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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-cbff11c392","quickstartHeroTitle":{"lines":["Build a Generative BI Dashboard with Amazon QuickSight and Amazon Q"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Bharath 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this guide, We will build an end-to-end Generative BI dashboard using \u003Ca href=\"https://aws.amazon.com/quicksight/q/\"\u003EAmazon Q in QuickSight\u003C/a\u003E, with \u003Ca href=\"/en/\"\u003ESnowflake AI & Data Cloud\u003C/a\u003E as the data source. We will use the  Sales dataset to explore sales trends, regional performance, and customer insights with the power of Natural Language Q&A and Executive Summaries.\u003C/p\u003E\n","\u003Cp\u003EThis guide walks through:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPreparing and loading data into Snowflake\u003C/li\u003E\u003Cli\u003EConnecting QuickSight to Snowflake\u003C/li\u003E\u003Cli\u003EEnabling Generative BI features with Amazon Q\u003C/li\u003E\u003Cli\u003EBuilding smart insights interactively\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis guide applies to any business domain where structured data and conversational analytics intersect.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic familiarity with Amazon QuickSight and Snowflake\u003C/li\u003E\u003Cli\u003EAWS account with QuickSight Enterprise enabled\u003C/li\u003E\u003Cli\u003ESnowflake account with access to a database, schema, and compute warehouse\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to load and clean real-world datasets into Snowflake\u003C/li\u003E\u003Cli\u003EHow to connect Amazon QuickSight to Snowflake\u003C/li\u003E\u003Cli\u003EHow to build smart visuals using Generative BI\u003C/li\u003E\u003Cli\u003EHow to use Amazon Q for Natural Language Q&A\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Account. \u003Ca href=\"https://signup.snowflake.com/\"\u003ESign up for Snowflake Trial Account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EAWS QuickSight Enterprise enabled\u003C/li\u003E\u003Cli\u003EKaggle Dataset\u003C/li\u003E\u003Cli\u003EPython 3.x with pandas (optional)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Generative BI dashboard powered by Amazon Q and backed by Snowflake\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Loading\u003C/h2\u003E\n","\u003Ch3\u003EDownload the Dataset\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EDownload the  Sales Data from Kaggle: Go to the \u003Ca href=\"https://www.kaggle.com/datasets/ishanshrivastava28/superstore-sales\"\u003EKaggle Dataset\u003C/a\u003E page and download the CSV file.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate the Target Database Objects\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBefore you create the table, make sure to configure environment with the right role, warehouse, database, and schema. We can do this step-by-step as follows:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Step 1: Use the ACCOUNTADMIN role (or any role with required privileges)\nUSE ROLE ACCOUNTADMIN;\n\n-- Step 2: Create a virtual warehouse if not already created\nCREATE OR REPLACE WAREHOUSE demo_wh\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 60\n  AUTO_RESUME = TRUE;\n\n-- Step 3: Create a database to store your dataset\nCREATE OR REPLACE DATABASE demo_db;\n\n-- Step 4: Create a schema for organization (optional)\nCREATE OR REPLACE SCHEMA demo_db.sales_data;\n\n-- Step 5: Set your context\nUSE WAREHOUSE demo_wh;\nUSE DATABASE demo_db;\nUSE SCHEMA sales_data;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EOnce your context is set, create the table to hold the cleaned data:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE sales_orders (\n    row_id INT,\n    order_id STRING,\n    order_date DATE,\n    ship_date DATE,\n    ship_mode STRING,\n    customer_id STRING,\n    customer_name STRING,\n    segment STRING,\n    country STRING,\n    city STRING,\n    state STRING,\n    postal_code STRING,\n    region STRING,\n    product_id STRING,\n    category STRING,\n    sub_category STRING,\n    product_name STRING,\n    sales FLOAT,\n    quantity INT,\n    discount FLOAT,\n    profit FLOAT\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Named File Format (Optional)\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EYou can explicitly define a CSV file format for reuse:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE FILE FORMAT csv_format\n  TYPE = 'CSV'\n  FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n  SKIP_HEADER = 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate a Snowflake Stage\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EStages allow you to load data into Snowflake from cloud storage (e.g., S3). For example:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE demo_stage\n  URL = 's3://your-bucket-name/path-to-folder/'\n  STORAGE_INTEGRATION = your_integration_name;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EIf using internal stage, you can upload the file via Snowsight UI:\u003C/li\u003E\u003Cli\u003EGo to Data > Stages\u003C/li\u003E\u003Cli\u003ESelect your stage and click Upload File\u003C/li\u003E\u003Cli\u003EUpload superstore_sales.csv to the stage\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECOPY INTO sales_orders\nFROM @demo_stage/sales_orders.csv\nFILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY='"' SKIP_HEADER=1);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConnect QuickSight to Snowflake\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003EIn this step, you'll configure Amazon QuickSight and connect it to your Snowflake instance to build live or SPICE-imported dashboards.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECollect Snowflake Connection Info\u003C/h3\u003E\n","\u003Cp\u003EGather the following connection details from your Snowflake admin or Snowsight:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAccount Identifier: (e.g., xy12345.us-east-1)\u003C/li\u003E\u003Cli\u003EDatabase: demo_db\u003C/li\u003E\u003Cli\u003ESchema: sales_data\u003C/li\u003E\u003Cli\u003EWarehouse: demo_wh\u003C/li\u003E\u003Cli\u003ETable: sales_orders\u003C/li\u003E\u003Cli\u003EUsername & Password: Snowflake login credentials with USAGE and SELECT privileges\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/snowflake-account-identifier.png\" alt=\"Snowflake Connection Information\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESign in to the AWS Console\u003C/h3\u003E\n","\u003Cp\u003ENavigate to the AWS Console\nSearch for QuickSight in the service search bar\nChoose Amazon QuickSight and click Sign in\nIf you're using QuickSight for the first time:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChoose your region (must match your Snowflake region if using SPICE)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate a Snowflake Data Source in QuickSight\u003C/h3\u003E\n","\u003Cp\u003EFrom the QuickSight homepage, go to Manage data in the left panel\nClick New dataset > Select Snowflake as the source\nFill in the Snowflake connection form:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/quicksight-snowflake-connection.png\" alt=\"Quicksight Snowflake Connection\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EData source name: SnowflakeSalesOrders\u003C/li\u003E\u003Cli\u003EAccount name: Enter your Snowflake account locator (e.g., xy12345.us-east-1)\u003C/li\u003E\u003Cli\u003EUser name / Password: Use Snowflake credentials with SELECT access\u003C/li\u003E\u003Cli\u003EWarehouse: demo_wh\u003C/li\u003E\u003Cli\u003EDatabase: demo_db\u003C/li\u003E\u003Cli\u003ESchema: sales_data\nClick Validate connection and then Create data source\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESelect and Import the Table\u003C/h3\u003E\n","\u003Cp\u003ESelect the sales_orders table from the schema browser\nChoose a data access mode:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDirect Query: Fetches live data from Snowflake with each interaction\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/table-import.png\" alt=\"Import Table\"\u003E\u003C/li\u003E\u003Cli\u003EImport to SPICE: Recommended for performance, especially for static datasets\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/enable-spice.png\" alt=\"Spice Enable\"\u003E\u003C/li\u003E\u003Cli\u003EClick Visualize to begin building your dashboards\u003C/li\u003E\u003Cli\u003EOptional: QuickSight connects to Snowflake securely using JDBC. Use Secrets Manager or SSO for enhanced credential management.. You can enable SSO or use AWS Secrets Manager for managing credentials.\u003C/li\u003E\u003Cli\u003EQuickSight uses a secure JDBC connection with optional IAM-based credentials.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EVisualization\u003C/h2\u003E\n","\u003Ch3\u003EBuild Core Visuals\u003C/h3\u003E\n","\u003Cp\u003ENow that your Snowflake data is available in QuickSight, it's time to create a set of foundational visualizations. These visuals will help users analyze sales performance, regional trends, customer behavior, and product-level profitability.