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Business Problem\u003C/h3\u003E\n","\u003Cp\u003EImagine you're a merchandising manager at ABT, a consumer electronics retailer. You need to perform a competitive pricing analysis against Best Buy to ensure your prices remain competitive in the market. You've successfully scraped product data from Best Buy's website, but now face a critical challenge: \u003Cstrong\u003Ehow do you match Best Buy's product descriptions to your own catalog when the data formats, naming conventions, and product identifiers are completely different?\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis is a classic \u003Cstrong\u003Edata harmonization\u003C/strong\u003E problem – determining which records in different datasets refer to the same real-world entity. Traditional solutions would require extensive manual mapping, brittle rule-based systems, or complex ML model training.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, you'll build a modern, AI-powered data harmonization pipeline that leverages Snowflake's native capabilities to automatically harmonize and match product data with minimal manual effort.\u003C/p\u003E\n","\u003Ch3\u003EThe Solution Architecture\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart demonstrates a three-stage approach to data harmonization:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EData Harmonization\u003C/strong\u003E: Use Snowflake Cortex AI to analyze schema differences and create unified datasets with semantic field mappings\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHybrid Entity Matching\u003C/strong\u003E: Combine vector similarity (fast, efficient) with AI_CLASSIFY (intelligent, context-aware) for optimal matching performance\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnmatched Record Reconciliation\u003C/strong\u003E: Provide an interactive interface for reviewing and correcting low-confidence matches\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Entity_Resolution.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use Snowflake Cortex AI functions for schema analysis and semantic mapping\u003C/li\u003E\u003Cli\u003EHow to build interactive Streamlit applications within Snowflake\u003C/li\u003E\u003Cli\u003EHow to implement hybrid entity matching using vector embeddings and AI classification\u003C/li\u003E\u003Cli\u003EHow to create a complete data harmonization workflow from raw data to validated matches\u003C/li\u003E\u003Cli\u003EBest practices for handling unmatched records and improving match quality\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake\u003C/a\u003E account. Sign up for a 30-day free trial account, if required.\u003C/li\u003E\u003Cli\u003EAccess to \u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-cortex/aisql\"\u003ESnowflake Cortex AI functions\u003C/a\u003E (available in most commercial regions)\u003C/li\u003E\u003Cli\u003EBasic understanding of \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks\"\u003ESnowflake Notebooks\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EBy the end of this quickstart, you will have built:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EA \u003Cstrong\u003EData Harmonization Streamlit app\u003C/strong\u003E that uses AI to map schema differences between datasets\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003EHybrid Entity Matching notebook\u003C/strong\u003E that combines vector similarity and AI classification for intelligent product matching\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003EUnmatched Records Review app\u003C/strong\u003E for human-in-the-loop validation of uncertain matches\u003C/li\u003E\u003Cli\u003EA complete, production-ready data harmonization pipeline with audit trails and quality metrics\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe solution processes 1,000+ product records from two different retailers and produces high-quality matches with confidence scores, achieving 85%+ accuracy through the hybrid approach.\u003C/p\u003E\n","\u003Ch3\u003EDownload the Source Files\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe 3 source files (2 streamlit apps and 1 notebook) can be found \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/streamlit_apps\"\u003Ehere\u003C/a\u003E and \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/notebooks\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ENote on the Datasets Used\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe datasets used in this quickstart are commonly used data harmonization test datasets. These datasets are made available by the database group of Prof. Erhard Rahm under the \u003Ca href=\"https://creativecommons.org/licenses/by/4.0/\"\u003ECreative Commons license\u003C/a\u003E. Column titles are changed at the table level from the original CSV files.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECitation:\u003C/strong\u003E Hanna Köpcke, Andreas Thor, and Erhard Rahm. 2010. Evaluation of data harmonization approaches on real-world match problems. Proc. VLDB Endow. 3, 1–2 (September 2010), 484–493. \u003Ca href=\"https://doi.org/10.14778/1920841.1920904\"\u003Ehttps://doi.org/10.14778/1920841.1920904\u003C/a\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003ESet-up\u003C/h2\u003E\n","\u003Cp\u003EIn this section, you'll prepare your Snowflake environment by creating the necessary database objects and loading the sample datasets. We'll be working with three CSV files containing product data from ABT and Best Buy, plus a ground truth mapping file for validation.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you didn't download the source files from the previous step, plese do so \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/streamlit_apps\"\u003Ehere\u003C/a\u003E and \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/notebooks\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1: Create Database and Schema\u003C/h3\u003E\n","\u003Cp\u003EFirst, create a dedicated database and schema for this project:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create the main database\nCREATE OR REPLACE DATABASE ABT_BEST_BUY;\n\n-- Create the schema to organize our tables\nCREATE OR REPLACE SCHEMA ABT_BEST_BUY.STRUCTURED;\n\n-- Set context for subsequent operations\nUSE DATABASE ABT_BEST_BUY;\nUSE SCHEMA STRUCTURED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2: Create a Warehouse\u003C/h3\u003E\n","\u003Cp\u003ECreate a warehouse sized appropriately for the AI and vector operations we'll be performing:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create your data harmonization Snowflake Warehouse (we used a 2XL in the demo video)\nCREATE OR REPLACE WAREHOUSE ENTITY_RESOLUTION_WH\nWITH\n    WAREHOUSE_SIZE = '2X-LARGE'\n    AUTO_SUSPEND = 300\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\n    COMMENT = 'Warehouse for data harmonization with Cortex AI';\n\n-- Set the warehouse as active\nUSE WAREHOUSE ENTITY_RESOLUTION_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3: Create File Format\u003C/h3\u003E\n","\u003Cp\u003ECreate a reusable file format for loading CSV files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create file format for CSV loading\nCREATE OR REPLACE FILE FORMAT ABT_BEST_BUY.STRUCTURED.CSV_FORMAT\n    TYPE = CSV\n    SKIP_HEADER = 1\n    FIELD_DELIMITER = ','\n    TRIM_SPACE = TRUE\n    FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n    REPLACE_INVALID_CHARACTERS = TRUE\n    DATE_FORMAT = AUTO\n    TIME_FORMAT = AUTO\n    TIMESTAMP_FORMAT = AUTO\n    COMMENT = 'CSV format for data harmonization data';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4: Create Internal Stage\u003C/h3\u003E\n","\u003Cp\u003ECreate an Internal Stage where we will upload our CSV files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create stage for CSV files\nCREATE OR REPLACE STAGE ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n -- Enable the directory table feature\n    DIRECTORY = ( ENABLE = TRUE )\n    COMMENT = 'Internal Stage for ABT and Best Buy product data';\n\n-- Verify stage contents (which will be empty initially)\nLIST @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5: Add Datasets to Internal Stage\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The ABT/Best Buy CSV files themselves are from common datasets used for data harmonization evalutions and can be downloaded from the Universität Leipzig website for \u003Ca href=\"https://dbs.uni-leipzig.de/research/projects/benchmark-datasets-for-entity-resolution\"\u003EBenchmark datasets for data harmonization\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/CSV_Files.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EUnzip the files. We will then upload the 3 files downloaded via Snowsight to the stage we just created:\u003C/p\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Add_Files_To_Stage.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EAfter the files are uploaded, run the LIST command again to verify they are in the stage. You should see the 3 files we just uploaded in the results returned.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\n-- Verify stage contents\nLIST @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 6: Create Tables\u003C/h3\u003E\n","\u003Cp\u003ENow create the three tables that will hold our product data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Create ABT products table\nCREATE OR REPLACE TABLE ABT_BEST_BUY.STRUCTURED.ABT (\n    SKU NUMBER(38,0),\n    PRODUCT_LABEL VARCHAR(16777216),\n    ITEM_DETAILS VARCHAR(16777216),\n    RETAIL_PRICE VARCHAR(16777216)\n);\n\n-- Create Best Buy products table\nCREATE OR REPLACE TABLE ABT_BEST_BUY.STRUCTURED.BEST_BUY (\n    PRODUCTID NUMBER(38,0),\n    NAME VARCHAR(16777216),\n    DESCRIPTION VARCHAR(16777216),\n    MANUFACTURER VARCHAR(16777216),\n    PRICE VARCHAR(16777216)\n);\n\n-- Create ground truth mapping table for validation\nCREATE OR REPLACE TABLE ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING (\n    IDABT NUMBER(38,0),\n    IDBUY NUMBER(38,0)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 7: Load Data\u003C/h3\u003E\n","\u003Cp\u003ELoad data from the staged CSV files into the tables:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Load ABT products\nCOPY INTO ABT_BEST_BUY.STRUCTURED.ABT\nFROM (\n    SELECT \n        $1::NUMBER AS SKU,\n        $2::VARCHAR AS PRODUCT_LABEL,\n        $3::VARCHAR AS ITEM_DETAILS,\n        $4::VARCHAR AS RETAIL_PRICE\n    FROM @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n)\nFILES = ('Abt.csv')\nFILE_FORMAT = (FORMAT_NAME = 'ABT_BEST_BUY.STRUCTURED.CSV_FORMAT')\nON_ERROR = CONTINUE;\n\n-- Load Best Buy products\nCOPY INTO ABT_BEST_BUY.STRUCTURED.BEST_BUY\nFROM (\n    SELECT \n        $1::NUMBER AS IDBUY,\n        $2::VARCHAR AS NAME,\n        $3::VARCHAR AS DESCRIPTION,\n        $4::VARCHAR AS MANUFACTURER,\n        $5::VARCHAR AS PRICE\n    FROM @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n)\nFILES = ('Buy.csv')\nFILE_FORMAT = (FORMAT_NAME = 'ABT_BEST_BUY.STRUCTURED.CSV_FORMAT')\nON_ERROR = CONTINUE;\n\n-- Load ground truth mapping\nCOPY INTO ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING\nFROM (\n    SELECT \n        $1::NUMBER AS IDABT,\n        $2::NUMBER AS IDBUY\n    FROM @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n)\nFILES = ('abt_buy_perfectMapping.csv')\nFILE_FORMAT = (FORMAT_NAME = 'ABT_BEST_BUY.STRUCTURED.CSV_FORMAT')\nON_ERROR = CONTINUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 8: Verify Data Load\u003C/h3\u003E\n","\u003Cp\u003EConfirm that data has been loaded successfully:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check ABT table\nSELECT 'ABT' AS TABLE_NAME, COUNT(*) AS RECORD_COUNT FROM ABT_BEST_BUY.STRUCTURED.ABT\nUNION ALL\nSELECT 'BUY' AS TABLE_NAME, COUNT(*) AS RECORD_COUNT FROM ABT_BEST_BUY.STRUCTURED.BEST_BUY\nUNION ALL\nSELECT 'ABT_BUY_PERFECTMAPPING' AS TABLE_NAME, COUNT(*) AS RECORD_COUNT FROM ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING;\n\n-- Preview sample records from each table\nSELECT 'ABT Sample' AS SOURCE, * FROM ABT_BEST_BUY.STRUCTURED.ABT LIMIT 100;\nSELECT 'BUY Sample' AS SOURCE, * FROM ABT_BEST_BUY.STRUCTURED.BEST_BUY LIMIT 100;\nSELECT 'Mapping Sample' AS SOURCE, * FROM ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EExpected Output:\u003C/h4\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/results_test.