_sold_date_sk) REFERENCES date_dim,\n    catalog_sales_to_bill_customer_demo AS catalog_sales (cs_bill_cdemo_sk) REFERENCES customer_demographics,\n    catalog_sales_to_item AS catalog_sales (cs_item_sk) REFERENCES item,\n    sales_returns_to_item AS store_returns (sr_item_sk) REFERENCES returned_item (i_item_sk),\n    sales_returns_to_sales AS store_returns (sr_ticket_number, sr_item_sk, sr_customer_sk) REFERENCES store_sales (ss_ticket_number, ss_item_sk, ss_customer_sk),\n    customer_to_customer_address AS customer (c_current_addr_sk) REFERENCES customer_address (ca_address_sk),\n    customer_to_household_demo AS customer (c_current_hdemo_sk) REFERENCES hd,\n    customer_to_customer_demo AS customer (c_current_cdemo_sk) REFERENCES current_customer_demographics (cd_demo_sk),\n    household_demo_to_income_band AS hd (hd_income_band_sk) REFERENCES income_band,\n    inventory_to_item AS inventory (inv_item_sk) REFERENCES item,\n    inventory_to_date AS inventory (inv_date_sk) REFERENCES date_dim,\n    catalog_sales_to_ship_mode AS catalog_sales (cs_ship_mode_sk) REFERENCES ship_mode,\n    web_sales_to_ship_mode AS web_sales (ws_ship_mode_sk) REFERENCES ship_mode\n  )\n  FACTS (\n    store_sales.f_ss_item_sk AS ss_item_sk\n      COMMENT = 'Item SKU (Stock Keeping Unit) for each sale',\n    store_sales.f_net_profit_tier AS CASE\n      WHEN store_sales.ss_net_profit \u003E 25000 THEN 'More than 25000'\n      WHEN store_sales.ss_net_profit BETWEEN 3000 AND 25000 THEN '3000-25000'\n      WHEN store_sales.ss_net_profit BETWEEN 2000 AND 3000 THEN '2000-3000'\n      WHEN store_sales.ss_net_profit BETWEEN 300 AND 2000 THEN '300-2000'\n      WHEN store_sales.ss_net_profit BETWEEN 250 AND 300 THEN '250-300'\n      WHEN store_sales.ss_net_profit BETWEEN 200 AND 250 THEN '200-250'\n      WHEN store_sales.ss_net_profit BETWEEN 150 AND 200 THEN '150-200'\n      WHEN store_sales.ss_net_profit BETWEEN 100 AND 150 THEN '100-150'\n      WHEN store_sales.ss_net_profit BETWEEN 50 AND 100 THEN ' 50-100'\n      WHEN store_sales.ss_net_profit BETWEEN 0 AND 50 THEN '  0- 50'\n      ELSE ' 50 or Less'\n    END\n      COMMENT = 'Tier labels for net profit from store sales',\n    date_dim.f_year AS date_dim.d_year\n      COMMENT = 'Year of Date',\n    store_returns.f_ss_has_sales AS IFF(store_sales.f_ss_item_sk IS NOT NULL, TRUE, FALSE)\n      COMMENT = 'Boolean indicating whether valid store sales item was returned'\n  )\n  DIMENSIONS (\n    store.s_store_sk AS store.s_store_sk\n      COMMENT = 'Store SKU (Stock Keeping Unit)',\n    store.s_city AS store.s_city\n      COMMENT = 'City where the store is located',\n    date_dim.d_year AS date_dim.d_year\n      COMMENT = 'Year of Date',\n    date_dim.d_date AS date_dim.d_date\n      COMMENT = 'Date of the day',\n    customer.c_first_name AS customer.c_first_name\n      COMMENT = 'First name of the customer',\n    customer.c_last_name AS customer.c_last_name\n      COMMENT = 'Last name of the customer',\n    current_customer_demographics.cd_dep_count AS current_customer_demographics.cd_dep_count\n      COMMENT = 'Current number of dependents for the customer',\n    customer_demographics.cd_dep_count AS customer_demographics.cd_dep_count\n      COMMENT = 'Number of dependents for the customer at the time of sale',\n    store.s_store_name AS store.s_store_name\n      COMMENT = 'the names of stores, likely a list of store names in a retail or commercial setting',\n    store_sales.ss_sale_year AS date_dim.d_year\n      COMMENT = 'Year of store sale',\n    store.s_manager AS store.s_manager\n      COMMENT = 'Store manager',\n    store.s_floor_space AS store.s_floor_space\n      COMMENT = 'Total floor space in square feet',\n    store.s_store_id AS store.s_store_id\n      COMMENT = 'Unique identifier for each store',\n    item.i_brand AS item.i_brand\n      COMMENT = 'Brands of export items',\n    item.i_product_name AS item.i_product_name\n      COMMENT = 'Product names',\n    item.i_manufact AS item.i_manufact\n      COMMENT = 'Manufacturing items, including antibarable, n stbarpri, and barationese',\n    customer_address.ca_state AS ca_state\n      COMMENT = 'State where the customer address is located',\n    item.i_item_id AS item.i_item_id\n      COMMENT = 'Unique item identifiers',\n    item.i_item_sk AS i_item_sk\n      COMMENT = 'Item identifier SKU (Stock Keeping Unit)',\n    customer.c_state AS customer_address.ca_state\n      COMMENT = 'Customer state abbreviation',\n    hd.hd_vehicle_count AS hd.hd_vehicle_count\n      COMMENT = 'Number of vehicles owned by the household',\n    customer.c_customer_id AS customer.c_customer_id\n      COMMENT = 'Customer identifier',\n    income_band.ib_income_band_sk AS income_band.ib_income_band_sk\n      COMMENT = 'Income Band Identifier',\n    customer_demographics.gender AS customer_demographics.cd_gender\n      COMMENT = 'Gender of the customer at the time of sale',\n    current_customer_demographics.gender AS current_customer_demographics.cd_gender\n      COMMENT = 'Gender of the customer',\n    store_sales.ss_net_profit_tier AS f_net_profit_tier\n      COMMENT = 'Tier labels for net profit from store sales',\n    store_sales.ss_customer_sk AS store_sales.ss_customer_sk\n      COMMENT = 'Customer ID',\n    store_sales.ss_store_sk AS store_sales.ss_store_sk\n      