\u003C/p\u003E\n","\u003Ch3\u003EStep 1: Open the Analysis Editor\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFrom the QuickSight home screen, go to Analyses\u003C/li\u003E\u003Cli\u003EClick New analysis\u003C/li\u003E\u003Cli\u003ESelect your previously connected dataset (from Snowflake)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2: Create the Following Visuals\u003C/h3\u003E\n","\u003Ch4\u003E1. Sales by Region (Bar Chart)\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EClick Add → Add visual\u003C/li\u003E\u003Cli\u003ESelect Vertical bar chart\u003C/li\u003E\u003Cli\u003EDrag region to X-axis\u003C/li\u003E\u003Cli\u003EDrag sales to Value\u003C/li\u003E\u003Cli\u003ESort by descending sales\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/sales-by-region.png\" alt=\"Sales by Region\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E2. Monthly Profit Trend (Line Chart)\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EClick Add visual\u003C/li\u003E\u003Cli\u003ESelect Line chart\u003C/li\u003E\u003Cli\u003EDrag order_date to X-axis and change the aggregation to Month\u003C/li\u003E\u003Cli\u003EDrag profit to Value\u003C/li\u003E\u003Cli\u003EFilter by recent year or region if desired\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/profit-by-date.png\" alt=\"Profits by Date\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E3. Category vs. Sub-Category Sales (Stacked Column Chart)\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EClick Add visual\u003C/li\u003E\u003Cli\u003ESelect Stacked column chart\u003C/li\u003E\u003Cli\u003EDrag category to X-axis\u003C/li\u003E\u003Cli\u003EDrag sales to Value\u003C/li\u003E\u003Cli\u003EDrag sub_category to Group by\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/sales-by-category.png\" alt=\"Sales by Category\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E4. Sales by State (Map Visual)\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003EClick Add visual → Select Map\u003C/li\u003E\u003Cli\u003EDrag state to Geospatial field\u003C/li\u003E\u003Cli\u003EDrag sales to Value\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/sales-region.png\" alt=\"Sales by Region\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 3: Customize Appearance\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFormat currency fields (sales, profit) to show in appropriate currency format\u003C/li\u003E\u003Cli\u003EAdjust color palettes to align with your brand or data themes\u003C/li\u003E\u003Cli\u003ERename visuals to user-friendly titles (e.g., "Top Performing States")\u003C/li\u003E\u003Cli\u003EOnce these base visuals are in place, you're ready to enrich them with AI-powered enhancements using Amazon Q.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EEnable Generative BI with Amazon Q\u003C/h2\u003E\n","\u003Ch3\u003ECreate Visualization with Q\u003C/h3\u003E\n","\u003Cp\u003ESelect Q build visual icon\u003C/p\u003E\n","\u003Cp\u003EAsk questions in natural english like:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Show total profit in California"\u003C/li\u003E\u003Cli\u003E"Top 5 products by sales"\u003C/li\u003E\u003Cli\u003E"Monthly orders by region"\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/q-analysis.gif\" alt=\"Q Analysis\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAdd Smart Widgets\u003C/h3\u003E\n","\u003Cp\u003EUse Amazon Q to create these visuals:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E"Sales and profit by category"\u003C/li\u003E\u003Cli\u003E"Products with negative profit"\u003C/li\u003E\u003Cli\u003E"Profit margin by customer segment"\u003C/li\u003E\u003Cli\u003E"Monthly order volume"\u003C/li\u003E\u003Cli\u003E"Most discounted items by category"\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/q-widget.gif\" alt=\"Q widget\"\u003E\nEach NLP prompt results in an auto-generated visual you can refine and add to your dashboard.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EGenerate Executive Summary\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick Publish Dashboard\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAmazon Q generates narrative insights for:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EKey trends\u003C/li\u003E\u003Cli\u003ERegional anomalies\u003C/li\u003E\u003Cli\u003ETop/worst performers\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Ch3\u003ESnowflake Clean Up\u003C/h3\u003E\n","\u003Cp\u003EIf this was a test/demo environment:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDrop demo tables:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP TABLE IF EXISTS sales_orders;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EDrop stages or file formats:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP STAGE IF EXISTS demo_stage;\nDROP FILE FORMAT IF EXISTS csv_format;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EDrop schema and database (if not used elsewhere):\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP SCHEMA IF EXISTS demo_db.sales_data;\nDROP DATABASE IF EXISTS demo_db;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAWS Clean Up Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EDisable dashboards not in use\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERemove unused datasets from Manage Data\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EManage SPICE capacity under Manage QuickSight > SPICE capacity\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVersioning & Collaboration\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EExport the dashboard and share with teams\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDocument data definitions and synonyms used in Q Topics\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAssign dataset permissions to relevant users or groups\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EYou’ve now built a powerful, AI-enabled dashboard that:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUses Snowflake for enterprise-grade data platform and querying\u003C/li\u003E\u003Cli\u003EUses Amazon QuickSight for visual exploration\u003C/li\u003E\u003Cli\u003EUses Amazon Q for Generative AI supported insights\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to ingest and query data in Snowflake\u003C/li\u003E\u003Cli\u003EHow to visualize live Snowflake data in QuickSight\u003C/li\u003E\u003Cli\u003EHow to build dashboards using AI-generated content\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/\"\u003ESnowflake Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.aws.amazon.com/quicksight/latest/user/quicksight-gen-bi.html\"\u003EAmazon Q for QuickSight Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.kaggle.com/datasets/ishanshrivastava28/superstore-sales\"\u003EKaggle Dataset\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build a Generative BI Dashboard with Amazon QuickSight and Amazon Q","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\r\nIn this guide, We will build an end-to-end Generative BI dashboard using [Amazon Q in QuickSight](https://aws.amazon.com/quicksight/q/), with [Snowflake AI & Data Cloud](/en/) as the data source. We will use the  Sales dataset to explore sales trends, regional performance, and customer insights with the power of Natural Language Q&A and Executive Summaries.\r\n\r\nThis guide walks through:\r\n\r\n- Preparing and loading data into Snowflake\r\n- Connecting QuickSight to Snowflake\r\n- Enabling Generative BI features with Amazon Q\r\n- Building smart insights interactively\r\n\r\nThis guide applies to any business domain where structured data and conversational analytics intersect.\r\n\r\n### Prerequisites\r\n- Basic familiarity with Amazon QuickSight and Snowflake\r\n- AWS account with QuickSight Enterprise enabled\r\n- Snowflake account with access to a database, schema, and compute warehouse\r\n\r\n### What You’ll Learn \r\n- How to load and clean real-world datasets into Snowflake\r\n- How to connect Amazon QuickSight to Snowflake\r\n- How to build smart visuals using Generative BI\r\n- How to use Amazon Q for Natural Language Q&A\r\n\r\n### What You’ll Need \r\n- A Snowflake Account. [Sign up for Snowflake Trial Account](https://signup.snowflake.com/)\r\n- AWS QuickSight Enterprise enabled\r\n- Kaggle Dataset\r\n- Python 3.x with pandas (optional)\r\n\r\n### What You’ll Build \r\n\r\n- A Generative BI dashboard powered by Amazon Q and backed by Snowflake\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n##  Data Loading\r\n\r\n### Download the Dataset\r\n- Download the  Sales Data from Kaggle: Go to the [Kaggle Dataset](https://www.kaggle.com/datasets/ishanshrivastava28/superstore-sales) page and download the CSV file.\r\n\r\n### Create the Target Database Objects\r\n- Before you create the table, make sure to configure environment with the right role, warehouse, database, and schema. We can do this step-by-step as follows:\r\n\r\n```sql\r\n-- Step 1: Use the ACCOUNTADMIN role (or any role with required privileges)\r\nUSE ROLE ACCOUNTADMIN;\r\n\r\n-- Step 2: Create a virtual warehouse if not already created\r\nCREATE OR REPLACE WAREHOUSE demo_wh\r\n  WITH WAREHOUSE_SIZE = 'XSMALL'\r\n  AUTO_SUSPEND = 60\r\n  AUTO_RESUME = TRUE;\r\n\r\n-- Step 3: Create a database to store your dataset\r\nCREATE OR REPLACE DATABASE demo_db;\r\n\r\n-- Step 4: Create a schema for organization (optional)\r\nCREATE OR REPLACE SCHEMA