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Results:\u003C/strong\u003E You should see approximately 1,081 ABT records, 1,092 Best Buy records, and 1,097 mapping records. The ABT table contains product identifiers (SKU, PRODUCT_LABEL, & ITEM_DETAILS), while the BEST_BUY table uses different field names (PRODUCTID, NAME, & DESCRIPTION) for similar data.\u003C/p\u003E\n","\u003Cp\u003EYour Snowflake environment is now ready! In the next section, we'll use the Data Harmonization Streamlit app to create unified datasets from these disparate schemas.\u003C/p\u003E\n","\u003Ch2\u003EData Harmonization\u003C/h2\u003E\n","\u003Ch3\u003EUnderstanding the Challenge\u003C/h3\u003E\n","\u003Cp\u003ELooking at the ABT and BEST_BUY tables you just created, you'll notice they have completely different column names:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EABT\u003C/strong\u003E: \u003Ccode\u003ESKU\u003C/code\u003E, \u003Ccode\u003EPRODUCT_LABEL\u003C/code\u003E, \u003Ccode\u003EITEM_DETAILS\u003C/code\u003E, \u003Ccode\u003ERETAIL_PRICE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBEST_BUY\u003C/strong\u003E: \u003Ccode\u003EPRODUCTID\u003C/code\u003E, \u003Ccode\u003ENAME\u003C/code\u003E, \u003Ccode\u003EDESCRIPTION\u003C/code\u003E, \u003Ccode\u003EMANUFACTURER\u003C/code\u003E, \u003Ccode\u003EPRICE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhile both tables contain product information, the column names, data types, and structures differ. Before we can match products between these datasets, we need to \u003Cstrong\u003Eharmonize\u003C/strong\u003E them – creating a unified schema with consistent field mappings.\u003C/p\u003E\n","\u003Cp\u003ETraditional approaches would require manual analysis and custom ETL code. Instead, we'll use \u003Cstrong\u003ESnowflake Cortex AI\u003C/strong\u003E to automatically analyze these schemas and recommend semantic mappings.\u003C/p\u003E\n","\u003Ch3\u003EWhat the Data Harmonization App Does\u003C/h3\u003E\n","\u003Cp\u003EThe Data Harmonization Streamlit application performs the following workflow:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ETable Profiling\u003C/strong\u003E: Analyzes both tables to understand column types, cardinality, null rates, sample values, and statistical distributions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI-Powered Mapping\u003C/strong\u003E: Uses Snowflake Cortex AI (mistral-large model) to recommend semantic mappings between fields based on content analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive Review\u003C/strong\u003E: Allows you to review and adjust the AI recommendations through a user-friendly interface\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDataset Creation\u003C/strong\u003E: Generates harmonized output tables with:\n\u003Cul\u003E\u003Cli\u003EConsistent column names (e.g., \u003Ccode\u003EPRODUCT_ID\u003C/code\u003E, \u003Ccode\u003EPRODUCT_NAME\u003C/code\u003E, \u003Ccode\u003EPRODUCT_DESCRIPTION\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003ECleaned text (special characters removed)\u003C/li\u003E\u003Cli\u003EAn audit table tracking all mapping decisions\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ERunning the Data Harmonization App\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Access Streamlit in Snowflake\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003EStreamlit\u003C/strong\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EName it \u003Ccode\u003EData_Harmonization_App\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect your \u003Ccode\u003EABT_BEST_BUY\u003C/code\u003E database and \u003Ccode\u003ESTRUCTURED\u003C/code\u003E schema\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003EENTITY_RESOLUTION_WH\u003C/code\u003E warehouse\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 2: Upload the Application Code\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EDelete the example code in the \u003Ccode\u003Estreamlit_app.py\u003C/code\u003E file\u003C/li\u003E\u003Cli\u003ECopy the contents of \u003Ccode\u003EData Harmonization with Snowflake Cortex AI.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EPaste the new code from the \u003Ccode\u003EData Harmonization with Snowflake Cortex AI.py\u003C/code\u003E into this file\u003C/li\u003E\u003Cli\u003EBe sure you install the appropriate packages\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.1.png\" alt=\"\"\u003E\n<br/><br/>\n4. Ensure you have the appropriate packages installed.\n<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.2.png\" alt=\"\"\u003E\n<br/><br/>\n5. Click \u003Cstrong\u003ERun\u003C/strong\u003E to launch the application\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Configure Tables\u003C/h4\u003E\n","\u003Cp\u003EIn the app interface:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EVerify the database and schema are set to \u003Ccode\u003EABT_BEST_BUY.STRUCTURED\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EABT\u003C/strong\u003E as the \u003Cstrong\u003EReference Table\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EBEST_BUY\u003C/strong\u003E as the \u003Cstrong\u003EInput Table\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EThe app will automatically profile both tables\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.3.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Generate AI Recommendations\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun AI Analysis\u003C/strong\u003E to invoke Cortex AI\u003C/li\u003E\u003Cli\u003EThe AI will analyze the schemas and suggest mappings:\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EABT.SKU\u003C/code\u003E → \u003Ccode\u003Eproduct_id\u003C/code\u003E ← \u003Ccode\u003EBEST_BUY.PRODUCTID\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EABT.PRODUCT_LABEL\u003C/code\u003E → \u003Ccode\u003Eproduct_name\u003C/code\u003E ← \u003Ccode\u003EBEST_BUY.NAME\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EABT.ITEM_DETAILS\u003C/code\u003E → \u003Ccode\u003Eproduct_description\u003C/code\u003E ← \u003Ccode\u003EBEST_BUY.DESCRIPTION\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E(The Manufacturer field should be 'None' for ABT because it doesn't have a corresponding match.)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Cstrong\u003EKey Features and Capabilities\u003C/strong\u003E\u003C/strong\u003E:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe Cortex AI model analyzes your data to identify:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EProduct Identifiers\u003C/strong\u003E: High-cardinality numeric fields likely to be primary keys\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProduct Names\u003C/strong\u003E: Short text fields containing product titles\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProduct Descriptions\u003C/strong\u003E: Long text fields with detailed specifications\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Ch4\u003ESample Profiling Output\u003C/h4\u003E\n","\u003Cp\u003EFor each column, the app computes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERow count and null count (data quality metrics)\u003C/li\u003E\u003Cli\u003EDistinct count (cardinality for identifying keys)\u003C/li\u003E\u003Cli\u003EAverage length (helps distinguish names from descriptions)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 5: Review and Adjust Mappings\u003C/h4\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EReview and Edit Mappings\u003C/strong\u003E section:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECheck the checkboxes for fields you want to include in matching (typically all fields with valid mappings)\u003C/li\u003E\u003Cli\u003EAdjust dropdowns if the AI recommendations don't look correct (shouldn't be necessary for this analysis)\u003C/li\u003E\u003Cli\u003EThe join key suggestions can be set to 'None' (we're not performing a traditional join)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYour output should look like this:\u003C/p\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_AI_analysis.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Ch4\u003EStep 6: Create Harmonized Datasets\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EPreview the output structure to verify column names and transformations\u003C/li\u003E\u003Cli\u003ESet the target database and schema (keep defaults: \u003Ccode\u003EABT_BEST_BUY.STRUCTURED\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ECreate Datasets\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.4.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003EThe app will create three tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003EABT_HARMONIZATION_YYYY_MM_DD\u003C/code\u003E: Harmonized ABT data\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EBUY_HARMONIZATION_YYYY_MM_DD\u003C/code\u003E: Harmonized Best Buy data\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EAUDIT_HARMONIZATION_YYYY_MM_DD\u003C/code\u003E: Audit trail with mapping decisions, user info, and timestamps\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAudit Table Structure\u003C/h3\u003E\n","\u003Cp\u003EThe audit table tracks:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDate of run, user, role, and session ID\u003C/li\u003E\u003Cli\u003ESource datasets (reference and input)\u003C/li\u003E\u003Cli\u003EColumn mappings (which fields were matched)\u003C/li\u003E\u003Cli\u003EWhether each field was included in output\u003C/li\u003E\u003Cli\u003EOutput table names for traceability\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EWhy This Matters\u003C/h4\u003E\n","\u003Cp\u003EBy harmonizing the data:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EStandardized Schema\u003C/strong\u003E: Both datasets now have consistent column names\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EClean Text\u003C/strong\u003E: Special characters removed, enabling better matching\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAudit Trail\u003C/strong\u003E: Complete lineage from source to harmonized tables\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReady for Matching\u003C/strong\u003E: Data is now in the perfect format for the hybrid entity matching algorithm\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWe will use this audit table in our upcoming notebook to ensure we're matching like-for-like from a column-matching perspective.\u003C/p\u003E\n","\u003Cp\u003EIn the next section, we'll use these harmonized tables to perform intelligent entity matching using a hybrid approach that combines vector similarity with AI classification.\u003C/p\u003E\n","\u003Ch2\u003EData Harmonization - Hybrid Matching\u003C/h2\u003E\n","\u003Cp\u003ENow that we have harmonized datasets, it's time to tackle the core challenge: \u003Cstrong\u003Edetermining which ABT products correspond to which Best Buy products\u003C/strong\u003E. This is where the magic of hybrid entity matching comes in.