COMMENT = 'Store''s SKU (Stock Keeping Unit) where sales happened',\n    income_band.ib_lower_bound AS income_band.ib_lower_bound\n      COMMENT = 'Lower bound of income bands',\n    income_band.ib_upper_bound AS income_band.ib_upper_bound\n      COMMENT = 'Upper bound of income bands',\n    hd.hd_buy_potential AS hd.hd_buy_potential\n      COMMENT = 'Household buying potential',\n    customer_address.ca_city AS ca_city\n      COMMENT = 'City where the customer address is located',\n    customer.c_city AS customer_address.ca_city\n      COMMENT = 'City where the customer is located',\n    item.i_item_size AS i_size\n      COMMENT = 'Item size',\n    store_sales.ss_item_id AS item.i_item_sk\n      COMMENT = 'Identifier of item that was sold through store',\n    store_sales.ss_product_name AS item.i_product_name\n      COMMENT = 'Product name of item that was sold through store',\n    store_sales.ss_item_size AS item.i_item_size\n      COMMENT = 'Size of item that was sold through store',\n    web_sales.ws_item_id AS item.i_item_sk\n      COMMENT = 'Identifier of item that was sold through web',\n    web_sales.ws_product_name AS item.i_product_name\n      COMMENT = 'Product name of item that was sold through web',\n    web_sales.ws_item_size AS item.i_item_size\n      COMMENT = 'Size of item that was sold through web',\n    catalog_sales.cs_item_id AS item.i_item_sk\n      COMMENT = 'Identifier of item that was sold through catalog',\n    catalog_sales.cs_product_name AS item.i_product_name\n      COMMENT = 'Product name of item that was sold through catalog',\n    catalog_sales.cs_item_size AS item.i_item_size\n      COMMENT = 'Size of item that was sold through catalog',\n    customer.c_customer_sk AS c_customer_sk\n      COMMENT = 'Customer unique identifier',\n    web_site.web_site_sk AS web_site.web_site_sk\n      COMMENT = 'Unique identifier for each web site',\n    web_site.web_name AS web_site.web_name\n      COMMENT = 'Web site name',\n    item.i_brand_id AS i_brand_id\n      COMMENT = 'Brand ID for items',\n    item.i_category AS item.i_category\n      COMMENT = 'Product categories',\n    item.i_color AS i_color\n      COMMENT = 'Color options',\n    store.s_hours AS s_hours\n      COMMENT = 'Store hours',\n    store.s_state AS s_state\n      COMMENT = 'Store state',\n    customer_address.ca_zip AS ca_zip\n      COMMENT = 'Customer zip code',\n    customer.ca_zip AS customer_address.ca_zip\n      COMMENT = 'Customer zip code',\n    customer.ca_state AS customer_address.ca_state\n      COMMENT = 'State where the customer address is located',\n    ship_mode.sm_type AS sm_type\n      COMMENT = 'Shipping mode type',\n    ship_mode.sm_carrier AS sm_carrier\n      COMMENT = 'Shipping mode carrier',\n    warehouse.w_warehouse_name AS w_warehouse_name\n      COMMENT = 'Warehouse name',\n    warehouse.w_city AS w_city\n      COMMENT = 'Warehouse city',\n    warehouse.w_warehouse_sq_ft AS w_warehouse_sq_ft\n      COMMENT = 'Warehouse square footage',\n    item.i_current_price AS i_current_price\n      COMMENT = 'Current price of the item',\n    store.s_number_employees AS s_number_employees\n      COMMENT = 'Number of employees in the store',\n    customer.c_birth_country AS c_birth_country\n      COMMENT = 'Country where the customer was born',\n    catalog_sales.cs_ship_mode_sk AS catalog_sales.cs_ship_mode_sk\n      COMMENT = 'Unique Identifier for Shipping mode  for catalog sales',\n    web_sales.ws_ship_mode_sk AS web_sales.ws_ship_mode_sk\n      COMMENT = 'Unique Identifier for Shipping mode for web sales',\n    hd.hd_income_band_sk AS hd.hd_income_band_sk\n      COMMENT = 'Unique Identifier for Household income band ',\n    catalog_sales.cs_item_sk AS catalog_sales.cs_item_sk\n      COMMENT = 'Unique Identifier for catalog sales item',\n    store_sales.ss_item_sk AS store_sales.ss_item_sk\n      COMMENT = 'Unique Identifier for store sales item',\n    web_sales.ws_item_sk AS web_sales.ws_item_sk\n      COMMENT = 'Unique Identifier for web sales item'\n  )\n  METRICS (\n    customer.customer_count AS COUNT(DISTINCT c_customer_sk)\n      COMMENT = 'Count of distinct customer identifiers',\n    item.product_count AS (COUNT(DISTINCT i_item_sk))\n      COMMENT = 'Count of distinct products',\n    store_returns.ss_store_returns AS COUNT_IF(f_ss_has_sales)\n      COMMENT = 'Count of records that have a valid store sales item returned',\n    web_sales.total_sales AS SUM(CAST(ws_ext_sales_price * ws_quantity AS DECIMAL(38, 2)))\n      COMMENT = 'Sum of the revenue (sales price multiplied by quantity) from web sales',\n    store_sales.start_date AS MIN(date_dim.d_date)\n      COMMENT = 'Min date (start date) of the store sales',\n    store_sales.end_date AS MAX(date_dim.d_date)\n      COMMENT = 'Max date (end date) of the store sales',\n    web_sales.w_net_profit AS SUM(ws_net_profit)\n      COMMENT = 'Sum of net profit through web sales',\n    catalog_sales.c_net_profit AS SUM(cs_net_profit)\n      COMMENT = 'Sum of net profit through catalog sales',\n    store_sales.s_net_profit AS SUM(ss_net_profit)\n      COMMENT = 'Sum of net profit through store sales',\n    catalog_sales.total_sales AS SUM(CAST(cs_sales_price * cs_quantity AS DECIMAL(38, 2)))\n      COMMENT = 'Sum of revenue (sales price multiplied by quantity) from