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When a dashboard needs to refresh every few seconds and answer sub-second queries at high concurrency, you need a different architecture. Snowflake's \u003Cstrong\u003Einteractive warehouses\u003C/strong\u003E close that gap: they maintain a warm in-memory cache tuned for selective, low-latency reads. They query standard tables directly, with no copy or conversion, an approach called zero-copy interactive analytics.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, you will build a complete real-time analytics system for a synthetic e-commerce store. A \u003Cstrong\u003ESnowpark Container Services (SPCS)\u003C/strong\u003E container generates orders server-side at a configurable rate, writing each tick into a standard, clustered table through a stored procedure. An \u003Cstrong\u003Einteractive warehouse\u003C/strong\u003E keeps that table's cache warm and serves millisecond-latency queries to a \u003Cstrong\u003EStreamlit in Snowflake\u003C/strong\u003E dashboard that refreshes automatically every few seconds.\u003C/p\u003E\n","\u003Cp\u003EEverything runs inside a single Snowflake account. No Kafka, no external message bus, no managed services outside Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/architecture-diagram.png?v=19c87ae3\" alt=\"Architecture diagram showing the data flow between the SPCS simulation service, stored procedure, live orders table, interactive warehouse, history and control tables, maintenance tasks, and the Streamlit dashboard.\"\u003E\u003C/p\u003E\n<!-- Editable source: quickstart/build-real-time-bi-dashboard-interactive-warehouses/assets/architecture_diagram.py in the companion repo (matplotlib) -->\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to cluster a standard table with a key tuned for dashboard queries\u003C/li\u003E\u003Cli\u003EHow to query a standard table through an interactive warehouse at sub-second latency, with no data copy\u003C/li\u003E\u003Cli\u003EHow to build and deploy a long-running Python service on Snowpark Container Services\u003C/li\u003E\u003Cli\u003EHow to run a demo under a dedicated role instead of \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to use \u003Ccode\u003EEXECUTE AS OWNER\u003C/code\u003E to give a service a single, narrow write path\u003C/li\u003E\u003Cli\u003EHow to build an auto-refreshing dashboard with \u003Ccode\u003Est.fragment\u003C/code\u003E in Streamlit in Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EYou will assemble five tightly integrated Snowflake objects into a single, self-contained real-time analytics system:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAn SPCS simulation service continuously generates synthetic e-commerce orders and writes them, via a stored procedure, into a clustered standard table\u003C/li\u003E\u003Cli\u003EAn interactive warehouse keeps that table warm for sub-second dashboard queries\u003C/li\u003E\u003Cli\u003EA history table accumulates every tick for trend charts and heatmaps\u003C/li\u003E\u003Cli\u003EA control table acts as the command channel between the dashboard and the simulation service\u003C/li\u003E\u003Cli\u003EA Streamlit in Snowflake app ties everything together, giving you live KPI tiles, auto-refreshing charts, and full simulation controls in one interface\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E, used once to create the demo role. The account must be in a region that supports both \u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive#region-availability\"\u003Einteractive warehouses\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview#available-regions\"\u003ESnowpark Container Services\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.docker.com/get-docker/\"\u003EDocker Desktop\u003C/a\u003E installed and running locally.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/\"\u003EGit\u003C/a\u003E installed.\u003C/li\u003E\u003Cli\u003EBasic familiarity with SQL and Python.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClone the Repository\u003C/h2\u003E\n","\u003Cp\u003EFork and clone the companion repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/sfc-gh-cnantasenamat/build-real-time-bi-dashboard-interactive-warehouses.git\ncd build-real-time-bi-dashboard-interactive-warehouses\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe repository has the following structure:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E├── scripts/\n│   └── setup.sql             # Creates all Snowflake objects in one pass\n├── spcs/\n│   ├── Dockerfile            # Container definition for the simulation service\n│   ├── requirements.txt      # Python dependencies (snowflake-connector-python)\n│   ├── simulation_service.py # Long-running service loop\n│   └── simulation_spec.yaml  # SPCS service specification\n└── streamlit/\n    ├── streamlit_app.py      # Live dashboard application\n    └── environment.yml       # Streamlit in Snowflake Python environment\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Snowflake Objects\u003C/h2\u003E\n","\u003Cp\u003EAll setup SQL is in \u003Ccode\u003Escripts/setup.sql\u003C/code\u003E. Open a Snowsight worksheet and paste the blocks below, or run the full file in one shot. The script is idempotent, so it is safe to re-run.\u003C/p\u003E\n","\u003Cp\u003EHere is a summary of the Snowflake objects you will create:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EObject\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_ROLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOwns every object below, including the service\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EORDERS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETable (clustered)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECurrent tick snapshot of live orders\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAccumulates all historical ticks for charts and heatmaps\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECommand channel between the dashboard and the simulation service\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESP_SIMULATE_TICK\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStored Procedure\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EGenerates synthetic orders each tick\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_WH\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVirtual Warehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDDL, task execution, and general queries\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_IWH\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EInteractive