\u003C/p\u003E\n","\u003Ch3\u003EThe Hybrid Matching Strategy\u003C/h3\u003E\n","\u003Cp\u003ETraditional entity matching approaches typically fall into two camps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ERule-based matching\u003C/strong\u003E: Fast but brittle (e.g., "if product names match exactly")\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPure ML models\u003C/strong\u003E: Accurate but require training data and can be slow\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOur hybrid approach combines the best of both worlds:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EVector Similarity (Fast Path)\u003C/strong\u003E: Use semantic embeddings to quickly identify high-confidence matches (≥80% similarity)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI_CLASSIFY (Smart Path)\u003C/strong\u003E: For ambiguous cases with multiple candidates, use AI to intelligently select the best match based on context\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis strategy is inspired by Snowflake's AI SQL best practices and provides an optimal balance of speed, accuracy, and cost.\u003C/p\u003E\n","\u003Ch3\u003EStep 1. Uploading the data harmonization Notebook\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EFrom the Snowflake navation plane click \u003Cstrong\u003ECreate\u003C/strong\u003E > \u003Cstrong\u003ENotebook\u003C/strong\u003E > \u003Cstrong\u003EImport .ipynb File\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ELocate the \u003Ccode\u003EHYBRID_ENTITY_MATCHING_WITH_AI_CLASSIFY.ipynb\u003C/code\u003E file you downloaded and upload it\u003C/li\u003E\u003Cli\u003EName it \u003Ccode\u003EHYBRID_ENTITY_MATCHING_WITH_AI_CLASSIFY\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect your \u003Ccode\u003EABT_BEST_BUY\u003C/code\u003E database and \u003Ccode\u003ESTRUCTURED\u003C/code\u003E schema\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003EENTITY_RESOLUTION_WH\u003C/code\u003E warehouse as your query warehouse\u003C/li\u003E\u003Cli\u003EAll other defaults can be kept the same\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Notebook_Import.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Ch3\u003ENotebook Overview\u003C/h3\u003E\n","\u003Cp\u003EThe notebook is organized into several key sections. Let's walk through what each major cell accomplishes.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESection 1: Configuration Setup (Python Cell)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat It Does:\u003C/strong\u003E\nThis interactive cell discovers your harmonization output tables and lets you select which entity columns to use for matching.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Operations:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQueries the current database/schema for audit tables matching the pattern \u003Ccode\u003EAUDIT_HARMONIZATION_%\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ELoads the audit table to identify which fields were mapped and matched\u003C/li\u003E\u003Cli\u003EPresents an interactive interface to select the entity column pair for matching (typically the product name/label fields)\u003C/li\u003E\u003Cli\u003EValidates that the harmonized tables exist and are accessible\u003C/li\u003E\u003Cli\u003EStores configuration in a temporary table (\u003Ccode\u003ETEMP_HYBRID_CONFIG\u003C/code\u003E) for subsequent cells\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy It Matters:\u003C/strong\u003E\nThis dynamic configuration means the notebook adapts to your specific harmonization output – no hardcoded table names or column names. You can run this workflow on any pair of harmonized datasets.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun the cell \u003Ccode\u003Er_Harmonization_Table_Configuration\u003C/code\u003E\u003C/strong\u003E - This will run an in-notebook Streamlit app.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect your database (ABT_BEST_BUY) and schema (STRUCTURED)\u003C/li\u003E\u003Cli\u003EChoose the audit table created by the harmonization app\u003C/li\u003E\u003Cli\u003ESelect the entity column pair (for this run, we will use \u003Ccode\u003EPRODUCT_LABEL ↔ NAME\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EVerify the table names and click \u003Cstrong\u003EValidate, Save Configuration, and Set Up Environment\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ERunning this cell prepares the SQL environment and extracts configuration details for the matching process, ensuring all subsequent queries reference the correct tables and columns dynamically.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Notebook1.gif\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESection 2: Vector Feature Engineering (SQL Cell)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat It Does:\u003C/strong\u003E\nCreates the foundational features for hybrid matching by generating vector embeddings for all entity names and computing initial similarity scores.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Operations:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EGenerate Embeddings\u003C/strong\u003E: Uses \u003Ccode\u003ESNOWFLAKE.CORTEX.EMBED_TEXT_1024\u003C/code\u003E with the \u003Ccode\u003Evoyage-multilingual-2\u003C/code\u003E model to create 1024-dimension vector embeddings for each product name\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECross-Join\u003C/strong\u003E: Creates all possible pairs between reference (ABT) and input (Best Buy) products\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECompute Similarity\u003C/strong\u003E: Calculates \u003Ccode\u003EVECTOR_COSINE_SIMILARITY\u003C/code\u003E between each pair's embeddings\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFilter Low Scores\u003C/strong\u003E: Drops pairs with similarity < 0.2 to reduce search space\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy It Matters:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEmbeddings capture semantics\u003C/strong\u003E: Products like "Sony 10MP Camera" and "Sony Digital Camera 10 Megapixel" will have high similarity even with different exact wording\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPre-computed similarities\u003C/strong\u003E: Storing these scores enables fast filtering in subsequent steps\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReduced search space\u003C/strong\u003E: Filtering at 0.2 threshold eliminates obviously non-matching pairs\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun the cell \u003Ccode\u003Er_Vector_Feature_Engineering\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E\nThe cell displays a summary showing:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETotal comparisons generated (e.g., 50,000-100,000 pairs)\u003C/li\u003E\u003Cli\u003ENumber of unique reference and input products\u003C/li\u003E\u003Cli\u003EAverage vector similarity score\n\u003Cul\u003E\u003Cli\u003EThis will initially be low due to the current cartesian nature of the data. We will clean this up in later steps.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EAverage comparisons per reference product\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESection 3: Hybrid Matching with AI_CLASSIFY (SQL Cell)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat It Does:\u003C/strong\u003E\nImplements the two-stage matching logic that combines vector similarity with AI classification for optimal results. \u003Cstrong\u003EAt the end of this step, we will have matched our two datasets together, keeping only what we believe to be the actual matches and discarding the rest.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThe Algorithm:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStage 1: High-Confidence Vector Matches\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor each reference product, identify the top candidates by vector similarity\u003C/li\u003E\u003Cli\u003EIf the top match is a clear similarity match accept it immediately (no AI needed)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStage 2: AI_CLASSIFY for Ambiguous Cases\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFor reference products with multiple candidates:\u003C/li\u003E\u003Cli\u003EAggregate the top 15 candidate names into an array\u003C/li\u003E\u003Cli\u003ECall \u003Ccode\u003EAI_CLASSIFY(reference_name, [candidate1, candidate2, ...])\u003C/code\u003E to intelligently select the best match\u003C/li\u003E\u003Cli\u003EAI_CLASSIFY uses context and semantic understanding beyond simple similarity\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStage 3: Confidence Scoring\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAssign confidence scores based on:\n\u003Cul\u003E\u003Cli\u003EVector similarity score\u003C/li\u003E\u003Cli\u003EWhether AI_CLASSIFY was used (slight confidence boost)\u003C/li\u003E\u003Cli\u003EMatch method (HIGH_CONFIDENCE_VECTOR, MEDIUM_CONFIDENCE_VECTOR, or AI_CLASSIFY)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy This Works:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECost Optimization\u003C/strong\u003E: AI_CLASSIFY is only called when needed (ambiguous cases)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESpeed\u003C/strong\u003E: High-confidence matches are resolved with simple vector comparison\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccuracy\u003C/strong\u003E: AI_CLASSIFY handles nuanced cases where vector similarity alone is insufficient (e.g., similar product names for different models)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun the cell \u003Ccode\u003Er_Hybrid_Matching\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Output:\u003C/strong\u003E\nThe cell creates the \u003Ccode\u003Ehybrid_final_results\u003C/code\u003E table and displays metrics:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETotal matches found\u003C/li\u003E\u003Cli\u003EAverage confidence score\u003C/li\u003E\u003Cli\u003ENumber of high-confidence vector matches\u003C/li\u003E\u003Cli\u003ENumber of AI_CLASSIFY matches\u003C/li\u003E\u003Cli\u003EAI_CLASSIFY usage percentage (typically 20-40% of records)\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESection 4: Record Management and Threshold Selection (Python Cell)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat It Does:\u003C/strong\u003E\nProvides an interactive streamlit interface to review match quality and separate records into MATCHED and UNMATCHED tables based on a confidence threshold.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy This Matters:\u003C/strong\u003E\nDifferent use cases require different confidence thresholds:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EHigh-stakes pricing decisions\u003C/strong\u003E: Use 90%+ threshold, manually review unmatched\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EExploratory analysis\u003C/strong\u003E: Use 70%+ threshold, accept more false positives\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIterative improvement\u003C/strong\u003E: Start at 80%, review unmatched, improve matching logic\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun the cell \u003Ccode\u003Er_Record_Management\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExpected Interaction:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReview the confidence distribution chart\u003C/li\u003E\u003Cli\u003EAdjust the slider to find the right threshold for your use case (we us 80% in the demo)\u003C/li\u003E\u003Cli\u003EPreview sample matches at the current threshold\u003C/li\u003E\u003Cli\u003EConfigure output table names (use defaults for this demo)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EFinalize & Create Datasets\u003C/strong\u003E to generate:\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003E[AUDIT_TABLE]_HYBRID_MATCHED\u003C/code\u003E: High-confidence matches\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003E[AUDIT_TABLE]_HYBRID_UNMATCHED\u003C/code\u003E: Records needing review\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/notebook_scroll.gif\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESection 5: Performance Evaluation (SQL Cell)\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat It Does:\u003C/strong\u003E\nEvaluates the hybrid matching approach against our golden dataset mapping table to measure accuracy and identify areas for improvement. \u003Cstrong\u003EIn many cases, you may not have a golden dataset to compare to. Here we have one which we use to test the accuracy of our methods.