catalog sales',\n    store_sales.ss_customer_count AS COUNT(ss_customer_sk)\n      COMMENT = 'Count of customers who purchased through store sales',\n    store_sales.ss_average_sale_quantity AS CASE\n      WHEN COUNT(ss_quantity) = 0 THEN NULL\n      ELSE CAST((SUM(ss_quantity) / COUNT(ss_quantity)) AS DOUBLE)\n    END\n      COMMENT = 'Average store sale quantity calculated as sum of sold quantity divided by number of rows',\n    store_sales.total_sales AS SUM(ss_sales_price * ss_quantity)\n      COMMENT = 'Sum of the revenue (sales price multiplied by quantity) from store sales',\n    web_sales.total_quantity_sold AS COALESCE(SUM(ws_quantity), 0)\n      COMMENT = 'Sum of number of items sold through web sales',\n    web_sales.total_shipping_cost AS SUM(ws_ext_ship_cost)\n      COMMENT = 'Sum of the shipping cost',\n    store_sales.ss_average_store_net_profit AS CASE\n      WHEN (SUM(ss_quantity) = 0) THEN NULL\n      ELSE CAST(CAST(SUM(ss_net_profit) AS DECIMAL(17, 2)) / SUM(ss_quantity) AS DECIMAL(37, 22))\n    END\n      COMMENT = 'Average profit sold through store',\n    inventory.total_inventory_on_hand AS SUM(inv_quantity_on_hand)\n      COMMENT = 'Total inventory on hand for a given item',\n    store_sales.total_quantity_sold AS COALESCE(SUM(ss_quantity), 0)\n      COMMENT = 'Total quantity sold for a given item in a store',\n    store_returns.total_quantity_returned AS COALESCE(SUM(sr_return_quantity), 0)\n      COMMENT = 'Total quantity returned for a given item in a store',\n    catalog_sales.start_date AS MIN(date_dim.d_date)\n      COMMENT = 'Min date for catalog sales',\n    catalog_sales.end_date AS MAX(date_dim.d_date)\n      COMMENT = 'Max date for catalog sales',\n    catalog_sales.unique_catalog_customers AS COUNT(DISTINCT cs_bill_customer_sk)\n      COMMENT = 'Unique customers who made a purchase through catalog sales',\n    catalog_sales.total_quantity_sold AS COALESCE(SUM(cs_quantity), 0)\n      COMMENT = 'Sum of number of items sold through catalog sales'\n  );\n```\n\nAfter running the above SQL, you should see the following output confirming the semantic view was created successfully:\n\n```\n+----------------------------------------------------+\n|                       status                       |\n+----------------------------------------------------+\n| Semantic view TPCDS_NLQ_VIEW successfully created. |\n+----------------------------------------------------+\n```\n\n\u003C!-- ------------------------ --\u003E\n## Traditional vs Semantic SQL\n\nNow that we've created our semantic view, let's compare how traditional SQL and semantic SQL handle queries of varying complexity.\n\nWe'll use the TPC-DS benchmark to demonstrate queries ranging from simple filters to complex multi-table aggregations. Each example will show:\n1. **Traditional SQL**: The standard approach with explicit `JOIN`s and aggregations\n2. **Semantic SQL**: The simplified approach using our semantic view\n\n\u003C!-- ------------------------ --\u003E\n## Simple Questions\n\nThese queries demonstrate simple filtering and selection operations on single tables or simple joins. They represent straightforward business questions that can be answered with basic SQL operations like `WHERE` clauses and simple aggregations.\n\n### What are all of the unique store numbers in Tennessee?\n\n**Traditional SQL:** Queries the store table directly with a `WHERE` filter on state.\n\n```sql\nSELECT\n    DISTINCT s_store_sk\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store\nWHERE\n    s_state = 'TN'\n    AND s_store_sk IS NOT NULL\nORDER BY s_store_sk;\n```\n\n**Semantic SQL:** Uses `DIMENSIONS` to select the store identifier directly. No need to specify the full table path, the semantic view knows where `s_store_sk` lives.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    DIMENSIONS store.s_store_sk\n    WHERE s_state='TN'\n)\nORDER BY s_store_sk;\n```\n\n**Expected Output:** Returns unique store IDs for Tennessee stores, ordered by store number.\n\n```\n+------------+\n| S_STORE_SK |\n+------------+\n|          1 |\n|         22 |\n|         52 |\n|         76 |\n|         96 |\n|        102 |\n|        112 |\n|        140 |\n|        ... |\n+------------+\n```\n\n### What are the first and last names of all customers that have more than 5 dependents?\n\n**Traditional SQL:** Uses `JOIN` on customer and customer_demographics tables with a `WHERE` filter on dependent count.\n\n```sql\nSELECT\n    customer.c_first_name,\n    customer.c_last_name\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.customer\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_demographics AS current_customer_demographics\n    ON customer.c_current_cdemo_sk = current_customer_demographics.cd_demo_sk\nWHERE\n    current_customer_demographics.cd_dep_count \u003E 5\nORDER BY\n    c_first_name ASC, c_last_name ASC\nLIMIT 100;\n```\n\n**Semantic SQL:** Uses `FACTS` to retrieve customer attributes. The semantic view handles the `JOIN` between `customer` and `customer_demographics` automatically, so no explicit `JOIN` is needed.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    FACTS customer.c_first_name first_name, customer.c_last_name last_name\n    WHERE current_customer_demographics.cd_dep_count \u003E 5\n)\nORDER BY first_name ASC, last_name ASC\nLIMIT 100;\n```\n\n**Expected Output:** Returns customer names sorted alphabetically, filtered to those with more than 5 dependents.