Warehouse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESub-second query cache for the ORDERS table\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_POOL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompute Pool\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESPCS node that runs the simulation container\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_SVC\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESPCS Service\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELong-running simulation service\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_AUTOSTOP\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETask (serverless)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClears a stale running flag when no ticks have landed for 1 hour\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EINTERACTIVE_ANALYTICS_HISTORY_CLEANUP\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETask (warehouse)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPurges ORDERS_HISTORY rows older than 1 day\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ELIVE_ORDERS_DASHBOARD\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStreamlit in Snowflake\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELive dashboard with KPI tiles, charts, and simulation controls\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EDemo role\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E is used only for this block. It creates \u003Ccode\u003EINTERACTIVE_ANALYTICS_ROLE\u003C/code\u003E and grants it the account-level privileges it needs to create its own database, warehouses, compute pool, and tasks. Every other object in this guide is created and owned by that role, so the long-running SPCS service never holds an \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E session.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE ROLE IF NOT EXISTS INTERACTIVE_ANALYTICS_ROLE\n  COMMENT = 'Owns all objects for the real-time BI dashboard demo';\n\nGRANT CREATE DATABASE, CREATE WAREHOUSE, CREATE COMPUTE POOL,\n      EXECUTE TASK, EXECUTE MANAGED TASK\n  ON ACCOUNT TO ROLE INTERACTIVE_ANALYTICS_ROLE;\n\nGRANT ROLE INTERACTIVE_ANALYTICS_ROLE TO ROLE SYSADMIN;\n\nSET MY_USER = CURRENT_USER();\nGRANT ROLE INTERACTIVE_ANALYTICS_ROLE TO USER IDENTIFIER($MY_USER);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDatabase and schema\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\n\nCREATE DATABASE IF NOT EXISTS INTERACTIVE_STREAMING_DEMO;\nCREATE SCHEMA   IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStandard warehouse\u003C/h3\u003E\n","\u003Cp\u003EThe standard warehouse handles DDL, stored procedure execution, task runs, and the longer history queries. Interactive warehouses reject DML and \u003Ccode\u003ECALL\u003C/code\u003E statements and cap queries at 5 seconds, so every write goes through this separate standard warehouse.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE WAREHOUSE IF NOT EXISTS INTERACTIVE_ANALYTICS_WH\n  WAREHOUSE_SIZE      = 'XSMALL'\n  AUTO_SUSPEND        = 60\n  AUTO_RESUME         = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EOrders table\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EORDERS\u003C/code\u003E holds the current tick snapshot: only the most recently generated set of orders. It is a regular table, because interactive warehouses query standard tables directly with no data copy or conversion. The \u003Ccode\u003ECLUSTER BY\u003C/code\u003E key matches the dashboard's \u003Ccode\u003EWHERE\u003C/code\u003E clause: a recent time window filtered by region. Truncating the timestamp to the minute keeps the clustering key low-cardinality and prunes efficiently for "last N minutes" queries.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS (\n  ORDER_ID          STRING,\n  ORDER_TS          TIMESTAMP_NTZ,\n  REGION            STRING,\n  PRODUCT_CATEGORY  STRING,\n  QUANTITY          INT,\n  AMOUNT            NUMBER(10,2)\n)\nCLUSTER BY (DATE_TRUNC('MINUTE', ORDER_TS), REGION);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInteractive warehouse\u003C/h3\u003E\n","\u003Cp\u003ECreate the interactive warehouse and add \u003Ccode\u003EORDERS\u003C/code\u003E to it. Adding a table is optional, since an interactive warehouse can query any table, but it tells the warehouse to warm that table's data cache proactively instead of on the first dashboard query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE INTERACTIVE WAREHOUSE IF NOT EXISTS INTERACTIVE_ANALYTICS_IWH\n  WAREHOUSE_SIZE      = 'XSMALL'\n  AUTO_RESUME         = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nALTER WAREHOUSE INTERACTIVE_ANALYTICS_IWH RESUME IF SUSPENDED;\n\nALTER WAREHOUSE INTERACTIVE_ANALYTICS_IWH\n  ADD TABLES (INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS);\n\nALTER WAREHOUSE INTERACTIVE_ANALYTICS_IWH\n  SET FALLBACK_WAREHOUSE = INTERACTIVE_ANALYTICS_WH;\n\n-- Switch back: interactive warehouses cannot run INSERT, UPDATE or DELETE.\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreating a warehouse makes it the current warehouse for your session. The final \u003Ccode\u003EUSE WAREHOUSE\u003C/code\u003E switches back to the standard warehouse, because an interactive warehouse rejects DML such as the \u003Ccode\u003EINSERT\u003C/code\u003E statements in the next steps with \u003Ccode\u003ECannot run statement type 'INSERT' on an interactive warehouse\u003C/code\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EInteractive warehouses enforce a \u003Cstrong\u003E5-second query timeout\u003C/strong\u003E and a \u003Cstrong\u003E24-hour minimum auto-suspend\u003C/strong\u003E. The \u003Ccode\u003EFALLBACK_WAREHOUSE\u003C/code\u003E setting re-runs any query that exceeds the timeout on the standard warehouse instead of failing it, which mainly matters while the cache is still cold right after a resume. Keep dashboard queries selective (narrow projections, filters aligned with the clustering key) for consistent sub-second latency.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EHistory and control tables\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E accumulates every tick so the dashboard can display multi-minute time-series charts and a Region x Category heatmap. \u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E is the command channel between the dashboard and the SPCS service.