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Metrics Calculated:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EOverall Accuracy\u003C/strong\u003E: Percentage of matches that align with ground truth\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccuracy by Confidence Bucket\u003C/strong\u003E: How accuracy varies across confidence ranges\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccuracy by Match Method\u003C/strong\u003E: Performance of HIGH_CONFIDENCE_VECTOR vs AI_CLASSIFY vs MEDIUM_CONFIDENCE_VECTOR\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFalse Match Analysis\u003C/strong\u003E: Understanding where the algorithm makes mistakes\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy This Matters:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EValidates Approach\u003C/strong\u003E: Proves the hybrid method achieves high accuracy\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInforms Thresholds\u003C/strong\u003E: Shows you where to set confidence cutoffs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIdentifies Improvements\u003C/strong\u003E: Highlights which types of products are harder to match\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBenchmarking\u003C/strong\u003E: Provides a baseline for comparing alternative approaches\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun the cell \u003Ccode\u003Er_Performance_Evaluation\u003C/code\u003E\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAs you can see, using our methods against this test dataset we achieve a >90% accuracy when compared to our golden dataset!\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/notebook4.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n\u003Chr\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPro Tip\u003C/strong\u003E: The hybrid approach shines when you have a mix of easy and hard matching cases. Pure vector similarity handles the easy 70-80%, while AI_CLASSIFY intelligently resolves the remaining ambiguous cases.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn the next section, we'll build an interactive Streamlit app to review and correct the unmatched records, creating a human-in-the-loop validation workflow.\u003C/p\u003E\n","\u003Ch2\u003EUnmatched Record Reconciliation\u003C/h2\u003E\n","\u003Cp\u003EAfter running the hybrid matching algorithm, you'll have two sets of records:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EMatched Records\u003C/strong\u003E: High-confidence matches ready for use in analysis\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnmatched Records\u003C/strong\u003E: Lower-confidence cases that need human review\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis section addresses the unmatched records through an interactive Streamlit application that enables efficient, user-friendly review and correction.\u003C/p\u003E\n","\u003Ch3\u003EThe Problem with Unmatched Records\u003C/h3\u003E\n","\u003Cp\u003EUnmatched records typically fall into a few categories:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EClose Calls\u003C/strong\u003E: Multiple viable candidates with similar scores (e.g., different models of the same product line)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Quality Issues\u003C/strong\u003E: Missing information, typos, or incomplete descriptions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnique Products\u003C/strong\u003E: Items that genuinely don't have a match in the other dataset\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAlgorithm Limitations\u003C/strong\u003E: Cases where both vector similarity and AI_CLASSIFY struggled\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ERather than accepting these as lost opportunities, the Unmatched Records Reviewer provides a structured workflow to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReview each unmatched record systematically\u003C/li\u003E\u003Cli\u003EView alternative candidate matches with their similarity scores\u003C/li\u003E\u003Cli\u003EMake informed manual selections or confirm "no match found"\u003C/li\u003E\u003Cli\u003ETrack all decisions with full audit trails\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat the Unmatched Records App Does\u003C/h3\u003E\n","\u003Cp\u003EThe application implements a comprehensive review workflow:\u003C/p\u003E\n","\u003Ch4\u003E1. Intelligent Data Loading\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EAutomatically discovers unmatched table from the audit trail\u003C/li\u003E\u003Cli\u003EIdentifies the corresponding matched table for approved records\u003C/li\u003E\u003Cli\u003ELoads up to 20 candidate matches per record from the original \u003Ccode\u003Ehybrid_features\u003C/code\u003E table\u003C/li\u003E\u003Cli\u003EPaginates records for manageable review sessions (10 records per page)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E2. Interactive Review Interface\u003C/h4\u003E\n","\u003Cp\u003EFor each unmatched record, the reviewer displays:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EReference Entity Name\u003C/strong\u003E: The ABT product you're trying to match\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETop Candidate Matches\u003C/strong\u003E: Dropdown showing up to 20 Best Buy candidates, ranked by vector similarity\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESimilarity Scores\u003C/strong\u003E: Real-time display of vector similarity for the selected candidate\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMatch Approval Checkbox\u003C/strong\u003E: Confirm when you've selected the correct match\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E3. Dynamic Search and Filtering\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EText Search\u003C/strong\u003E: Filter records by reference entity name to quickly find specific products\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPagination\u003C/strong\u003E: Navigate through pages of unmatched records\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EJump Controls\u003C/strong\u003E: Skip to first or last page instantly\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E4. Batch Processing\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPage-by-Page Approval\u003C/strong\u003E: Review and approve matches on each page\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECross-Page Memory\u003C/strong\u003E: Selections are preserved as you navigate between pages\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBulk Submission\u003C/strong\u003E: Submit all approved matches from all pages at once\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERunning the Unmatched Records App\u003C/h3\u003E\n","\u003Ch4\u003EStep 1: Create the Streamlit App\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EProjects\u003C/strong\u003E > \u003Cstrong\u003EStreamlit\u003C/strong\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EName it \u003Ccode\u003Edata harmonization - Unmatched Records\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ESelect your \u003Ccode\u003EABT_BEST_BUY\u003C/code\u003E database and \u003Ccode\u003ESTRUCTURED\u003C/code\u003E schema\u003C/li\u003E\u003Cli\u003ESelect the \u003Ccode\u003EENTITY_RESOLUTION_WH\u003C/code\u003E warehouse\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 2: Upload the Application Code\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EDelete the example code in the \u003Ccode\u003Estreamlit_app.py\u003C/code\u003E file\u003C/li\u003E\u003Cli\u003ECopy the contents of \u003Ccode\u003Edata harmonization - Unmatched Records.py\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EPaste the new code from the \u003Ccode\u003Edata harmonization - Unmatched Records.py\u003C/code\u003E into this file\u003C/li\u003E\u003Cli\u003EBe sure you install the appropriate packages\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched1.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Select Unmatched Table\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EThe app automatically queries \u003Ccode\u003EAUDIT_HARMONIZATION_HYBRID_AUDIT\u003C/code\u003E to find unmatched/matched table pairs\u003C/li\u003E\u003Cli\u003EFrom the dropdown, select your unmatched table (e.g., \u003Ccode\u003EAUDIT_HARMONIZATION_2025_10_04_HYBRID_UNMATCHED\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EProcess\u003C/strong\u003E to load the records\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The app automatically identifies the corresponding matched table from the audit table, ensuring records are moved to the correct destination.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 3: Review Records\u003C/h4\u003E\n","\u003Cp\u003EFor each record on the page:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReference Record Display:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched2.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECandidate Selection:\u003C/strong\u003E\nThe dropdown shows candidates with similarity scores:\u003C/p\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched3.png\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EActions:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect the best match from the dropdown\u003C/li\u003E\u003Cli\u003EReview the vector similarity score (displayed dynamically)\u003C/li\u003E\u003Cli\u003ECheck \u003Cstrong\u003E✅ Match Approved\u003C/strong\u003E if you're confident in the selection\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELeave unchecked if no good match exists (record remains unmatched) - Only mannually selected records will be processed to our matched table\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EStep 4: Navigate and Continue Reviewing\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EUse \u003Cstrong\u003E← Previous\u003C/strong\u003E and \u003Cstrong\u003ENext →\u003C/strong\u003E buttons to move between pages\u003C/li\u003E\u003Cli\u003EUse \u003Cstrong\u003E⏪ Jump to First Page\u003C/strong\u003E or \u003Cstrong\u003E⏩ Jump to Last Page\u003C/strong\u003E for quick navigation\u003C/li\u003E\u003Cli\u003EUse the search box to filter records by specific text\u003C/li\u003E\u003Cli\u003EThe page indicator shows your progress (e.g., "Page 5 of 25")\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWorkflow Tip\u003C/strong\u003E: Focus on records with high similarity scores (>0.75) first, as these are more likely to be correct matches. Records with very low scores (<0.50) may genuinely have no match in the dataset.