demo_db.sales_data;\r\n\r\n-- Step 5: Set your context\r\nUSE WAREHOUSE demo_wh;\r\nUSE DATABASE demo_db;\r\nUSE SCHEMA sales_data;\r\n```\r\n\r\n- Once your context is set, create the table to hold the cleaned data:\r\n```sql\r\nCREATE OR REPLACE TABLE sales_orders (\r\n    row_id INT,\r\n    order_id STRING,\r\n    order_date DATE,\r\n    ship_date DATE,\r\n    ship_mode STRING,\r\n    customer_id STRING,\r\n    customer_name STRING,\r\n    segment STRING,\r\n    country STRING,\r\n    city STRING,\r\n    state STRING,\r\n    postal_code STRING,\r\n    region STRING,\r\n    product_id STRING,\r\n    category STRING,\r\n    sub_category STRING,\r\n    product_name STRING,\r\n    sales FLOAT,\r\n    quantity INT,\r\n    discount FLOAT,\r\n    profit FLOAT\r\n);\r\n```\r\n\r\n### Create a Named File Format (Optional)\r\n- You can explicitly define a CSV file format for reuse:\r\n\r\n```sql\r\nCREATE OR REPLACE FILE FORMAT csv_format\r\n  TYPE = 'CSV'\r\n  FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\r\n  SKIP_HEADER = 1;\r\n```\r\n\r\n### Create a Snowflake Stage\r\n- Stages allow you to load data into Snowflake from cloud storage (e.g., S3). For example:\r\n\r\n```sql\r\nCREATE OR REPLACE STAGE demo_stage\r\n  URL = 's3://your-bucket-name/path-to-folder/'\r\n  STORAGE_INTEGRATION = your_integration_name;\r\n```\r\n\r\n- If using internal stage, you can upload the file via Snowsight UI:\r\n- Go to Data \u003E Stages\r\n- Select your stage and click Upload File\r\n- Upload superstore_sales.csv to the stage\r\n\r\n```sql\r\nCOPY INTO sales_orders\r\nFROM @demo_stage/sales_orders.csv\r\nFILE_FORMAT = (TYPE = 'CSV' FIELD_OPTIONALLY_ENCLOSED_BY='\"' SKIP_HEADER=1);\r\n```\r\n\r\n## Connect QuickSight to Snowflake\r\n\r\n- In this step, you'll configure Amazon QuickSight and connect it to your Snowflake instance to build live or SPICE-imported dashboards.\r\n\r\n### Collect Snowflake Connection Info\r\n\r\nGather the following connection details from your Snowflake admin or Snowsight:\r\n- Account Identifier: (e.g., xy12345.us-east-1)\r\n- Database: demo_db\r\n- Schema: sales_data\r\n- Warehouse: demo_wh\r\n- Table: sales_orders\r\n- Username & Password: Snowflake login credentials with USAGE and SELECT privileges\r\n![Snowflake Connection Information](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/snowflake-account-identifier.png)\r\n### Sign in to the AWS Console\r\nNavigate to the AWS Console\r\nSearch for QuickSight in the service search bar\r\nChoose Amazon QuickSight and click Sign in\r\nIf you're using QuickSight for the first time:\r\n- Choose your region (must match your Snowflake region if using SPICE)\r\n\r\n### Create a Snowflake Data Source in QuickSight\r\n\r\nFrom the QuickSight homepage, go to Manage data in the left panel\r\nClick New dataset \u003E Select Snowflake as the source\r\nFill in the Snowflake connection form:\r\n![Quicksight Snowflake Connection](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/quicksight-snowflake-connection.png)\r\n- Data source name: SnowflakeSalesOrders\r\n- Account name: Enter your Snowflake account locator (e.g., xy12345.us-east-1)\r\n- User name / Password: Use Snowflake credentials with SELECT access\r\n- Warehouse: demo_wh\r\n- Database: demo_db\r\n- Schema: sales_data\r\nClick Validate connection and then Create data source\r\n\r\n### Select and Import the Table\r\n\r\nSelect the sales_orders table from the schema browser\r\nChoose a data access mode:\r\n- Direct Query: Fetches live data from Snowflake with each interaction\r\n![Import Table](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/table-import.png)\r\n- Import to SPICE: Recommended for performance, especially for static datasets\r\n![Spice Enable](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/enable-spice.png)\r\n- Click Visualize to begin building your dashboards\r\n- Optional: QuickSight connects to Snowflake securely using JDBC. Use Secrets Manager or SSO for enhanced credential management.. You can enable SSO or use AWS Secrets Manager for managing credentials.\r\n- QuickSight uses a secure JDBC connection with optional IAM-based credentials.\r\n\r\n## Visualization\r\n\r\n###  Build Core Visuals\r\n\r\nNow that your Snowflake data is available in QuickSight, it's time to create a set of foundational visualizations. These visuals will help users analyze sales performance, regional trends, customer behavior, and product-level profitability.