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE TABLE IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS_HISTORY (\n  ORDER_ID          STRING,\n  ORDER_TS          TIMESTAMP_NTZ,\n  REGION            STRING,\n  PRODUCT_CATEGORY  STRING,\n  QUANTITY          NUMBER,\n  AMOUNT            NUMBER(10,2),\n  TICK_AT           TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n);\n\nCREATE OR REPLACE TABLE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL (\n  is_running      BOOLEAN       NOT NULL DEFAULT FALSE,\n  mode            VARCHAR       NOT NULL DEFAULT 'normal',\n  flash_category  VARCHAR,\n  rate_mult       FLOAT         NOT NULL DEFAULT 1.0,\n  last_active_at  TIMESTAMP_NTZ NOT NULL DEFAULT CURRENT_TIMESTAMP(),\n  total_orders    NUMBER        NOT NULL DEFAULT 0\n);\n\nINSERT INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL\n  (is_running, mode, flash_category, rate_mult, last_active_at, total_orders)\nVALUES (FALSE, 'normal', NULL, 1.0, CURRENT_TIMESTAMP(), 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESimulation stored procedure\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003ESP_SIMULATE_TICK\u003C/code\u003E is the heart of the simulation. The SPCS service calls it every second. In a single round-trip it:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EReplaces the \u003Ccode\u003EORDERS\u003C/code\u003E snapshot with \u003Ccode\u003EINSERT OVERWRITE\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EAppends the new rows to \u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EIncrements the \u003Ccode\u003ETOTAL_ORDERS\u003C/code\u003E counter in \u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EEXECUTE AS OWNER\u003C/code\u003E clause runs the writes with the privileges of the procedure's owner, \u003Ccode\u003EINTERACTIVE_ANALYTICS_ROLE\u003C/code\u003E, so the procedure is the single path that modifies \u003Ccode\u003EORDERS\u003C/code\u003E, \u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E, and \u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE INTERACTIVE_STREAMING_DEMO.PUBLIC.SP_SIMULATE_TICK(\n    MODE           VARCHAR,\n    FLASH_CATEGORY VARCHAR,\n    NUM_ROWS       FLOAT,\n    DOMINANT       VARCHAR,\n    DOM_PCT        FLOAT\n)\nRETURNS VARCHAR\nLANGUAGE JAVASCRIPT\nEXECUTE AS OWNER\nAS\n$$\n    var flash   = FLASH_CATEGORY || null;\n    var numRows = Math.floor(NUM_ROWS);\n\n    // ---- Tick categories ----\n    // Pick 2-5 categories randomly for this tick.\n    // If a Flash Sale is active, the promoted category is always included.\n    var all_cats = ['Electronics','Apparel','Home','Beauty','Sports','Toys'];\n    for (var i = all_cats.length - 1; i > 0; i--) {   // Fisher-Yates shuffle\n        var j = Math.floor(Math.random() * (i + 1));\n        var tmp = all_cats[i]; all_cats[i] = all_cats[j]; all_cats[j] = tmp;\n    }\n    var n_cats    = Math.floor(Math.random() * 4) + 2;\n    var tick_cats = all_cats.slice(0, n_cats);\n    if (MODE === 'flash_sale' && flash && tick_cats.indexOf(flash) === -1) {\n        tick_cats[tick_cats.length - 1] = flash;\n    }\n\n    var n         = tick_cats.length;\n    var quoted    = tick_cats.map(function(c) { return "'" + c + "'"; });\n    var cat_array = 'ARRAY_CONSTRUCT(' + quoted.join(',') + ')';\n    var cat_idx   = 'UNIFORM(0,' + (n - 1) + ',RANDOM())';\n\n    var cat_sql, amount_sql;\n\n    if (MODE === 'flash_sale' && flash) {\n        // Flash Sale: 50 % of orders go to the promoted category; prices elevated.\n        cat_sql    = "CASE WHEN UNIFORM(0,99,RANDOM()) < 50 THEN '" + flash + "'"\n                   + " ELSE " + cat_array + "[" + cat_idx + "]::STRING END";\n        amount_sql = "CASE WHEN PRODUCT_CATEGORY = '" + flash + "'"\n                   + " THEN UNIFORM(50,800,RANDOM()) ELSE UNIFORM(5,400,RANDOM()) END";\n    } else {\n        // Each region has a preferred product category with a graduated\n        // probability spectrum (28 % down to 8 %), creating a visible\n        // diagonal gradient in the Region x Category heatmap.\n        var rw = {\n            'North America': {'Electronics':28,'Sports':22,'Beauty':18,'Home':14,'Toys':10,'Apparel':8},\n            'Europe':        {'Apparel':28,'Electronics':22,'Beauty':20,'Toys':14,'Home':8,'Sports':8},\n            'APAC':          {'Toys':28,'Home':22,'Electronics':18,'Beauty':14,'Apparel':10,'Sports':8},\n            'LATAM':         {'Home':28,'Apparel':22,'Beauty':18,'Sports':14,'Toys':10,'Electronics':8},\n            'MEA':           {'Beauty':28,'Toys':22,'Home':18,'Apparel':14,'Electronics':10,'Sports':8}\n        };\n        var regs   = ['North America','Europe','APAC','LATAM','MEA'];\n        var rcases = [];\n        for (var ri = 0; ri < regs.length; ri++) {\n            var reg = regs[ri];\n            var w   = rw[reg];\n            var wl  = tick_cats.map(function(c) { return {cat: c, w: w[c] || 8}; });\n            wl.sort(function(a, b) { return b.w - a.w; });\n            var tot = 0;\n            for (var wi = 0; wi < wl.length; wi++) { tot += wl[wi].w; }\n            var cum = 0;\n            var inn = [];\n            for (var k = 0; k < wl.length - 1; k++) {\n                cum += Math.round(wl[k].w * 100 / tot);\n                inn.push('WHEN rv < ' + cum + " THEN '" + wl[k].cat + "'");\n            }\n            inn.push("ELSE '" + wl[wl.length - 1].cat + "'");\n            // One UNIFORM draw per row via scalar subquery for accurate distribution.\n            rcases.push(\n                "WHEN REGION = '" + reg + "' THEN "\n                + '(SELECT CASE ' + inn.join(' ') + ' END'\n                + ' FROM (SELECT UNIFORM(0,99,RANDOM()) AS rv) _t)'\n            );\n        }\n        rcases.push('ELSE ' + cat_array + '[' + cat_idx + ']::STRING');\n        cat_sql    = 'CASE ' + rcases.join(' ') + ' END';\n        amount_sql = 'UNIFORM(5,400,RANDOM())';\n    }\n\n    var sql = [\n        'INSERT OVERWRITE INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS',\n        'SELECT ORDER_ID, ORDER_TS, REGION, PRODUCT_CATEGORY, QUANTITY,',\n        '       ROUND(UNIT_PRICE * QUANTITY, 2) AS AMOUNT',\n        'FROM (',\n        '  SELECT UUID_STRING() AS ORDER_ID,',\n        "    DATEADD('second',-UNIFORM(0,300,RANDOM()),CURRENT_TIMESTAMP())",\n        '      ::TIMESTAMP_NTZ AS ORDER_TS,',\n        "    ARRAY_CONSTRUCT('North America','Europe','APAC','LATAM','MEA')",\n        '      [UNIFORM(0,4,RANDOM())]::STRING AS REGION,',\n        '    ' + cat_sql + ' AS PRODUCT_CATEGORY,',\n        '    UNIFORM(1,5,RANDOM()) AS QUANTITY,',\n        '    ' + amount_sql + ' AS UNIT_PRICE',\n        '  FROM TABLE(GENERATOR(ROWCOUNT => ' + numRows + '))',\n        ')'\n    ];\n    snowflake.execute({sqlText: sql.join(' ')});\n\n    snowflake.execute({sqlText:\n        'INSERT INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS_HISTORY'\n        + ' (ORDER_ID, ORDER_TS, REGION, PRODUCT_CATEGORY, QUANTITY, AMOUNT, TICK_AT)'\n        + ' SELECT ORDER_ID, ORDER_TS, REGION, PRODUCT_CATEGORY, QUANTITY, AMOUNT,'\n        + ' CURRENT_TIMESTAMP() FROM INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS'\n    });\n\n    snowflake.execute({sqlText:\n        'UPDATE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL'\n        + ' SET TOTAL_ORDERS = COALESCE(TOTAL_ORDERS, 0) + ' + numRows\n        + ',    LAST_ACTIVE_AT = CURRENT_TIMESTAMP()'\n    });\n    return 'OK';\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECompute pool and image repository\u003C/h3\u003E\n","\u003Cp\u003EThe SPCS service runs on a compute pool. The image repository is a Snowflake-managed private Docker registry.