\n\n```\n+------------+------------+\n| FIRST_NAME | LAST_NAME  |\n+------------+------------+\n| Aaron      | Aaron      |\n| Aaron      | Aaron      |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abbott     |\n| Aaron      | Abel       |\n| Aaron      | Abel       |\n| Aaron      | Abel       |\n| ...        | ...        |\n+------------+------------+\n```\n\n### What is the name, manager and floor space of each store in the city of Midway?\n\n**Traditional SQL:** Uses `SELECT` on multiple columns from the store table with a `WHERE` city filter.\n\n```sql\nSELECT\n    s_store_name,\n    s_manager,\n    s_floor_space\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store\nWHERE\n    s_city = 'Midway'\nORDER BY s_store_name;\n```\n\n**Semantic SQL:** Uses `FACTS` to select multiple store attributes in one call. Clean, readable syntax without needing to reference the full table path.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    FACTS s_store_name, s_manager, s_floor_space\n    WHERE s_city = 'Midway'\n)\nORDER BY s_store_name;\n```\n\n**Expected Output:** Returns store details for all stores located in Midway, sorted by store name.\n\n```\n+--------------+-------------------+--------------+\n| S_STORE_NAME | S_MANAGER         | S_FLOOR_SPACE|\n+--------------+-------------------+--------------+\n| ation        | Harry Harkins     |      6849804 |\n| bar          | Christopher Garris|      7610137 |\n| eing         | Stephen Garcia    |      6101180 |\n| eing         | Brian Strickland  |      6396268 |\n| eing         | Robert Tyson      |      5903931 |\n| ese          | Dean Patel        |      9875948 |\n| ought        | Robert Yost       |      5945154 |\n+--------------+-------------------+--------------+\n```\n\n\u003C!-- ------------------------ --\u003E\n## Simple Questions with Aggregations\n\nThese queries introduce aggregations, grouping, and metrics while maintaining relatively simple table relationships. The semantic view's pre-defined metrics significantly reduce query complexity.\n\n### What is the total count of customers for each customer home state?\n\n**Traditional SQL:** Uses `JOIN` on customer and address tables, then `GROUP BY` state with `COUNT(DISTINCT ...)`.\n\n```sql\nSELECT\n    ca_state,\n    COUNT(DISTINCT c_customer_sk) AS customer_count\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.customer\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_address\n    ON customer.c_current_addr_sk = customer_address.ca_address_sk\nGROUP BY\n    ca_state\nORDER BY\n    customer_count DESC, ca_state\nLIMIT 100;\n```\n\n**Semantic SQL:** Uses `DIMENSIONS` for grouping and `METRICS` for the pre-defined `customer_count` aggregation. The `JOIN` between `customer` and `customer_address` is handled automatically by the semantic view's relationships.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    DIMENSIONS customer_address.ca_state AS ca_state\n    METRICS customer.customer_count\n)\nORDER BY customer_count DESC\nLIMIT 100;\n```\n\n**Expected Output:** Returns customer counts by state, with Texas (TX) having the most customers at over 5 million.\n\n```\n+----------+----------------+\n| CA_STATE | CUSTOMER_COUNT |\n+----------+----------------+\n| TX       |        5154196 |\n| GA       |        3225083 |\n| VA       |        2735265 |\n| KY       |        2431364 |\n| MO       |        2213416 |\n| KS       |        2131894 |\n| IL       |        2048115 |\n| NC       |        2034567 |\n| ...      |            ... |\n+----------+----------------+\n```\n\n### What was the overall web sales for the year 2002?\n\n**Traditional SQL:** Uses `JOIN` on web_sales with date_dim and calculates total revenue using `SUM()` with `CAST()`.\n\n```sql\nSELECT\n    SUM(CAST(ws_ext_sales_price * ws_quantity AS DECIMAL(38, 2))) AS total_sales\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.web_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON web_sales.ws_sold_date_sk = date_dim.d_date_sk\nWHERE\n    date_dim.d_year = 2002;\n```\n\n**Semantic SQL:** Uses `METRICS` to call the pre-defined `total_sales` calculation. No need to write the complex `SUM(CAST(...))` formula since it's already defined in the semantic view. The `JOIN` to `date_dim` is automatic.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    METRICS web_sales.total_sales\n    WHERE date_dim.d_year = 2002\n)\nLIMIT 100;\n```\n\n**Expected Output:** Returns total web sales for 2002: approximately $2.46 quadrillion (TPC-DS scale factor).\n\n```\n+----------------------+\n|     TOTAL_SALES      |\n+----------------------+\n| 2458971149461555.79  |\n+----------------------+\n```\n\n### What is the count of products in each product category?\n\n**Traditional SQL:** Uses `GROUP BY` category and `COUNT(DISTINCT ...)` to count products per category.\n\n```sql\nSELECT\n    i_category AS product_category,\n    COUNT(DISTINCT i_item_sk) AS product_count\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.item\nWHERE\n    i_category IS NOT NULL\n    AND i_item_sk IS NOT NULL\nGROUP BY\n    i_category\nORDER BY\n    i_category\nLIMIT 5000;\n```\n\n**Semantic SQL:** Combines `DIMENSIONS` for grouping by category and `METRICS` for the pre-defined `product_count`. The aggregation logic (`COUNT(DISTINCT i_item_sk)`) is encapsulated in the metric definition.