\r\n\r\n### Step 1: Open the Analysis Editor\r\n\r\n- From the QuickSight home screen, go to Analyses\r\n- Click New analysis\r\n- Select your previously connected dataset (from Snowflake)\r\n\r\n### Step 2: Create the Following Visuals\r\n\r\n#### 1. Sales by Region (Bar Chart)\r\n\r\n- Click Add → Add visual\r\n- Select Vertical bar chart\r\n- Drag region to X-axis\r\n- Drag sales to Value\r\n- Sort by descending sales\r\n![Sales by Region](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/sales-by-region.png)\r\n#### 2. Monthly Profit Trend (Line Chart)\r\n\r\n- Click Add visual\r\n- Select Line chart\r\n- Drag order_date to X-axis and change the aggregation to Month\r\n- Drag profit to Value\r\n- Filter by recent year or region if desired\r\n![Profits by Date](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/profit-by-date.png)\r\n\r\n#### 3. Category vs. Sub-Category Sales (Stacked Column Chart)\r\n\r\n- Click Add visual\r\n- Select Stacked column chart\r\n- Drag category to X-axis\r\n- Drag sales to Value\r\n- Drag sub_category to Group by\r\n![Sales by Category](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/sales-by-category.png)\r\n\r\n#### 4. Sales by State (Map Visual)\r\n\r\n- Click Add visual → Select Map\r\n- Drag state to Geospatial field\r\n- Drag sales to Value\r\n![Sales by Region](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/sales-region.png)\r\n\r\n### Step 3: Customize Appearance\r\n\r\n- Format currency fields (sales, profit) to show in appropriate currency format\r\n- Adjust color palettes to align with your brand or data themes\r\n- Rename visuals to user-friendly titles (e.g., \"Top Performing States\")\r\n- Once these base visuals are in place, you're ready to enrich them with AI-powered enhancements using Amazon Q.\r\n\r\n## Enable Generative BI with Amazon Q\r\n\r\n### Create Visualization with Q \r\n\r\nSelect Q build visual icon\r\n\r\n\r\nAsk questions in natural english like:\r\n- \"Show total profit in California\"\r\n- \"Top 5 products by sales\"\r\n- \"Monthly orders by region\"\r\n![Q Analysis](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/q-analysis.gif)\r\n\r\n### Add Smart Widgets\r\n\r\nUse Amazon Q to create these visuals:\r\n- \"Sales and profit by category\"\r\n- \"Products with negative profit\"\r\n- \"Profit margin by customer segment\"\r\n- \"Monthly order volume\"\r\n- \"Most discounted items by category\"\r\n![Q widget](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/quickstart-generative-bi-quicksight/q-widget.gif)\r\nEach NLP prompt results in an auto-generated visual you can refine and add to your dashboard.\r\n\r\n### Generate Executive Summary\r\n\r\n- Click Publish Dashboard\r\n\r\nAmazon Q generates narrative insights for:\r\n- Key trends\r\n- Regional anomalies\r\n- Top/worst performers\r\n## Clean Up\r\n\r\n### Snowflake Clean Up\r\n\r\nIf this was a test/demo environment:\r\n\r\n- Drop demo tables:\r\n\r\n```sql\r\nDROP TABLE IF EXISTS sales_orders;\r\n```\r\n\r\n- Drop stages or file formats:\r\n\r\n```sql\r\nDROP STAGE IF EXISTS demo_stage;\r\nDROP FILE FORMAT IF EXISTS csv_format;\r\n```\r\n\r\n- Drop schema and database (if not used elsewhere):\r\n\r\n```sql\r\nDROP SCHEMA IF EXISTS demo_db.sales_data;\r\nDROP DATABASE IF EXISTS demo_db;\r\n```\r\n\r\n### AWS Clean Up Steps\r\n\r\n- Disable dashboards not in use\r\n\r\n- Remove unused datasets from Manage Data\r\n\r\n- Manage SPICE capacity under Manage QuickSight \u003E SPICE capacity\r\n\r\n### Versioning & Collaboration\r\n\r\n- Export the dashboard and share with teams\r\n\r\n- Document data definitions and synonyms used in Q Topics\r\n\r\n- Assign dataset permissions to relevant users or groups\r\n\r\n## Conclusion and Resources\r\n\r\nYou’ve now built a powerful, AI-enabled dashboard that:\r\n- Uses Snowflake for enterprise-grade data platform and querying\r\n- Uses Amazon QuickSight for visual exploration\r\n- Uses Amazon Q for Generative AI supported insights\r\n\r\n### What You Learned\r\n- How to ingest and query data in Snowflake\r\n- How to visualize live Snowflake data in QuickSight\r\n- How to build dashboards using AI-generated content\r\n\r\n### Related Resources\r\n\r\n- [Snowflake Documentation](https://docs.snowflake.com/)\r\n- [Amazon Q for QuickSight Documentation](https://docs.aws.amazon.com/quicksight/latest/user/quicksight-gen-bi.html)\r\n- [Kaggle Dataset](https://www.kaggle.com/datasets/ishanshrivastava28/superstore-sales)","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-a3536619f4","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-8eeea2fbdb",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ee184c8e5c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-04-21",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-01a8e89bdb","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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