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 5: Submit Reviews\u003C/h4\u003E\n","\u003Cp\u003EOnce you reach the last page:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReview the \u003Cstrong\u003EReview Summary\u003C/strong\u003E showing:\n\u003Cul\u003E\u003Cli\u003ERecords Approved to Move\u003C/li\u003E\u003Cli\u003ERecords Not Approved\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003ESubmit All Reviews\u003C/strong\u003E to process all approved matches across all pages\u003C/li\u003E\u003Cli\u003EThe app will:\n\u003Cul\u003E\u003Cli\u003EUpdate the \u003Ccode\u003EFINAL_MATCH\u003C/code\u003E column for approved records\u003C/li\u003E\u003Cli\u003EMove approved records from unmatched to matched table\u003C/li\u003E\u003Cli\u003EDelete processed records from unmatched table\u003C/li\u003E\u003Cli\u003EDisplay a detailed summary of changes\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 6: Review Completion Summary\u003C/h4\u003E\n","\u003Cp\u003EAfter submission, you'll see a summary of the records you sent to the matched table.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E📑 Continue with Analysis\u003C/strong\u003E to review remaining records or refresh the view.\u003C/p\u003E\n","\u003Cp\u003E<br/><br/>\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched2.gif\" alt=\"\"\u003E\n<br/><br/>\u003C/p\u003E\n","\u003Ch3\u003EBest Practices for Record Review\u003C/h3\u003E\n","\u003Ch4\u003E1. Prioritize by Confidence\u003C/h4\u003E\n","\u003Cp\u003EStart with records that have similarity scores just below your threshold (e.g., 0.75-0.80), as these are most likely to be correctable with manual review.\u003C/p\u003E\n","\u003Ch4\u003E2. Look for Patterns\u003C/h4\u003E\n","\u003Cp\u003EIf you notice multiple records failing for the same reason (e.g., missing manufacturer information), consider updating your harmonization mapping to include additional fields.\u003C/p\u003E\n","\u003Ch4\u003E3. Use Domain Knowledge\u003C/h4\u003E\n","\u003Cp\u003EProduct expertise is invaluable here. If you know ABT's catalog, you can quickly identify:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProducts that should match but have different model numbers\u003C/li\u003E\u003Cli\u003EProducts that are actually different items despite similar names\u003C/li\u003E\u003Cli\u003EProducts that genuinely don't exist in Best Buy's catalog\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E4. Don't Force Matches\u003C/h4\u003E\n","\u003Cp\u003EIf none of the candidates are correct, leave the record unchecked. It's better to have an unmatched record than an incorrect match that pollutes your analysis.\u003C/p\u003E\n","\u003Ch4\u003E5. Iterative Review Sessions\u003C/h4\u003E\n","\u003Cp\u003EYou don't have to review all records in one sitting. The app preserves state across sessions (records remain in the unmatched table until explicitly moved).\u003C/p\u003E\n","\u003Ch3\u003EThe Value of Human-in-the-Loop\u003C/h3\u003E\n","\u003Cp\u003EThis review workflow exemplifies the \u003Cstrong\u003Ehuman-in-the-loop\u003C/strong\u003E approach to AI:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EAI Does the Heavy Lifting\u003C/strong\u003E: The hybrid matching algorithm handles 75-85% of records automatically\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHumans Handle Edge Cases\u003C/strong\u003E: Reviewers focus only on ambiguous cases that need domain expertise\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EContinuous Improvement\u003C/strong\u003E: Insights from manual review can inform future matching logic improvements\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EQuality Assurance\u003C/strong\u003E: Human validation provides confidence in match quality for downstream analytics\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReal-World Impact\u003C/strong\u003E: In production environments, this workflow reduces data harmonization time from weeks of manual work to hours of focused review, while maintaining high match quality.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EBy the end of this section, you'll have:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E✅ Reviewed all unmatched records systematically\u003C/li\u003E\u003Cli\u003E✅ Made informed decisions about match selections\u003C/li\u003E\u003Cli\u003E✅ Moved high-quality matches to the matched table\u003C/li\u003E\u003Cli\u003E✅ Identified records that genuinely have no match\u003C/li\u003E\u003Cli\u003E✅ Maintained complete audit trails for all decisions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou now have a complete, production-ready data harmonization pipeline with matched records ready for competitive pricing analysis!\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully built an end-to-end data harmonization solution using Snowflake's native AI capabilities, combining automated matching with human-in-the-loop validation.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Accomplished\u003C/h3\u003E\n","\u003Cp\u003EThroughout this quickstart, you:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E✅ \u003Cstrong\u003EPrepared Data\u003C/strong\u003E: Loaded product catalogs from two different retailers with different schemas\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EHarmonized Schemas\u003C/strong\u003E: Used Snowflake Cortex AI to automatically map semantic field differences and create unified datasets\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EImplemented Hybrid Matching\u003C/strong\u003E: Combined vector similarity (fast) with AI_CLASSIFY (intelligent) for optimal matching performance\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EValidated Quality\u003C/strong\u003E: Measured accuracy against ground truth data and achieved 85%+ match quality\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EReviewed Edge Cases\u003C/strong\u003E: Used an interactive Streamlit app to manually validate uncertain matches\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EBuilt Production Pipeline\u003C/strong\u003E: Created a complete workflow with audit trails, confidence scoring, and quality metrics\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EKey Takeaways\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBusiness Value:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReduced data harmonization from weeks of manual work to hours of automated processing\u003C/li\u003E\u003Cli\u003EAchieved high match quality (85%+) without training custom ML models\u003C/li\u003E\u003Cli\u003ECreated a repeatable, scalable process for ongoing data integration needs\u003C/li\u003E\u003Cli\u003EEnabled competitive pricing analysis that was previously infeasible\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETechnical Innovation:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex AI for Schema Analysis\u003C/strong\u003E: Automatically understand and map disparate data structures\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVector Embeddings\u003C/strong\u003E: Capture semantic similarity beyond exact text matching\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EHybrid Approach\u003C/strong\u003E: Optimize for both speed (vector similarity) and accuracy (AI_CLASSIFY)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStreamlit in Snowflake\u003C/strong\u003E: Build rich, interactive applications without external infrastructure\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest Practices:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAlways profile and harmonize data before attempting matching\u003C/li\u003E\u003Cli\u003EUse confidence thresholds to separate high-quality matches from cases needing review\u003C/li\u003E\u003Cli\u003EMaintain audit trails for all decisions and transformations\u003C/li\u003E\u003Cli\u003EValidate against ground truth when available to measure and improve accuracy\u003C/li\u003E\u003Cli\u003EImplement human-in-the-loop for edge cases that require domain expertise\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDocumentation:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview\"\u003ESnowflake Cortex AI\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/vector-embeddings\"\u003ESnowflake Cortex Vector Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/ml-functions/classification\"\u003ESnowflake Cortex AI_CLASSIFY\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit\"\u003EStreamlit in Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight-notebooks\"\u003ESnowflake Notebooks\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Harmonize data from multiple sources in Snowflake Cortex AI and Streamlit for consistent analytics, unified schemas, clean models, and integration.","title":"End-to-End Data Harmonization with Snowflake Cortex AI",":type":"snowflake-site/components/contentfragment","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview \n\n### The Business Problem\n\nImagine you're a merchandising manager at ABT, a consumer electronics retailer. You need to perform a competitive pricing analysis against Best Buy to ensure your prices remain competitive in the market. You've successfully scraped product data from Best Buy's website, but now face a critical challenge: **how do you match Best Buy's product descriptions to your own catalog when the data formats, naming conventions, and product identifiers are completely different?**\n\nThis is a classic **data harmonization** problem – determining which records in different datasets refer to the same real-world entity. Traditional solutions would require extensive manual mapping, brittle rule-based systems, or complex ML model training. \n\nIn this quickstart, you'll build a modern, AI-powered data harmonization pipeline that leverages Snowflake's native capabilities to automatically harmonize and match product data with minimal manual effort.\n\n### The Solution Architecture\n\nThis quickstart demonstrates a three-stage approach to data harmonization:\n\n1. **Data Harmonization**: Use Snowflake Cortex AI to analyze schema differences and create unified datasets with semantic field mappings\n2. **Hybrid Entity Matching**: Combine vector similarity (fast, efficient) with AI_CLASSIFY (intelligent, context-aware) for optimal matching performance\n3. **Unmatched Record Reconciliation**: Provide an interactive interface for reviewing and correcting low-confidence matches\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Entity_Resolution.png)\n\n### What You'll Learn\n\n- How to use Snowflake Cortex AI functions for schema analysis and semantic mapping\n- How to build interactive Streamlit applications within Snowflake\n- How to implement hybrid entity matching using vector embeddings and AI classification\n- How to create a complete data harmonization workflow from raw data to validated matches\n- Best practices for handling unmatched records and improving match quality\n\n### What You'll Need\n\n- A [Snowflake](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) account. Sign up for a 30-day free trial account, if required.\n- Access to [Snowflake Cortex AI functions](https://docs.snowflake.com/user-guide/snowflake-cortex/aisql) (available in most commercial regions)\n- Basic understanding of [Snowflake Notebooks](https://docs.snowflake.com/en/user-guide/ui-snowsight/notebooks) and [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n\n\n### What You'll Build\n\nBy the end of this quickstart, you will have built:\n\n1. A **Data Harmonization Streamlit app** that uses AI to map schema differences between datasets\n2. A **Hybrid Entity Matching notebook** that combines vector similarity and AI classification for intelligent product matching\n3. An **Unmatched Records Review app** for human-in-the-loop validation of uncertain matches\n4. A complete, production-ready data harmonization pipeline with audit trails and quality metrics\n\nThe solution processes 1,000+ product records from two different retailers and produces high-quality matches with confidence scores, achieving 85%+ accuracy through the hybrid approach.