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE COMPUTE POOL IF NOT EXISTS INTERACTIVE_ANALYTICS_POOL\n  MIN_NODES         = 1\n  MAX_NODES         = 1\n  INSTANCE_FAMILY   = CPU_X64_XS\n  AUTO_RESUME       = TRUE\n  AUTO_SUSPEND_SECS = 3600;\n\nCREATE IMAGE REPOSITORY IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.SIM_REPO;\n\n-- Note the repository_url in the output; you will use it in the next step.\nSHOW IMAGE REPOSITORIES LIKE 'SIM_REPO'\n  IN SCHEMA INTERACTIVE_STREAMING_DEMO.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EApp stage\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE STAGE IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EMaintenance tasks\u003C/h3\u003E\n","\u003Cp\u003EThese tasks run in the background to manage costs. The autostop task is a safety net: if \u003Ccode\u003Eis_running\u003C/code\u003E is still TRUE but no tick has updated \u003Ccode\u003Elast_active_at\u003C/code\u003E for an hour (for example, the service crashed), it sets the flag back to FALSE. It does not stop a healthy simulation, because every tick refreshes \u003Ccode\u003Elast_active_at\u003C/code\u003E, so click \u003Cstrong\u003EStop\u003C/strong\u003E when you finish. The cleanup task deletes history rows older than one day to keep storage costs low.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP\n  SCHEDULE                                 = '15 MINUTE'\n  USER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'XSMALL'\n  COMMENT                                  = 'Auto-stop simulation when idle > 1 h'\nAS\n  UPDATE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL\n  SET is_running = FALSE\n  WHERE is_running = TRUE\n    AND last_active_at < DATEADD('hour', -1, CURRENT_TIMESTAMP());\n\nCREATE OR REPLACE TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP\n  WAREHOUSE = INTERACTIVE_ANALYTICS_WH\n  SCHEDULE  = 'USING CRON 0 2 * * * UTC'\n  COMMENT   = 'Delete ORDERS_HISTORY rows older than 1 day'\nAS\n  DELETE FROM INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS_HISTORY\n  WHERE TICK_AT < DATEADD('day', -1, CURRENT_TIMESTAMP());\n\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP RESUME;\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESeed initial orders\u003C/h3\u003E\n","\u003Cp\u003ESeed \u003Ccode\u003EORDERS\u003C/code\u003E with 2,000 rows so the dashboard renders immediately before the simulation starts.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT OVERWRITE INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS\nSELECT\n  UUID_STRING()                                                       AS ORDER_ID,\n  DATEADD('second', -UNIFORM(0, 1800, RANDOM()), CURRENT_TIMESTAMP())\n    ::TIMESTAMP_NTZ                                                   AS ORDER_TS,\n  ARRAY_CONSTRUCT('North America','Europe','APAC','LATAM','MEA')\n    [UNIFORM(0, 4, RANDOM())]::STRING                                 AS REGION,\n  ARRAY_CONSTRUCT('Electronics','Apparel','Home','Beauty','Sports','Toys')\n    [UNIFORM(0, 5, RANDOM())]::STRING                                 AS PRODUCT_CATEGORY,\n  UNIFORM(1, 5, RANDOM())                                             AS QUANTITY,\n  ROUND(UNIFORM(5, 400, RANDOM()) * UNIFORM(1, 5, RANDOM()), 2)      AS AMOUNT\nFROM TABLE(GENERATOR(ROWCOUNT => 2000));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify the objects\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Cstrong\u003ECatalog > Database Explorer > INTERACTIVE_STREAMING_DEMO > PUBLIC\u003C/strong\u003E in Snowsight. The schema should list eight objects:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/snowsight-schema-objects.png?v=19c87ae3\" alt=\"Snowsight catalog view of the PUBLIC schema listing APP_STAGE, the two INTERACTIVE_ANALYTICS tasks, the ORDERS and ORDERS_HISTORY tables, SIM_REPO, SIMULATION_CONTROL, and SP_SIMULATE_TICK.\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003EORDERS\u003C/code\u003E is listed as a regular \u003Cstrong\u003ETable\u003C/strong\u003E: the interactive warehouse reads it in place, so there is no special table type or extra object to create. The warehouses and the compute pool are account-level objects, so they appear under \u003Cstrong\u003ECompute\u003C/strong\u003E rather than in this list.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Container\u003C/h2\u003E\n","\u003Cp\u003EThe simulation service is a lightweight Python container. It reads \u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E every few ticks and calls \u003Ccode\u003ESP_SIMULATE_TICK\u003C/code\u003E in a tight loop, generating orders every second.\u003C/p\u003E\n","\u003Ch3\u003EHow the service authenticates\u003C/h3\u003E\n","\u003Cp\u003EWhen running inside SPCS, the container automatically receives an OAuth token from Snowflake at \u003Ccode\u003E/snowflake/session/token\u003C/code\u003E. The service reads this token and uses it to connect, so no password or key-pair is needed. The session runs as the service's owner role, \u003Ccode\u003EINTERACTIVE_ANALYTICS_ROLE\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# spcs/simulation_service.py (key excerpt)\nWAREHOUSE = os.environ.get("SNOWFLAKE_WAREHOUSE", "INTERACTIVE_ANALYTICS_WH")\nROLE = os.environ.get("SNOWFLAKE_ROLE", "INTERACTIVE_ANALYTICS_ROLE")\nTOKEN_FILE = "/snowflake/session/token"\n\ndef connect():\n    with open(TOKEN_FILE) as f:\n        token = f.read().strip()\n    return snowflake.connector.connect(\n        host=os.environ["SNOWFLAKE_HOST"],\n        account=os.environ["SNOWFLAKE_ACCOUNT"],\n        authenticator="oauth",\n        token=token,\n        database="INTERACTIVE_STREAMING_DEMO",\n        schema="PUBLIC",\n        role=ROLE,\n        warehouse=WAREHOUSE,\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EGet the registry URL\u003C/h3\u003E\n","\u003Cp\u003ERun the following query and copy the \u003Ccode\u003Erepository_url\u003C/code\u003E value. It looks like:\n\u003Ccode\u003E<orgname>-<accountname>.registry.snowflakecomputing.com/interactive_streaming_demo/public/sim_repo\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW IMAGE REPOSITORIES LIKE 'SIM_REPO'\n  IN SCHEMA INTERACTIVE_STREAMING_DEMO.