\n\n```sql\nSELECT * FROM semantic_view(tpcds_nlq_view\n    DIMENSIONS item.i_category\n    METRICS item.product_count\n)\nORDER BY i_category\nlimit 100;\n```\n\n**Expected Output:** Returns all 10 product categories with approximately 40,000 products each.\n\n```\n+------------------+---------------+\n| PRODUCT_CATEGORY | PRODUCT_COUNT |\n+------------------+---------------+\n| Books            |         39643 |\n| Children         |         40406 |\n| Electronics      |         40172 |\n| Home             |         40124 |\n| Jewelry          |         40114 |\n| Men              |         39892 |\n| Music            |         40342 |\n| Shoes            |         40158 |\n| Sports           |         40315 |\n| Women            |         39871 |\n+------------------+---------------+\n```\n\n\u003C!-- ------------------------ --\u003E\n## Advanced Questions\n\nThese queries involve simple filters but require joining multiple tables across complex relationships. The semantic view dramatically simplifies these queries by hiding the complex join logic.\n\n### What is the customer id and vehicle count for every customer in income band 9?\n\n**Traditional SQL:** Uses multiple `JOIN` clauses across customer, household_demographics, and income_band tables with a `WHERE` filter on income band.\n\n```sql\nSELECT DISTINCT\n    customer.c_customer_id,\n    hd.hd_vehicle_count\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.customer\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.household_demographics hd\n    ON customer.c_current_hdemo_sk = hd.hd_demo_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.income_band\n    ON hd.hd_income_band_sk = income_band.ib_income_band_sk\nWHERE\n    income_band.ib_income_band_sk = 9\n    AND customer.c_customer_id IS NOT NULL\nORDER BY\n    customer.c_customer_id\nLIMIT 100;\n```\n\n**Semantic SQL:** Uses `FACTS` to access attributes across 3 related tables (`customer`, `household_demographics`, `income_band`). The semantic view's pre-defined relationships handle all the `JOIN`s automatically. What was 3 `JOIN`s becomes a single semantic query.\n\n```sql\nSELECT DISTINCT c_customer_id, hd_vehicle_count FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        FACTS customer.c_customer_id, hd.hd_vehicle_count\n        WHERE ib_income_band_sk = 9 AND c_customer_id IS NOT NULL\n    )\n)\nORDER BY c_customer_id\nLIMIT 5000;\n```\n\n**Expected Output:** Returns customer IDs and their vehicle counts for income band 9, with vehicle counts ranging from 0-4.\n\n```\n+------------------+------------------+\n| C_CUSTOMER_ID    | HD_VEHICLE_COUNT |\n+------------------+------------------+\n| AAAAAAAAAAAALAA  |                2 |\n| AAAAAAAAAAAAMAA  |                0 |\n| AAAAAAAAAAAPCA   |                3 |\n| AAAAAAAAAABCBA   |                4 |\n| AAAAAAAAAABDBA   |                4 |\n| AAAAAAAAABJDA    |                2 |\n| AAAAAAAAAACJDA   |                3 |\n| AAAAAAAAAACKAA   |                4 |\n| AAAAAAAAAACKBA   |                0 |\n| AAAAAAAAAACMDA   |                1 |\n| ...              |              ... |\n+------------------+------------------+\n```\n\n### What is the net profit tier for each store name and gender in the 2002 sales year?\n\n**Traditional SQL:** Uses 4 `JOIN` clauses and a complex `CASE` statement to categorize profit into tiers.\n\n```sql\nSELECT DISTINCT\n    store.s_store_name,\n    customer_demographics.cd_gender,\n    CASE\n        WHEN store_sales.ss_net_profit \u003E 25000 THEN 'More than 25000'\n        WHEN store_sales.ss_net_profit BETWEEN 3000 AND 25000 THEN '3000-25000'\n        WHEN store_sales.ss_net_profit BETWEEN 2000 AND 3000 THEN '2000-3000'\n        WHEN store_sales.ss_net_profit BETWEEN 300 AND 2000 THEN '300-2000'\n        WHEN store_sales.ss_net_profit BETWEEN 250 AND 300 THEN '250-300'\n        WHEN store_sales.ss_net_profit BETWEEN 200 AND 250 THEN '200-250'\n        WHEN store_sales.ss_net_profit BETWEEN 150 AND 200 THEN '150-200'\n        WHEN store_sales.ss_net_profit BETWEEN 100 AND 150 THEN '100-150'\n        WHEN store_sales.ss_net_profit BETWEEN 50 AND 100 THEN ' 50-100'\n        WHEN store_sales.ss_net_profit BETWEEN 0 AND 50 THEN '  0- 50'\n        ELSE ' 50 or Less'\n    END AS net_profit_tier\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_demographics\n    ON store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    date_dim.d_year = 2002\nORDER BY\n    s_store_name, cd_gender, net_profit_tier\nLIMIT 100;\n```\n\n**Semantic SQL:** Uses `FACTS` with the pre-defined `f_net_profit_tier` calculation. The complex `CASE` statement is encapsulated in the semantic view, so there's no need to rewrite the tiering logic. `JOIN`s across 4 tables are automatic.\n\n```sql\nSELECT DISTINCT s_store_name, gender, f_net_profit_tier FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        FACTS store_sales.f_net_profit_tier, \n              store.s_store_name, \n              customer_demographics.gender,\n              date_dim.d_year\n    )\n)\nWHERE d_year = 2002\n      AND NOT gender IS NULL\nORDER BY s_store_name, gender, f_net_profit_tier\nLIMIT 100;\n```\n\n**Expected Output:** Returns distinct store/gender/tier combinations, showing profit tiers from \"$0-50\" up to \"$3000-25000\".