\n\n### Download the Source Files\n\n\u003E The 3 source files (2 streamlit apps and 1 notebook) can be found [here](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/streamlit_apps) and [here](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/notebooks).\n\n### Note on the Datasets Used\n\n\u003E The datasets used in this quickstart are commonly used data harmonization test datasets. These datasets are made available by the database group of Prof. Erhard Rahm under the [Creative Commons license](https://creativecommons.org/licenses/by/4.0/). Column titles are changed at the table level from the original CSV files.\n\n\u003E **Citation:** Hanna Köpcke, Andreas Thor, and Erhard Rahm. 2010. Evaluation of data harmonization approaches on real-world match problems. Proc. VLDB Endow. 3, 1–2 (September 2010), 484–493. [https://doi.org/10.14778/1920841.1920904](https://doi.org/10.14778/1920841.1920904)\n\n\n## Set-up\n\nIn this section, you'll prepare your Snowflake environment by creating the necessary database objects and loading the sample datasets. We'll be working with three CSV files containing product data from ABT and Best Buy, plus a ground truth mapping file for validation.\n\n\u003E If you didn't download the source files from the previous step, plese do so [here](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/streamlit_apps) and [here](https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/data-harmonization/assets/notebooks).\n\n### Step 1: Create Database and Schema\n\nFirst, create a dedicated database and schema for this project:\n\n```sql\n-- Create the main database\nCREATE OR REPLACE DATABASE ABT_BEST_BUY;\n\n-- Create the schema to organize our tables\nCREATE OR REPLACE SCHEMA ABT_BEST_BUY.STRUCTURED;\n\n-- Set context for subsequent operations\nUSE DATABASE ABT_BEST_BUY;\nUSE SCHEMA STRUCTURED;\n```\n\n### Step 2: Create a Warehouse\n\nCreate a warehouse sized appropriately for the AI and vector operations we'll be performing:\n\n```sql\n-- Create your data harmonization Snowflake Warehouse (we used a 2XL in the demo video)\nCREATE OR REPLACE WAREHOUSE ENTITY_RESOLUTION_WH\nWITH\n    WAREHOUSE_SIZE = '2X-LARGE'\n    AUTO_SUSPEND = 300\n    AUTO_RESUME = TRUE\n    INITIALLY_SUSPENDED = TRUE\n    COMMENT = 'Warehouse for data harmonization with Cortex AI';\n\n-- Set the warehouse as active\nUSE WAREHOUSE ENTITY_RESOLUTION_WH;\n```\n\n### Step 3: Create File Format\n\nCreate a reusable file format for loading CSV files:\n\n```sql\n-- Create file format for CSV loading\nCREATE OR REPLACE FILE FORMAT ABT_BEST_BUY.STRUCTURED.CSV_FORMAT\n    TYPE = CSV\n    SKIP_HEADER = 1\n    FIELD_DELIMITER = ','\n    TRIM_SPACE = TRUE\n    FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\n    REPLACE_INVALID_CHARACTERS = TRUE\n    DATE_FORMAT = AUTO\n    TIME_FORMAT = AUTO\n    TIMESTAMP_FORMAT = AUTO\n    COMMENT = 'CSV format for data harmonization data';\n```\n\n### Step 4: Create Internal Stage\n\nCreate an Internal Stage where we will upload our CSV files:\n\n```sql\n-- Create stage for CSV files\nCREATE OR REPLACE STAGE ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n -- Enable the directory table feature\n    DIRECTORY = ( ENABLE = TRUE )\n    COMMENT = 'Internal Stage for ABT and Best Buy product data';\n\n-- Verify stage contents (which will be empty initially)\nLIST @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE;\n```\n\n### Step 5: Add Datasets to Internal Stage\n\n\u003E **Note:** The ABT/Best Buy CSV files themselves are from common datasets used for data harmonization evalutions and can be downloaded from the Universität Leipzig website for [Benchmark datasets for data harmonization](https://dbs.uni-leipzig.de/research/projects/benchmark-datasets-for-entity-resolution).\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/CSV_Files.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nUnzip the files. We will then upload the 3 files downloaded via Snowsight to the stage we just created:\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Add_Files_To_Stage.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nAfter the files are uploaded, run the LIST command again to verify they are in the stage. You should see the 3 files we just uploaded in the results returned.\n\n\n```sql\n\n-- Verify stage contents\nLIST @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE;\n```\n\n\n### Step 6: Create Tables\n\nNow create the three tables that will hold our product data:\n\n```sql\n-- Create ABT products table\nCREATE OR REPLACE TABLE ABT_BEST_BUY.STRUCTURED.ABT (\n    SKU NUMBER(38,0),\n    PRODUCT_LABEL VARCHAR(16777216),\n    ITEM_DETAILS VARCHAR(16777216),\n    RETAIL_PRICE VARCHAR(16777216)\n);\n\n-- Create Best Buy products table\nCREATE OR REPLACE TABLE ABT_BEST_BUY.STRUCTURED.BEST_BUY (\n    PRODUCTID NUMBER(38,0),\n    NAME VARCHAR(16777216),\n    DESCRIPTION VARCHAR(16777216),\n    MANUFACTURER VARCHAR(16777216),\n    PRICE VARCHAR(16777216)\n);\n\n-- Create ground truth mapping table for validation\nCREATE OR REPLACE TABLE ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING (\n    IDABT NUMBER(38,0),\n    IDBUY NUMBER(38,0)\n);\n```\n\n### Step 7: Load Data\n\nLoad data from the staged CSV files into the tables:\n\n```sql\n-- Load ABT products\nCOPY INTO ABT_BEST_BUY.STRUCTURED.ABT\nFROM (\n    SELECT \n        $1::NUMBER AS SKU,\n        $2::VARCHAR AS PRODUCT_LABEL,\n        $3::VARCHAR AS ITEM_DETAILS,\n        $4::VARCHAR AS RETAIL_PRICE\n    FROM @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n)\nFILES = ('Abt.csv')\nFILE_FORMAT = (FORMAT_NAME = 'ABT_BEST_BUY.STRUCTURED.CSV_FORMAT')\nON_ERROR = CONTINUE;\n\n-- Load Best Buy products\nCOPY INTO ABT_BEST_BUY.STRUCTURED.BEST_BUY\nFROM (\n    SELECT \n        $1::NUMBER AS IDBUY,\n        $2::VARCHAR AS NAME,\n        $3::VARCHAR AS DESCRIPTION,\n        $4::VARCHAR AS MANUFACTURER,\n        $5::VARCHAR AS PRICE\n    FROM @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n)\nFILES = ('Buy.csv')\nFILE_FORMAT = (FORMAT_NAME = 'ABT_BEST_BUY.STRUCTURED.CSV_FORMAT')\nON_ERROR = CONTINUE;\n\n-- Load ground truth mapping\nCOPY INTO ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING\nFROM (\n    SELECT \n        $1::NUMBER AS IDABT,\n        $2::NUMBER AS IDBUY\n    FROM @ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_DATA_STAGE\n)\nFILES = ('abt_buy_perfectMapping.csv')\nFILE_FORMAT = (FORMAT_NAME = 'ABT_BEST_BUY.STRUCTURED.CSV_FORMAT')\nON_ERROR = CONTINUE;\n```\n\n### Step 8: Verify Data Load\n\nConfirm that data has been loaded successfully:\n\n```sql\n-- Check ABT table\nSELECT 'ABT' AS TABLE_NAME, COUNT(*) AS RECORD_COUNT FROM ABT_BEST_BUY.STRUCTURED.ABT\nUNION ALL\nSELECT 'BUY' AS TABLE_NAME, COUNT(*) AS RECORD_COUNT FROM ABT_BEST_BUY.STRUCTURED.BEST_BUY\nUNION ALL\nSELECT 'ABT_BUY_PERFECTMAPPING' AS TABLE_NAME, COUNT(*) AS RECORD_COUNT FROM ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING;\n\n-- Preview sample records from each table\nSELECT 'ABT Sample' AS SOURCE, * FROM ABT_BEST_BUY.STRUCTURED.ABT LIMIT 100;\nSELECT 'BUY Sample' AS SOURCE, * FROM ABT_BEST_BUY.STRUCTURED.BEST_BUY LIMIT 100;\nSELECT 'Mapping Sample' AS SOURCE, * FROM ABT_BEST_BUY.STRUCTURED.ABT_BEST_BUY_PERFECT_MAPPING LIMIT 100;\n```\n\n#### Expected Output:\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/results_test.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n**Expected Results:** You should see approximately 1,081 ABT records, 1,092 Best Buy records, and 1,097 mapping records. The ABT table contains product identifiers (SKU, PRODUCT_LABEL, & ITEM_DETAILS), while the BEST_BUY table uses different field names (PRODUCTID, NAME, & DESCRIPTION) for similar data.\n\nYour Snowflake environment is now ready! In the next section, we'll use the Data Harmonization Streamlit app to create unified datasets from these disparate schemas.\n\n## Data Harmonization\n\n### Understanding the Challenge\n\nLooking at the ABT and BEST_BUY tables you just created, you'll notice they have completely different column names:\n\n- **ABT**: `SKU`, `PRODUCT_LABEL`, `ITEM_DETAILS`, `RETAIL_PRICE`\n- **BEST_BUY**: `PRODUCTID`, `NAME`, `DESCRIPTION`, `MANUFACTURER`, `PRICE`\n\nWhile both tables contain product information, the column names, data types, and structures differ. Before we can match products between these datasets, we need to **harmonize** them – creating a unified schema with consistent field mappings.\n\nTraditional approaches would require manual analysis and custom ETL code. Instead, we'll use **Snowflake Cortex AI** to automatically analyze these schemas and recommend semantic mappings.\n\n### What the Data Harmonization App Does\n\nThe Data Harmonization Streamlit application performs the following workflow:\n\n1. **Table Profiling**: Analyzes both tables to understand column types, cardinality, null rates, sample values, and statistical distributions\n2. **AI-Powered Mapping**: Uses Snowflake Cortex AI (mistral-large model) to recommend semantic mappings between fields based on content analysis\n3. **Interactive Review**: Allows you to review and adjust the AI recommendations through a user-friendly interface\n4. **Dataset Creation**: Generates harmonized output tables with:\n   - Consistent column names (e.g., `PRODUCT_ID`, `PRODUCT_NAME`, `PRODUCT_DESCRIPTION`)\n   - Cleaned text (special characters removed)\n   - An audit table tracking all mapping decisions\n\n\n### Running the Data Harmonization App\n\n#### Step 1: Access Streamlit in Snowflake\n\n1. Navigate to **Projects** \u003E **Streamlit** in Snowsight\n2. Click **+ Streamlit App**\n3. Name it `Data_Harmonization_App`\n4. Select your `ABT_BEST_BUY` database and `STRUCTURED` schema\n5. Select the `ENTITY_RESOLUTION_WH` warehouse\n\n#### Step 2: Upload the Application Code\n\n1. Delete the example code in the `streamlit_app.py` file\n2. Copy the contents of `Data Harmonization with Snowflake Cortex AI.py`\n3. Paste the new code from the `Data Harmonization with Snowflake Cortex AI.py` into this file\n4. Be sure you install the appropriate packages\n5. Click **Run**\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.1.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n4. Ensure you have the appropriate packages installed.\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.2.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n5. Click **Run** to launch the application\n\n#### Step 3: Configure Tables\n\nIn the app interface:\n1. Verify the database and schema are set to `ABT_BEST_BUY.STRUCTURED`\n2. Select **ABT** as the **Reference Table**\n3. Select **BEST_BUY** as the **Input Table**\n4. The app will automatically profile both tables\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.3.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n#### Step 4: Generate AI Recommendations\n\n1. Click **Run AI Analysis** to invoke Cortex AI\n2. The AI will analyze the schemas and suggest mappings:\n   - `ABT.SKU` → `product_id` ← `BEST_BUY.PRODUCTID`\n   - `ABT.PRODUCT_LABEL` → `product_name` ← `BEST_BUY.NAME`\n   - `ABT.ITEM_DETAILS` → `product_description` ← `BEST_BUY.DESCRIPTION`\n   - (The Manufacturer field should be 'None' for ABT because it doesn't have a corresponding match.)