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELog in to the registry\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker login <your_registry_url>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen prompted, enter your Snowflake username and password (or a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/programmatic-access-tokens\"\u003Eprogrammatic access token\u003C/a\u003E).\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you have the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E installed and a connection configured, \u003Ccode\u003Esnow spcs image-registry login\u003C/code\u003E logs Docker in to your account's registry using that connection, so you don't have to type a password.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EBuild, tag, and push the image\u003C/h3\u003E\n","\u003Cp\u003EFrom the root of the cloned repository:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker build -t simulation ./spcs/\n\ndocker tag simulation \\\n  <your_registry_url>/simulation:latest\n\ndocker push <your_registry_url>/simulation:latest\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EBuilding on Apple Silicon (arm64)? Add \u003Ccode\u003E--platform linux/amd64\u003C/code\u003E to \u003Ccode\u003Edocker build\u003C/code\u003E so the image runs correctly on Snowflake's x86_64 compute pool nodes.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETo confirm the push, open \u003Ccode\u003ESIM_REPO\u003C/code\u003E in Snowsight under \u003Cstrong\u003ECatalog > Database Explorer > INTERACTIVE_STREAMING_DEMO > PUBLIC > Image Repositories\u003C/strong\u003E. The \u003Ccode\u003Esimulation\u003C/code\u003E image should be listed with a fresh timestamp and its \u003Ccode\u003Esha256\u003C/code\u003E digest, which matches the digest that \u003Ccode\u003Edocker push\u003C/code\u003E printed:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/snowsight-image-repository.png?v=19c87ae3\" alt=\"Snowsight view of the SIM_REPO image repository showing the simulation image, created just now, with its sha256 digest.\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy Everything\u003C/h2\u003E\n","\u003Ch3\u003ECreate the SPCS simulation service\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Esimulation_spec.yaml\u003C/code\u003E file in the repository defines the container environment. Create the service from the inline specification below (the image path uses the path relative to your registry, which Snowflake resolves automatically):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n\nCREATE SERVICE IF NOT EXISTS\n  INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_SVC\n  IN COMPUTE POOL INTERACTIVE_ANALYTICS_POOL\n  FROM SPECIFICATION $$\n  spec:\n    container:\n    - name: simulator\n      image: /interactive_streaming_demo/public/sim_repo/simulation:latest\n      env:\n        SNOWFLAKE_WAREHOUSE: INTERACTIVE_ANALYTICS_WH\n        SIM_INTERVAL: "1"\n        IDLE_SUSPEND_SECS: "3600"\n        ROWS_BASE: "100"\n        CTRL_CACHE_TICKS: "5"\n  $$\n  MIN_INSTANCES = 1\n  MAX_INSTANCES = 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait until the status column shows \u003Ccode\u003ERUNNING\u003C/code\u003E (typically 30 to 60 seconds):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW SERVICES LIKE 'INTERACTIVE_ANALYTICS_SVC'\n  IN SCHEMA INTERACTIVE_STREAMING_DEMO.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf the service takes longer or shows errors, inspect the container logs:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$GET_SERVICE_LOGS(\n  'INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_SVC',\n  '0', 'simulator', 50\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA healthy service logs its configuration, connects with the SPCS OAuth token, and resets the idle timer. It then waits quietly until you press \u003Cstrong\u003EStart\u003C/strong\u003E in the dashboard:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EINFO Simulation service starting (interval=1.0s, idle_suspend=3600s, rows_base=100, cache_ticks=5)\nINFO Connecting to GLOBAL Snowflake domain\nINFO Connected to Snowflake as INTERACTIVE_ANALYTICS_ROLE\nINFO Idle timer reset\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce the simulation is running, a \u003Ccode\u003Etick N: ... rows\u003C/code\u003E line appears for every call to \u003Ccode\u003ESP_SIMULATE_TICK\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EUpload the Streamlit dashboard\u003C/h3\u003E\n","\u003Cp\u003ERun these commands from the \u003Ccode\u003Estreamlit/\u003C/code\u003E directory using \u003Cstrong\u003ESnowSQL\u003C/strong\u003E or the \u003Cstrong\u003ESnowflake Python connector\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\n\nPUT file://streamlit_app.py\n    @INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE/streamlit/\n    OVERWRITE=TRUE AUTO_COMPRESS=FALSE;\n\nPUT file://environment.yml\n    @INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE/streamlit/\n    OVERWRITE=TRUE AUTO_COMPRESS=FALSE;\n\nCREATE OR REPLACE STREAMLIT INTERACTIVE_STREAMING_DEMO.PUBLIC.LIVE_ORDERS_DASHBOARD\n  ROOT_LOCATION   = '@INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE/streamlit'\n  MAIN_FILE       = 'streamlit_app.py'\n  QUERY_WAREHOUSE = INTERACTIVE_ANALYTICS_WH\n  TITLE           = 'Live Orders Dashboard';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen the app in Snowsight under \u003Cstrong\u003EProjects > Streamlit > LIVE_ORDERS_DASHBOARD\u003C/strong\u003E. The first launch takes a minute or two while Snowflake builds the Python environment from \u003Ccode\u003Eenvironment.yml\u003C/code\u003E. That file pins \u003Ccode\u003Estreamlit=1.52.2\u003C/code\u003E from the Snowflake Anaconda channel because the dashboard's auto-refresh relies on \u003Ccode\u003Est.fragment(run_every=...)\u003C/code\u003E, which the older default runtime does not support. Leave the pin in place unless you have tested the app on another version.\u003C/p\u003E\n","\u003Cp\u003EBefore you start the simulation, the dashboard looks like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-first-open.png?v=19c87ae3\" alt=\"The Live Orders Dashboard before the simulation starts: the sidebar shows Stopped with an auto-suspend countdown, the KPI tiles read 0, and a banner says the simulation is stopped.