\n\n```\n+--------------+-----------+-----------------+\n| S_STORE_NAME | CD_GENDER | NET_PROFIT_TIER |\n+--------------+-----------+-----------------+\n| able         | F         | 0- 50           |\n| able         | F         | 50 or Less      |\n| able         | F         | 50-100          |\n| able         | F         | 100-150         |\n| able         | F         | 150-200         |\n| able         | F         | 200-250         |\n| able         | F         | 2000-3000       |\n| able         | F         | 250-300         |\n| able         | F         | 300-2000        |\n| able         | F         | 3000-25000      |\n| ...          | ...       | ...             |\n+--------------+-----------+-----------------+\n```\n\n### What was the first name and gender of each customer that shopped in the store named 'ese' in 2001?\n\n**Traditional SQL:** Uses 5 `JOIN` clauses to link store_sales to customer details with `WHERE` filters on store name and year.\n\n```sql\nSELECT DISTINCT\n    customer.c_first_name,\n    customer_demographics.cd_gender\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer\n    ON store_sales.ss_customer_sk = customer.c_customer_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.customer_demographics\n    ON store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    store.s_store_name = 'ese'\n    AND date_dim.d_year = 2001\nORDER BY\n    c_first_name, cd_gender\nLIMIT 5000;\n```\n\n**Semantic SQL:** Uses `FACTS` to access attributes from 5 related tables (`store_sales`, `customer`, `customer_demographics`, `store`, `date_dim`). The semantic view handles all `JOIN`s through pre-defined relationships. What was 4 explicit `JOIN`s becomes implicit.\n\n```sql\nSELECT DISTINCT c_first_name, gender FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        FACTS store_sales.ss_customer_sk,\n              customer.c_first_name,\n              customer_demographics.gender,\n              date_dim.d_year,\n              store.s_store_name\n    )\n)\nWHERE s_store_name = 'ese'\n    AND d_year = 2001\n    AND NOT gender IS NULL\nORDER BY c_first_name, gender\nLIMIT 5000;\n```\n\n**Expected Output:** Returns unique first name and gender combinations for customers who shopped at store 'ese' in 2001.\n\n```\n+--------------+-----------+\n| C_FIRST_NAME | CD_GENDER |\n+--------------+-----------+\n| Aaron        | F         |\n| Aaron        | M         |\n| Abbey        | F         |\n| Abbey        | M         |\n| Abbie        | F         |\n| Abbie        | M         |\n| Abby         | F         |\n| Abby         | M         |\n| Abdul        | F         |\n| Abdul        | M         |\n| ...          | ...       |\n+--------------+-----------+\n```\n\n\u003C!-- ------------------------ --\u003E\n## Advanced Questions with Aggregations\n\nThese queries represent the most challenging scenarios, combining complex aggregations with intricate multi-table joins. The semantic view provides the greatest value here by abstracting both the complex relationships and pre-computing metrics.\n\n### For each store state in the year 2002, what was the count of store customers and the average sales quantity?\n\n**Traditional SQL:** Uses `JOIN` on store_sales with store and date_dim, then `GROUP BY` with `COUNT()` and `CASE` for null-safe average calculation.\n\n```sql\nSELECT\n    store.s_state,\n    COUNT(store_sales.ss_customer_sk) AS store_customer_count,\n    CASE \n        WHEN COUNT(store_sales.ss_quantity) = 0 THEN NULL \n        ELSE CAST((SUM(store_sales.ss_quantity) / COUNT(store_sales.ss_quantity)) AS DOUBLE) \n    END AS average_store_sales_quantity\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    date_dim.d_year = 2002\n    AND store.s_state IS NOT NULL\nGROUP BY\n    store.s_state\nORDER BY\n    store.s_state\nLIMIT 5000;\n```\n\n**Semantic SQL:** Uses `DIMENSIONS` for grouping and multiple `METRICS` (`ss_customer_count`, `ss_average_sale_quantity`). The complex `CASE` statement for average calculation and all `JOIN`s are encapsulated in the semantic view.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW (\n    tpcds_nlq_view\n    DIMENSIONS store.s_state\n    METRICS\n        store_sales.ss_customer_count,\n        store_sales.ss_average_sale_quantity\n    WHERE date_dim.d_year = 2002\n) AS R(store_state, store_customer_count, average_store_sales_quantity)\nWHERE store_state IS NOT NULL\nORDER BY store_state\nLIMIT 5000;\n```\n\n**Expected Output:** Returns metrics per state for 2002. Georgia (GA) leads with over 500 million customers, average quantity ~50 units.\n\n```\n+---------+----------------------+------------------------------+\n| S_STATE | STORE_CUSTOMER_COUNT | AVERAGE_STORE_SALES_QUANTITY |\n+---------+----------------------+------------------------------+\n| AL      |            148448445 |                    50.508838 |\n| CA      |            176854713 |                    50.503677 |\n| CO      |             63727493 |                     50.49999 |\n| FL      |             49548871 |                     50.51083 |\n| GA      |            502313211 |                    50.504699 |\n| IA      |             99133835 |                    50.491451 |\n| IL      |             77971044 |                     50.50326 |\n| IN      |            198222807 |                    50.500988 |\n| KS      |            155749285 |                    50.494168 |\n| KY      |            141595491 |                    50.503138 |\n| ...     |                  ... |                          ... |\n+---------+----------------------+------------------------------+\n```\n\n### What were the store sales in 2002 for each store manager in the state of Tennessee?\n\n**Traditional SQL:** Uses `JOIN` on store_sales with store and date_dim, `GROUP BY` manager with `SUM()` and `WHERE` filters on state/year.\n\n```sql\nSELECT\n    store.s_manager,\n    SUM(store_sales.ss_sales_price * store_sales.ss_quantity) AS total_sales\nFROM\n    snowflake_sample_data.tpcds_sf10tcl.store_sales\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.store\n    ON store_sales.ss_store_sk = store.s_store_sk\nJOIN\n    snowflake_sample_data.tpcds_sf10tcl.date_dim\n    ON store_sales.ss_sold_date_sk = date_dim.d_date_sk\nWHERE\n    store.s_state = 'TN'\n    AND date_dim.d_year = 2002\n    AND store.s_manager IS NOT NULL\nGROUP BY\n    store.s_manager\nORDER BY\n    total_sales DESC NULLS LAST\nLIMIT 5000;\n```\n\n**Semantic SQL:** Combines `DIMENSIONS` for grouping by manager and `METRICS` for the pre-defined `total_sales` calculation. The `SUM` formula and all table `JOIN`s are handled by the semantic view.\n\n```sql\nSELECT s_manager, total_sales FROM (\n    SELECT * FROM SEMANTIC_VIEW (\n        tpcds_nlq_view\n        DIMENSIONS store.s_manager\n        METRICS store_sales.total_sales\n        WHERE store.s_state = 'TN' AND d_year = 2002 \n              AND store.s_manager IS NOT NULL\n    )\n)\nORDER BY total_sales DESC NULLS LAST\nLIMIT 5000;\n```\n\n**Expected Output:** Returns sales per TN manager in 2002. Top performer Robert Young at ~$13.5 billion, all managers close in performance.\n\n```\n+------------------+----------------+\n| S_MANAGER        | TOTAL_SALES    |\n+------------------+----------------+\n| Robert Young     | 13540376902.13 |\n| Donald Dodson    | 13531825145.25 |\n| Jesus Dickinson  | 13520973098.82 |\n| Russell Pedigo   | 13506626925.90 |\n| Norman Gould     | 13503720972.50 |\n| Jesse Nielson    | 13493375137.32 |\n| Armando Vasquez  | 13489216257.07 |\n| John Fogle       | 13482056094.88 |\n| Daniel Slaton    | 13479913604.98 |\n| Frederick Bunn   | 13479143024.52 |\n| ...              |            ... |\n+------------------+----------------+\n```\n\n### What were the web sales by site in New Jersey?\n\n**Question:** For each website, what is the quantity sold and total shipping cost for customers with a shipping address in the state of New Jersey?\n\n**Traditional SQL:** Uses `JOIN` on web_sales with web_site and customer_address, `GROUP BY` site with `SUM()` for NJ shipments.\n\n```sql\nSELECT\n  ws.ws_web_site_sk AS \"web_site_sk\",\n  web.web_name AS \"web_name\",\n  SUM(ws.ws_quantity) AS \"total_quantity_sold\",\n  SUM(ws.ws_ext_ship_cost) AS \"total_shipping_cost\"\nFROM\n  snowflake_sample_data.tpcds_sf10tcl.web_sales AS ws\n  JOIN snowflake_sample_data.tpcds_sf10tcl.web_site AS web ON ws.ws_web_site_sk = web.web_site_sk\n  JOIN snowflake_sample_data.tpcds_sf10tcl.customer_address AS ca ON ws.ws_ship_addr_sk = ca.ca_address_sk\nWHERE\n  ca.ca_state='NJ'\n  AND web.web_name IS NOT NULL\n  AND ws.ws_quantity IS NOT NULL\n  AND ws.ws_ext_ship_cost IS NOT NULL\nGROUP BY\n  ws.ws_web_site_sk,\n  web.web_name\nORDER BY\n  ws.ws_web_site_sk;\n```\n\n**Semantic SQL:** Uses `DIMENSIONS` for grouping by website and `METRICS` for pre-defined `total_quantity_sold` and `total_shipping_cost`. The `JOIN`s between `web_sales`, `web_site`, and `customer_address` are handled automatically.\n\n```sql\nSELECT * FROM SEMANTIC_VIEW(\n    tpcds_nlq_view\n    DIMENSIONS web_site.web_site_sk, web_site.web_name\n    METRICS web_sales.total_quantity_sold, web_sales.total_shipping_cost\n    WHERE customer_address.ca_state='NJ'\n)\nWHERE web_name IS NOT NULL\n    AND total_quantity_sold IS NOT NULL\n    AND total_shipping_cost IS NOT NULL\nORDER BY web_site_sk;\n```\n\n**Expected Output:** Returns web sales metrics for NJ shipments. Site_0 (ID 1) leads with ~61M items sold and ~$1.5B in shipping.\n\n```\n+-------------+----------+---------------------+---------------------+\n| WEB_SITE_SK | WEB_NAME | TOTAL_QUANTITY_SOLD | TOTAL_SHIPPING_COST |\n+-------------+----------+---------------------+---------------------+\n|           1 | site_0   |            61028703 |       1542231372.61 |\n|           2 | site_0   |            36583068 |        924147129.20 |\n|           3 | site_0   |            24539919 |        618876530.14 |\n|           4 | site_0   |            24398128 |        614892075.27 |\n|           5 | site_0   |            24353870 |        614586576.94 |\n|           6 | site_0   |            12364943 |        312510257.79 |\n|           7 | site_1   |            61102675 |       1538701306.86 |\n|           8 | site_1   |            36505051 |        923039800.32 |\n|           9 | site_1   |            24509946 |        619652299.83 |\n|          10 | site_1   |            24399101 |        616355076.94 |\n|         ... | ...      |                 ... |                 ... |\n+-------------+----------+---------------------+---------------------+\n```\n\n\u003C!-- ------------------------ --\u003E\n## Lessons Learned\n\nThroughout this tutorial, we discovered key patterns for simplifying queries with Semantic Views:\n\n| Semantic Parameter | What It Does | When to Use |\n|--------------------|--------------|-------------|\n| `DIMENSIONS` | Selects categorical attributes for grouping | When you need to `GROUP BY` or select descriptive data |\n| `METRICS` | Calls pre-defined aggregations (`SUM`, `COUNT`, `AVG`) | When you need calculated measures without writing formulas |\n| `FACTS` | Retrieves row-level computed values | When you need detailed or derived data from the semantic model |\n| `WHERE` | Filters data using any attribute | Same as traditional SQL `WHERE` clause |\n\n### Complexity Comparison: Key Findings\n\nWe explored queries across four complexity levels:\n\n| Complexity Level | Traditional SQL | Semantic SQL | Improvement |\n|------------------|----------------|--------------|-------------|\n| **Simple Questions** | 8-13 lines | 6-7 lines | Modest: simpler syntax |\n| **Simple Questions with Aggregations** | 9-13 lines | 6-7 lines | Moderate: metrics eliminate formulas |\n| **Advanced Questions** | 17-23 lines | 9-15 lines | Significant: auto `JOIN`s |\n| **Advanced Questions with Aggregations** | 19-32 lines | 10-13 lines | **Dramatic: biggest ROI** |\n\n**Bottom Line:** Semantic Views provide the greatest value for advanced questions with aggregations. These are exactly the queries that cause the most pain in traditional SQL development.\n\n### Key Takeaways\n\n1. **`JOIN`s become automatic.** Define relationships once in the semantic view, and Snowflake handles the joins. What was 4-5 explicit `JOIN`s becomes implicit.\n\n2. **Metrics are reusable.** Complex calculations (like `SUM(price * quantity)` or `CASE` statements) are defined once and called by name. No copy-pasting formulas.\n\n3. **Business-friendly naming.** Use semantic aliases that make sense to analysts (e.g., `total_sales` instead of `SUM(ss_sales_price * ss_quantity)`).\n\n4. **Query complexity scales.** Simple queries stay simple, but complex multi-table aggregations see the biggest improvement (from 20+ lines to 5-10 lines).\n\n5. **Same results, less code.** Semantic SQL produces identical results to traditional SQL with significantly less code to write and maintain.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You've successfully built a comprehensive Semantic View over the TPC-DS dataset and learned how to transform complex SQL queries into simple, business-friendly Semantic SQL. You've seen how Semantic Views can dramatically reduce query complexity while maintaining full analytical power.\n\nHappy querying!\n\n### What You Learned\n- How to create Semantic Views with tables, relationships, dimensions, facts, and metrics\n- The difference between Traditional SQL and Semantic SQL approaches\n- How Semantic Views abstract complex join logic and pre-define reusable metrics\n- Query patterns across different complexity levels\n\n### Related Resources\n\nDocumentation:\n- [Semantic Views Overview](https://docs.snowflake.com/en/user-guide/views-semantic/overview) - Comprehensive overview of semantic views in Snowflake\n- [CREATE SEMANTIC VIEW Reference](https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view) - SQL command reference for creating semantic views\n- [Querying Semantic Views](https://docs.snowflake.com/en/user-guide/views-semantic/querying) - How to query semantic views using SEMANTIC_VIEW construct\n- [Best practices for semantic views](https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-dev) - Best practices for working with semantic models\n- [Cortex Analyst Getting Started](https://quickstarts.snowflake.com/guide/getting_started_with_cortex_analyst/) - Step-by-step tutorial for building semantic models\n\nTPC-DS Resources:\n- [TPC-DS Benchmark Specification](https://www.tpc.org/tpcds/) - Official TPC-DS specification and documentation\n- [TPCDS NLQ Benchmark](https://github.com/NLQBenchmarks/TPCDS_Benchmark) - Open benchmark for evaluating Text-to-SQL solutions with 40 questions\n- [Snowflake Sample Data: TPC-DS](https://docs.snowflake.com/en/user-guide/sample-data-tpcds) - Information about the TPC-DS sample data available in Snowflake\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b7bb917bd0","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-b6b6d92dc6",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-1ce00f3fd4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-01-16",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-56311a3831","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. 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the TPC-DS Dataset\u003C/h2\u003E","\u003Ch2\u003ESetup\u003C/h2\u003E","\u003Ch2\u003ETraditional vs Semantic SQL\u003C/h2\u003E","\u003Ch2\u003ESimple Questions\u003C/h2\u003E","\u003Ch2\u003ESimple Questions with Aggregations\u003C/h2\u003E","\u003Ch2\u003EAdvanced Questions\u003C/h2\u003E","\u003Ch2\u003EAdvanced Questions with Aggregations\u003C/h2\u003E","\u003Ch2\u003ELessons Learned\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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