\n\n****Key Features and Capabilities****:\n\n\u003EThe Cortex AI model analyzes your data to identify:\n\u003E- **Product Identifiers**: High-cardinality numeric fields likely to be primary keys\n\u003E- **Product Names**: Short text fields containing product titles\n\u003E- **Product Descriptions**: Long text fields with detailed specifications\n\u003E\n---\n\n#### Sample Profiling Output\n\nFor each column, the app computes:\n- Row count and null count (data quality metrics)\n- Distinct count (cardinality for identifying keys)\n- Average length (helps distinguish names from descriptions)\n\n\n\n#### Step 5: Review and Adjust Mappings\n\nIn the **Review and Edit Mappings** section:\n- Check the checkboxes for fields you want to include in matching (typically all fields with valid mappings)\n- Adjust dropdowns if the AI recommendations don't look correct (shouldn't be necessary for this analysis)\n- The join key suggestions can be set to 'None' (we're not performing a traditional join)\n\nYour output should look like this:\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_AI_analysis.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\n#### Step 6: Create Harmonized Datasets\n\n1. Preview the output structure to verify column names and transformations\n2. Set the target database and schema (keep defaults: `ABT_BEST_BUY.STRUCTURED`)\n3. Click **Create Datasets**\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/data_harm_streamlit_setup.4.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\nThe app will create three tables:\n- `ABT_HARMONIZATION_YYYY_MM_DD`: Harmonized ABT data\n- `BUY_HARMONIZATION_YYYY_MM_DD`: Harmonized Best Buy data\n- `AUDIT_HARMONIZATION_YYYY_MM_DD`: Audit trail with mapping decisions, user info, and timestamps\n\n\n### Audit Table Structure\n\nThe audit table tracks:\n- Date of run, user, role, and session ID\n- Source datasets (reference and input)\n- Column mappings (which fields were matched)\n- Whether each field was included in output\n- Output table names for traceability\n\n#### Why This Matters\n\nBy harmonizing the data:\n1. **Standardized Schema**: Both datasets now have consistent column names\n2. **Clean Text**: Special characters removed, enabling better matching\n3. **Audit Trail**: Complete lineage from source to harmonized tables\n4. **Ready for Matching**: Data is now in the perfect format for the hybrid entity matching algorithm\n\nWe will use this audit table in our upcoming notebook to ensure we're matching like-for-like from a column-matching perspective.\n\nIn the next section, we'll use these harmonized tables to perform intelligent entity matching using a hybrid approach that combines vector similarity with AI classification.\n\n## Data Harmonization - Hybrid Matching\n\nNow that we have harmonized datasets, it's time to tackle the core challenge: **determining which ABT products correspond to which Best Buy products**. This is where the magic of hybrid entity matching comes in.\n\n### The Hybrid Matching Strategy\n\nTraditional entity matching approaches typically fall into two camps:\n\n1. **Rule-based matching**: Fast but brittle (e.g., \"if product names match exactly\")\n2. **Pure ML models**: Accurate but require training data and can be slow\n\nOur hybrid approach combines the best of both worlds:\n\n- **Vector Similarity (Fast Path)**: Use semantic embeddings to quickly identify high-confidence matches (≥80% similarity)\n- **AI_CLASSIFY (Smart Path)**: For ambiguous cases with multiple candidates, use AI to intelligently select the best match based on context\n\nThis strategy is inspired by Snowflake's AI SQL best practices and provides an optimal balance of speed, accuracy, and cost.\n\n### Step 1. Uploading the data harmonization Notebook\n\n1. From the Snowflake navation plane click **Create** \u003E **Notebook** \u003E **Import .ipynb File** \n2. Locate the `HYBRID_ENTITY_MATCHING_WITH_AI_CLASSIFY.ipynb` file you downloaded and upload it\n3. Name it `HYBRID_ENTITY_MATCHING_WITH_AI_CLASSIFY`\n4. Select your `ABT_BEST_BUY` database and `STRUCTURED` schema\n5. Select the `ENTITY_RESOLUTION_WH` warehouse as your query warehouse\n6. All other defaults can be kept the same\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Notebook_Import.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n### Notebook Overview\n\nThe notebook is organized into several key sections. Let's walk through what each major cell accomplishes.\n\n---\n\n### Section 1: Configuration Setup (Python Cell)\n\n**What It Does:**\nThis interactive cell discovers your harmonization output tables and lets you select which entity columns to use for matching.\n\n**Key Operations:**\n- Queries the current database/schema for audit tables matching the pattern `AUDIT_HARMONIZATION_%`\n- Loads the audit table to identify which fields were mapped and matched\n- Presents an interactive interface to select the entity column pair for matching (typically the product name/label fields)\n- Validates that the harmonized tables exist and are accessible\n- Stores configuration in a temporary table (`TEMP_HYBRID_CONFIG`) for subsequent cells\n\n**Why It Matters:**\nThis dynamic configuration means the notebook adapts to your specific harmonization output – no hardcoded table names or column names. You can run this workflow on any pair of harmonized datasets.\n\n**Run the cell `r_Harmonization_Table_Configuration`** - This will run an in-notebook Streamlit app. \n\n    \n1. Select your database (ABT_BEST_BUY) and schema (STRUCTURED)\n2. Choose the audit table created by the harmonization app\n3. Select the entity column pair (for this run, we will use `PRODUCT_LABEL ↔ NAME`)\n4. Verify the table names and click **Validate, Save Configuration, and Set Up Environment**\n\nRunning this cell prepares the SQL environment and extracts configuration details for the matching process, ensuring all subsequent queries reference the correct tables and columns dynamically.\n\n---\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Notebook1.gif)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n---\n\n### Section 2: Vector Feature Engineering (SQL Cell)\n\n**What It Does:**\nCreates the foundational features for hybrid matching by generating vector embeddings for all entity names and computing initial similarity scores.\n\n**Key Operations:**\n\n1. **Generate Embeddings**: Uses `SNOWFLAKE.CORTEX.EMBED_TEXT_1024` with the `voyage-multilingual-2` model to create 1024-dimension vector embeddings for each product name\n2. **Cross-Join**: Creates all possible pairs between reference (ABT) and input (Best Buy) products\n3. **Compute Similarity**: Calculates `VECTOR_COSINE_SIMILARITY` between each pair's embeddings\n4. **Filter Low Scores**: Drops pairs with similarity \u003C 0.2 to reduce search space\n\n\n**Why It Matters:**\n- **Embeddings capture semantics**: Products like \"Sony 10MP Camera\" and \"Sony Digital Camera 10 Megapixel\" will have high similarity even with different exact wording\n- **Pre-computed similarities**: Storing these scores enables fast filtering in subsequent steps\n- **Reduced search space**: Filtering at 0.2 threshold eliminates obviously non-matching pairs\n\n**Run the cell `r_Vector_Feature_Engineering`**\n\n**Expected Output:**\nThe cell displays a summary showing:\n- Total comparisons generated (e.g., 50,000-100,000 pairs)\n- Number of unique reference and input products\n- Average vector similarity score\n    - This will initially be low due to the current cartesian nature of the data. We will clean this up in later steps.\n- Average comparisons per reference product\n\n---\n\n### Section 3: Hybrid Matching with AI_CLASSIFY (SQL Cell)\n\n**What It Does:**\nImplements the two-stage matching logic that combines vector similarity with AI classification for optimal results. **At the end of this step, we will have matched our two datasets together, keeping only what we believe to be the actual matches and discarding the rest.**\n\n**The Algorithm:**\n\n**Stage 1: High-Confidence Vector Matches**\n- For each reference product, identify the top candidates by vector similarity\n- If the top match is a clear similarity match accept it immediately (no AI needed)\n\n**Stage 2: AI_CLASSIFY for Ambiguous Cases**\n- For reference products with multiple candidates:\n- Aggregate the top 15 candidate names into an array\n- Call `AI_CLASSIFY(reference_name, [candidate1, candidate2, ...])` to intelligently select the best match\n- AI_CLASSIFY uses context and semantic understanding beyond simple similarity\n\n**Stage 3: Confidence Scoring**\n- Assign confidence scores based on:\n  - Vector similarity score\n  - Whether AI_CLASSIFY was used (slight confidence boost)\n  - Match method (HIGH_CONFIDENCE_VECTOR, MEDIUM_CONFIDENCE_VECTOR, or AI_CLASSIFY)\n\n\n**Why This Works:**\n- **Cost Optimization**: AI_CLASSIFY is only called when needed (ambiguous cases)\n- **Speed**: High-confidence matches are resolved with simple vector comparison\n- **Accuracy**: AI_CLASSIFY handles nuanced cases where vector similarity alone is insufficient (e.g., similar product names for different models)\n\n**Run the cell `r_Hybrid_Matching`**\n\n**Expected Output:**\nThe cell creates the `hybrid_final_results` table and displays metrics:\n- Total matches found\n- Average confidence score\n- Number of high-confidence vector matches\n- Number of AI_CLASSIFY matches\n- AI_CLASSIFY usage percentage (typically 20-40% of records)\n\n---\n\n### Section 4: Record Management and Threshold Selection (Python Cell)\n\n**What It Does:**\nProvides an interactive streamlit interface to review match quality and separate records into MATCHED and UNMATCHED tables based on a confidence threshold.\n\n**Why This Matters:**\nDifferent use cases require different confidence thresholds:\n- **High-stakes pricing decisions**: Use 90%+ threshold, manually review unmatched\n- **Exploratory analysis**: Use 70%+ threshold, accept more false positives\n- **Iterative improvement**: Start at 80%, review unmatched, improve matching logic\n\n**Run the cell `r_Record_Management`**\n\n**Expected Interaction:**\n- Review the confidence distribution chart\n- Adjust the slider to find the right threshold for your use case (we us 80% in the demo)\n- Preview sample matches at the current threshold\n- Configure output table names (use defaults for this demo)\n- Click **Finalize & Create Datasets** to generate:\n  - `[AUDIT_TABLE]_HYBRID_MATCHED`: High-confidence matches\n  - `[AUDIT_TABLE]_HYBRID_UNMATCHED`: Records needing review\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/notebook_scroll.gif)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n---\n\n### Section 5: Performance Evaluation (SQL Cell)\n\n**What It Does:**\nEvaluates the hybrid matching approach against our golden dataset mapping table to measure accuracy and identify areas for improvement. **In many cases, you may not have a golden dataset to compare to. Here we have one which we use to test the accuracy of our methods.**\n\n**Key Metrics Calculated:**\n\n1. **Overall Accuracy**: Percentage of matches that align with ground truth\n2. **Accuracy by Confidence Bucket**: How accuracy varies across confidence ranges\n3. **Accuracy by Match Method**: Performance of HIGH_CONFIDENCE_VECTOR vs AI_CLASSIFY vs MEDIUM_CONFIDENCE_VECTOR\n4. **False Match Analysis**: Understanding where the algorithm makes mistakes\n\n\n**Why This Matters:**\n- **Validates Approach**: Proves the hybrid method achieves high accuracy\n- **Informs Thresholds**: Shows you where to set confidence cutoffs\n- **Identifies Improvements**: Highlights which types of products are harder to match\n- **Benchmarking**: Provides a baseline for comparing alternative approaches\n\n**Run the cell `r_Performance_Evaluation`**\n\n**As you can see, using our methods against this test dataset we achieve a \u003E90% accuracy when compared to our golden dataset!**\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/notebook4.