\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe sidebar status reads \u003Cstrong\u003EStopped\u003C/strong\u003E, with a countdown to the service's idle auto-suspend. The KPI tiles read zero because \u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E is still empty. The 2,000 rows you seeded live only in \u003Ccode\u003EORDERS\u003C/code\u003E, which holds the current tick snapshot, so they don't count as history. The yellow banner and the empty chart areas both point you to the \u003Cstrong\u003EStart\u003C/strong\u003E button.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore the Dashboard\u003C/h2\u003E\n","\u003Ch3\u003EStart the simulation\u003C/h3\u003E\n","\u003Cp\u003EIn the sidebar, click \u003Cstrong\u003EStart\u003C/strong\u003E in the \u003Cstrong\u003ESimulation\u003C/strong\u003E section. If the SPCS service is suspended, the button reads \u003Cstrong\u003EStart (resumes service)\u003C/strong\u003E and the app resumes the service automatically before enabling the data feed. Within a few seconds the status turns green and reads \u003Cstrong\u003ERunning\u003C/strong\u003E, and the \u003Cstrong\u003EOrders over time\u003C/strong\u003E chart begins filling in as each tick arrives:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-running.png?v=19c87ae3\" alt=\"The dashboard with the simulation running: the sidebar shows Running, the KPI tiles show 25.5K total orders, 4.5K orders in the last 15 minutes, $2.7M revenue, 5 regions and 297 orders per minute, and the Orders over time chart shows a live area series.\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEach point on the chart is a 10-second bucket from \u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E, and the orange dot marks the latest bucket. The flat ramp in the middle of this screenshot is a period when the simulation was stopped and later restarted, which shows that history survives a stop and start.\u003C/p\u003E\n","\u003Ch3\u003EKPI tiles\u003C/h3\u003E\n","\u003Cp\u003EThe top row shows five live metrics:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETotal orders\u003C/strong\u003E: the cumulative order count since the last session reset, read from \u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E. The green badge underneath (for example \u003Cstrong\u003E+20/tick\u003C/strong\u003E) is the size of the current tick, read from the \u003Ccode\u003EORDERS\u003C/code\u003E table through \u003Ccode\u003EINTERACTIVE_ANALYTICS_IWH\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrders (15m)\u003C/strong\u003E: the order count within the selected time window. The label follows the \u003Cstrong\u003ETime window\u003C/strong\u003E setting, and reads \u003Cstrong\u003EOrders (all)\u003C/strong\u003E when the window is \u003Cstrong\u003EAll time\u003C/strong\u003E or the simulation is stopped.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERevenue\u003C/strong\u003E: total revenue within the time window.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERegions\u003C/strong\u003E: the number of regions with orders in the window.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrders/min\u003C/strong\u003E: the average order rate across the window.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELarge values are shortened (for example 25.5K or $2.7M) so the tiles stay readable on narrow screens.\u003C/p\u003E\n","\u003Ch3\u003EAdjust settings\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ESettings\u003C/strong\u003E sidebar section exposes three controls:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETime window\u003C/strong\u003E: how far back the charts look (5 min, 15 min, 30 min, 60 min, or All time).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETick interval\u003C/strong\u003E: how often the dashboard re-queries Snowflake (1 to 30 seconds).\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERate multiplier\u003C/strong\u003E: scales the order volume per tick (0.5× to 3×). Changes take effect within the next tick cycle.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFlash Sale\u003C/h3\u003E\n","\u003Cp\u003ESelect a product category from the \u003Cstrong\u003EFlash Sale category\u003C/strong\u003E dropdown and click \u003Cstrong\u003EStart\u003C/strong\u003E. Half of all new orders go to the selected category, their unit prices are raised to simulate promotional pricing, and each tick generates about 2.5 times as many orders. The sidebar status changes to \u003Cstrong\u003ERunning · Flash Sale: <category>\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-flash-sale.png?v=19c87ae3\" alt=\"The dashboard during a Flash Sale on Sports: the sidebar reads Running, Flash Sale: Sports, the Total orders tile shows a +50/tick badge, and the Orders over time chart jumps from about 80 to about 200 orders per 10-second bucket.\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe jump on the right of the chart is the Flash Sale taking effect: volume rises from roughly 80 to roughly 200 orders per 10-second bucket, and the tick badge goes from \u003Cstrong\u003E+20/tick\u003C/strong\u003E to \u003Cstrong\u003E+50/tick\u003C/strong\u003E. Because the service caches \u003Ccode\u003ESIMULATION_CONTROL\u003C/code\u003E for five ticks, the change can take a few seconds to show. Click \u003Cstrong\u003EStop\u003C/strong\u003E next to the Flash Sale \u003Cstrong\u003EStart\u003C/strong\u003E button to return to normal mode.\u003C/p\u003E\n","\u003Ch3\u003ERegion x Category heatmap\u003C/h3\u003E\n","\u003Cp\u003EThe heatmap shows order volume for every region-category pair, with \u003Cstrong\u003ERevenue by region\u003C/strong\u003E and \u003Cstrong\u003ETop product categories\u003C/strong\u003E side by side below it. Each region has a preferred product category with a graduated probability spectrum, so the preferred category is roughly 3.5 times more common than the least-preferred one. Over a few minutes this produces a visible diagonal of bright cells:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-heatmap-breakdowns.png?v=19c87ae3\" alt=\"The Orders by Region x Category heatmap after a few minutes of normal traffic, with the brightest cells at North America/Electronics, Europe/Apparel, APAC/Toys and MEA/Beauty, above the Revenue by region bar chart and the Top product categories table.\"\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ERegion\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETop category\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENorth America\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EElectronics\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEurope\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EApparel\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAPAC\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EToys\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELATAM\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHome\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMEA\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBeauty\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EIn this run, North America/Electronics (436), Europe/Apparel (392), APAC/Toys (452) and MEA/Beauty (388) are the brightest cells in their columns. LATAM's top two weights are close (Home 28 %, Apparel 22 %), and each tick only draws from a random subset of categories, so over a short window LATAM's second choice can briefly lead. In the \u003Cstrong\u003ETop product categories\u003C/strong\u003E table, categories that appear in the current tick are highlighted in color, and the rest are grey.\u003C/p\u003E\n","\u003Ch3\u003ENew session\u003C/h3\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ENew session\u003C/strong\u003E at the bottom of the \u003Cstrong\u003ESimulation\u003C/strong\u003E sidebar section, then click \u003Cstrong\u003EConfirm\u003C/strong\u003E in the warning that appears. The app stops the simulation, waits a few seconds for in-flight ticks to land, clears \u003Ccode\u003EORDERS_HISTORY\u003C/code\u003E, and resets the total counter to zero. The dashboard returns to the empty, stopped state you saw when you first opened it, which is handy when switching audiences or rerunning the demo from scratch.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003ERemove all resources when finished to stop incurring costs.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWarning:\u003C/strong\u003E Interactive warehouses have a \u003Cstrong\u003E24-hour minimum billing period\u003C/strong\u003E once created. Drop the warehouse as soon as you are done to avoid unexpected charges.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n\n-- Stop the data feed before dropping objects.\nUPDATE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL\n  SET is_running = FALSE;\n\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP SUSPEND;\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP SUSPEND;\n\nDROP SERVICE      IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_SVC;\nDROP STREAMLIT    IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.LIVE_ORDERS_DASHBOARD;\nDROP TASK         IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP;\nDROP TASK         IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP;\nDROP PROCEDURE    IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.SP_SIMULATE_TICK(VARCHAR, VARCHAR, FLOAT, VARCHAR, FLOAT);\nDROP WAREHOUSE    IF EXISTS INTERACTIVE_ANALYTICS_IWH;\nDROP WAREHOUSE    IF EXISTS INTERACTIVE_ANALYTICS_WH;\nDROP COMPUTE POOL IF EXISTS INTERACTIVE_ANALYTICS_POOL;\nDROP DATABASE     IF EXISTS INTERACTIVE_STREAMING_DEMO;\n\n-- Remove the demo role last.\nUSE ROLE ACCOUNTADMIN;\nDROP ROLE         IF EXISTS INTERACTIVE_ANALYTICS_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You built a complete real-time BI dashboard on Snowflake without any external infrastructure. You clustered a standard table for fast time-window queries, served it through an interactive warehouse for sub-second read latency with no data copy, deployed a containerized simulation service on Snowpark Container Services, and wired everything together through a live Streamlit in Snowflake dashboard with full simulation controls.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClustering a standard table on \u003Ccode\u003E(DATE_TRUNC('MINUTE', ORDER_TS), REGION)\u003C/code\u003E for fast "last N minutes by region" aggregations\u003C/li\u003E\u003Cli\u003EQuerying a standard table zero-copy through an interactive warehouse, and adding it to the warehouse to keep its data cache warm for millisecond-latency queries\u003C/li\u003E\u003Cli\u003EDeploying a long-running Python service on SPCS with internal OAuth authentication, with no keys or passwords in the container\u003C/li\u003E\u003Cli\u003ERunning every demo object, including the SPCS service, under a dedicated role instead of \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EUsing \u003Ccode\u003EEXECUTE AS OWNER\u003C/code\u003E so the stored procedure is the single write path for the simulation\u003C/li\u003E\u003Cli\u003EBuilding an auto-refreshing dashboard with \u003Ccode\u003Est.fragment(run_every=...)\u003C/code\u003E that re-queries Snowflake on a configurable timer\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EDocumentation:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive\"\u003ESnowflake interactive analytics\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive-performance\"\u003EPerformance for Snowflake interactive analytics\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview\"\u003ESnowpark Container Services overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-interactive-warehouse\"\u003ECREATE INTERACTIVE WAREHOUSE\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.streamlit.io/develop/api-reference/execution-flow/st.fragment\"\u003Est.fragment: Streamlit docs\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/programmatic-access-tokens\"\u003ESnowflake Programmatic Access Tokens\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAdditional Reading:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/release-notes/2026/other/2026-09-14-interactive-zero-copy-ga\"\u003EZero-copy support for all table formats in interactive warehouses: release note\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/tutorial-1\"\u003ESnowpark Container Services: Build and Deploy guide\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build a Real-Time BI Dashboard with Interactive Warehouses",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nTraditional analytical warehouses are optimized for large batch scans. When a dashboard needs to refresh every few seconds and answer sub-second queries at high concurrency, you need a different architecture. Snowflake's **interactive warehouses** close that gap: they maintain a warm in-memory cache tuned for selective, low-latency reads. They query standard tables directly, with no copy or conversion, an approach called zero-copy interactive analytics.