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n---\n\n\u003E **Pro Tip**: The hybrid approach shines when you have a mix of easy and hard matching cases. Pure vector similarity handles the easy 70-80%, while AI_CLASSIFY intelligently resolves the remaining ambiguous cases.\n\nIn the next section, we'll build an interactive Streamlit app to review and correct the unmatched records, creating a human-in-the-loop validation workflow.\n\n## Unmatched Record Reconciliation\n\nAfter running the hybrid matching algorithm, you'll have two sets of records:\n1. **Matched Records**: High-confidence matches ready for use in analysis\n2. **Unmatched Records**: Lower-confidence cases that need human review\n\nThis section addresses the unmatched records through an interactive Streamlit application that enables efficient, user-friendly review and correction.\n\n### The Problem with Unmatched Records\n\nUnmatched records typically fall into a few categories:\n\n1. **Close Calls**: Multiple viable candidates with similar scores (e.g., different models of the same product line)\n2. **Data Quality Issues**: Missing information, typos, or incomplete descriptions\n3. **Unique Products**: Items that genuinely don't have a match in the other dataset\n4. **Algorithm Limitations**: Cases where both vector similarity and AI_CLASSIFY struggled\n\nRather than accepting these as lost opportunities, the Unmatched Records Reviewer provides a structured workflow to:\n- Review each unmatched record systematically\n- View alternative candidate matches with their similarity scores\n- Make informed manual selections or confirm \"no match found\"\n- Track all decisions with full audit trails\n\n### What the Unmatched Records App Does\n\nThe application implements a comprehensive review workflow:\n\n#### 1. Intelligent Data Loading\n- Automatically discovers unmatched table from the audit trail\n- Identifies the corresponding matched table for approved records\n- Loads up to 20 candidate matches per record from the original `hybrid_features` table\n- Paginates records for manageable review sessions (10 records per page)\n\n#### 2. Interactive Review Interface\nFor each unmatched record, the reviewer displays:\n- **Reference Entity Name**: The ABT product you're trying to match\n- **Top Candidate Matches**: Dropdown showing up to 20 Best Buy candidates, ranked by vector similarity\n- **Similarity Scores**: Real-time display of vector similarity for the selected candidate\n- **Match Approval Checkbox**: Confirm when you've selected the correct match\n\n#### 3. Dynamic Search and Filtering\n- **Text Search**: Filter records by reference entity name to quickly find specific products\n- **Pagination**: Navigate through pages of unmatched records\n- **Jump Controls**: Skip to first or last page instantly\n\n#### 4. Batch Processing\n- **Page-by-Page Approval**: Review and approve matches on each page\n- **Cross-Page Memory**: Selections are preserved as you navigate between pages\n- **Bulk Submission**: Submit all approved matches from all pages at once\n\n### Running the Unmatched Records App\n\n#### Step 1: Create the Streamlit App\n\n1. Navigate to **Projects** \u003E **Streamlit** in Snowsight\n2. Click **+ Streamlit App**\n3. Name it `data harmonization - Unmatched Records`\n4. Select your `ABT_BEST_BUY` database and `STRUCTURED` schema\n5. Select the `ENTITY_RESOLUTION_WH` warehouse\n\n#### Step 2: Upload the Application Code\n\n1. Delete the example code in the `streamlit_app.py` file\n2. Copy the contents of `data harmonization - Unmatched Records.py`\n3. Paste the new code from the `data harmonization - Unmatched Records.py` into this file\n4. Be sure you install the appropriate packages\n5. Click **Run**\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched1.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\n#### Step 3: Select Unmatched Table\n\n1. The app automatically queries `AUDIT_HARMONIZATION_HYBRID_AUDIT` to find unmatched/matched table pairs\n2. From the dropdown, select your unmatched table (e.g., `AUDIT_HARMONIZATION_2025_10_04_HYBRID_UNMATCHED`)\n3. Click **Process** to load the records\n\n\n\u003E **Note**: The app automatically identifies the corresponding matched table from the audit table, ensuring records are moved to the correct destination.\n\n#### Step 3: Review Records\n\nFor each record on the page:\n\n**Reference Record Display:**\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched2.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n**Candidate Selection:**\nThe dropdown shows candidates with similarity scores:\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched3.png)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n\n**Actions:**\n- Select the best match from the dropdown\n- Review the vector similarity score (displayed dynamically)\n- Check **✅ Match Approved** if you're confident in the selection\n- **Leave unchecked if no good match exists (record remains unmatched) - Only mannually selected records will be processed to our matched table** \n\n#### Step 4: Navigate and Continue Reviewing\n\n- Use **← Previous** and **Next →** buttons to move between pages\n- Use **⏪ Jump to First Page** or **⏩ Jump to Last Page** for quick navigation\n- Use the search box to filter records by specific text\n- The page indicator shows your progress (e.g., \"Page 5 of 25\")\n\n\u003E **Workflow Tip**: Focus on records with high similarity scores (\u003E0.75) first, as these are more likely to be correct matches. Records with very low scores (\u003C0.50) may genuinely have no match in the dataset.\n\n#### Step 5: Submit Reviews\n\nOnce you reach the last page:\n1. Review the **Review Summary** showing:\n   - Records Approved to Move\n   - Records Not Approved\n2. Click **Submit All Reviews** to process all approved matches across all pages\n3. The app will:\n   - Update the `FINAL_MATCH` column for approved records\n   - Move approved records from unmatched to matched table\n   - Delete processed records from unmatched table\n   - Display a detailed summary of changes\n\n#### Step 6: Review Completion Summary\n\nAfter submission, you'll see a summary of the records you sent to the matched table.\n\nClick **📑 Continue with Analysis** to review remaining records or refresh the view.\n\n\u003Cbr/\u003E\u003Cbr/\u003E\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-harmonization/Unmatched2.gif)\n\u003Cbr/\u003E\u003Cbr/\u003E\n\n### Best Practices for Record Review\n\n#### 1. Prioritize by Confidence\nStart with records that have similarity scores just below your threshold (e.g., 0.75-0.80), as these are most likely to be correctable with manual review.\n\n#### 2. Look for Patterns\nIf you notice multiple records failing for the same reason (e.g., missing manufacturer information), consider updating your harmonization mapping to include additional fields.\n\n#### 3. Use Domain Knowledge\nProduct expertise is invaluable here. If you know ABT's catalog, you can quickly identify:\n- Products that should match but have different model numbers\n- Products that are actually different items despite similar names\n- Products that genuinely don't exist in Best Buy's catalog\n\n#### 4. Don't Force Matches\nIf none of the candidates are correct, leave the record unchecked. It's better to have an unmatched record than an incorrect match that pollutes your analysis.\n\n#### 5. Iterative Review Sessions\nYou don't have to review all records in one sitting. The app preserves state across sessions (records remain in the unmatched table until explicitly moved).\n\n### The Value of Human-in-the-Loop\n\nThis review workflow exemplifies the **human-in-the-loop** approach to AI:\n\n1. **AI Does the Heavy Lifting**: The hybrid matching algorithm handles 75-85% of records automatically\n2. **Humans Handle Edge Cases**: Reviewers focus only on ambiguous cases that need domain expertise\n3. **Continuous Improvement**: Insights from manual review can inform future matching logic improvements\n4. **Quality Assurance**: Human validation provides confidence in match quality for downstream analytics\n\n\u003E **Real-World Impact**: In production environments, this workflow reduces data harmonization time from weeks of manual work to hours of focused review, while maintaining high match quality.\n\nBy the end of this section, you'll have:\n- ✅ Reviewed all unmatched records systematically\n- ✅ Made informed decisions about match selections\n- ✅ Moved high-quality matches to the matched table\n- ✅ Identified records that genuinely have no match\n- ✅ Maintained complete audit trails for all decisions\n\nYou now have a complete, production-ready data harmonization pipeline with matched records ready for competitive pricing analysis!\n\n## Conclusion and Resources\n\nCongratulations! You've successfully built an end-to-end data harmonization solution using Snowflake's native AI capabilities, combining automated matching with human-in-the-loop validation.\n\n### What You Accomplished\n\nThroughout this quickstart, you:\n\n1. ✅ **Prepared Data**: Loaded product catalogs from two different retailers with different schemas\n2. ✅ **Harmonized Schemas**: Used Snowflake Cortex AI to automatically map semantic field differences and create unified datasets\n3. ✅ **Implemented Hybrid Matching**: Combined vector similarity (fast) with AI_CLASSIFY (intelligent) for optimal matching performance\n4. ✅ **Validated Quality**: Measured accuracy against ground truth data and achieved 85%+ match quality\n5. ✅ **Reviewed Edge Cases**: Used an interactive Streamlit app to manually validate uncertain matches\n6. ✅ **Built Production Pipeline**: Created a complete workflow with audit trails, confidence scoring, and quality metrics\n\n### Key Takeaways\n\n**Business Value:**\n- Reduced data harmonization from weeks of manual work to hours of automated processing\n- Achieved high match quality (85%+) without training custom ML models\n- Created a repeatable, scalable process for ongoing data integration needs\n- Enabled competitive pricing analysis that was previously infeasible\n\n**Technical Innovation:**\n- **Cortex AI for Schema Analysis**: Automatically understand and map disparate data structures\n- **Vector Embeddings**: Capture semantic similarity beyond exact text matching\n- **Hybrid Approach**: Optimize for both speed (vector similarity) and accuracy (AI_CLASSIFY)\n- **Streamlit in Snowflake**: Build rich, interactive applications without external infrastructure\n\n**Best Practices:**\n- Always profile and harmonize data before attempting matching\n- Use confidence thresholds to separate high-quality matches from cases needing review\n- Maintain audit trails for all decisions and transformations\n- Validate against ground truth when available to measure and improve accuracy\n- Implement human-in-the-loop for edge cases that require domain expertise\n\n### Related Resources\n\n**Documentation:**\n- [Snowflake Cortex AI](https://docs.snowflake.com/en/user-guide/snowflake-cortex/overview)\n- [Snowflake Cortex Vector Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/vector-embeddings)\n- [Snowflake Cortex AI_CLASSIFY](https://docs.snowflake.com/en/user-guide/snowflake-cortex/ml-functions/classification)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [Snowflake Notebooks](https://docs.snowflake.com/en/user-guide/ui-snowsight-notebooks)\n\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-700f906937","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-6baa22a5f7",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-fc9619b16c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-02-06",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b9b2932952","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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