\n\nIn this quickstart, you will build a complete real-time analytics system for a synthetic e-commerce store. A **Snowpark Container Services (SPCS)** container generates orders server-side at a configurable rate, writing each tick into a standard, clustered table through a stored procedure. An **interactive warehouse** keeps that table's cache warm and serves millisecond-latency queries to a **Streamlit in Snowflake** dashboard that refreshes automatically every few seconds.\n\nEverything runs inside a single Snowflake account. No Kafka, no external message bus, no managed services outside Snowflake.\n\n![Architecture diagram showing the data flow between the SPCS simulation service, stored procedure, live orders table, interactive warehouse, history and control tables, maintenance tasks, and the Streamlit dashboard.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/architecture-diagram.png?v=19c87ae3)\n\n\u003C!-- Editable source: quickstart/build-real-time-bi-dashboard-interactive-warehouses/assets/architecture_diagram.py in the companion repo (matplotlib) --\u003E\n\n### What You'll Learn\n- How to cluster a standard table with a key tuned for dashboard queries\n- How to query a standard table through an interactive warehouse at sub-second latency, with no data copy\n- How to build and deploy a long-running Python service on Snowpark Container Services\n- How to run a demo under a dedicated role instead of `ACCOUNTADMIN`\n- How to use `EXECUTE AS OWNER` to give a service a single, narrow write path\n- How to build an auto-refreshing dashboard with `st.fragment` in Streamlit in Snowflake\n\n### What You'll Build\nYou will assemble five tightly integrated Snowflake objects into a single, self-contained real-time analytics system:\n- An SPCS simulation service continuously generates synthetic e-commerce orders and writes them, via a stored procedure, into a clustered standard table\n- An interactive warehouse keeps that table warm for sub-second dashboard queries\n- A history table accumulates every tick for trend charts and heatmaps\n- A control table acts as the command channel between the dashboard and the simulation service\n- A Streamlit in Snowflake app ties everything together, giving you live KPI tiles, auto-refreshing charts, and full simulation controls in one interface\n\n### What You'll Need\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with `ACCOUNTADMIN`, used once to create the demo role. The account must be in a region that supports both [interactive warehouses](https://docs.snowflake.com/en/user-guide/interactive#region-availability) and [Snowpark Container Services](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview#available-regions).\n- [Docker Desktop](https://docs.docker.com/get-docker/) installed and running locally.\n- [Git](https://git-scm.com/) installed.\n- Basic familiarity with SQL and Python.\n\n\u003C!-- ------------------------ --\u003E\n## Clone the Repository\n\nFork and clone the companion repository:\n\n```bash\ngit clone https://github.com/sfc-gh-cnantasenamat/build-real-time-bi-dashboard-interactive-warehouses.git\ncd build-real-time-bi-dashboard-interactive-warehouses\n```\n\nThe repository has the following structure:\n\n```\n├── scripts/\n│   └── setup.sql             # Creates all Snowflake objects in one pass\n├── spcs/\n│   ├── Dockerfile            # Container definition for the simulation service\n│   ├── requirements.txt      # Python dependencies (snowflake-connector-python)\n│   ├── simulation_service.py # Long-running service loop\n│   └── simulation_spec.yaml  # SPCS service specification\n└── streamlit/\n    ├── streamlit_app.py      # Live dashboard application\n    └── environment.yml       # Streamlit in Snowflake Python environment\n```\n\n\u003C!-- ------------------------ --\u003E\n## Create Snowflake Objects\n\nAll setup SQL is in `scripts/setup.sql`. Open a Snowsight worksheet and paste the blocks below, or run the full file in one shot. The script is idempotent, so it is safe to re-run.\n\nHere is a summary of the Snowflake objects you will create:\n\n| Object | Type | Purpose |\n|---|---|---|\n| `INTERACTIVE_ANALYTICS_ROLE` | Role | Owns every object below, including the service |\n| `ORDERS` | Table (clustered) | Current tick snapshot of live orders |\n| `ORDERS_HISTORY` | Table | Accumulates all historical ticks for charts and heatmaps |\n| `SIMULATION_CONTROL` | Table | Command channel between the dashboard and the simulation service |\n| `SP_SIMULATE_TICK` | Stored Procedure | Generates synthetic orders each tick |\n| `INTERACTIVE_ANALYTICS_WH` | Virtual Warehouse | DDL, task execution, and general queries |\n| `INTERACTIVE_ANALYTICS_IWH` | Interactive Warehouse | Sub-second query cache for the ORDERS table |\n| `INTERACTIVE_ANALYTICS_POOL` | Compute Pool | SPCS node that runs the simulation container |\n| `INTERACTIVE_ANALYTICS_SVC` | SPCS Service | Long-running simulation service |\n| `INTERACTIVE_ANALYTICS_AUTOSTOP` | Task (serverless) | Clears a stale running flag when no ticks have landed for 1 hour |\n| `INTERACTIVE_ANALYTICS_HISTORY_CLEANUP` | Task (warehouse) | Purges ORDERS_HISTORY rows older than 1 day |\n| `LIVE_ORDERS_DASHBOARD` | Streamlit in Snowflake | Live dashboard with KPI tiles, charts, and simulation controls |\n\n### Demo role\n\n`ACCOUNTADMIN` is used only for this block. It creates `INTERACTIVE_ANALYTICS_ROLE` and grants it the account-level privileges it needs to create its own database, warehouses, compute pool, and tasks. Every other object in this guide is created and owned by that role, so the long-running SPCS service never holds an `ACCOUNTADMIN` session.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE ROLE IF NOT EXISTS INTERACTIVE_ANALYTICS_ROLE\n  COMMENT = 'Owns all objects for the real-time BI dashboard demo';\n\nGRANT CREATE DATABASE, CREATE WAREHOUSE, CREATE COMPUTE POOL,\n      EXECUTE TASK, EXECUTE MANAGED TASK\n  ON ACCOUNT TO ROLE INTERACTIVE_ANALYTICS_ROLE;\n\nGRANT ROLE INTERACTIVE_ANALYTICS_ROLE TO ROLE SYSADMIN;\n\nSET MY_USER = CURRENT_USER();\nGRANT ROLE INTERACTIVE_ANALYTICS_ROLE TO USER IDENTIFIER($MY_USER);\n```\n\n### Database and schema\n\n```sql\nUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\n\nCREATE DATABASE IF NOT EXISTS INTERACTIVE_STREAMING_DEMO;\nCREATE SCHEMA   IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC;\n```\n\n### Standard warehouse\n\nThe standard warehouse handles DDL, stored procedure execution, task runs, and the longer history queries. Interactive warehouses reject DML and `CALL` statements and cap queries at 5 seconds, so every write goes through this separate standard warehouse.\n\n```sql\nCREATE WAREHOUSE IF NOT EXISTS INTERACTIVE_ANALYTICS_WH\n  WAREHOUSE_SIZE      = 'XSMALL'\n  AUTO_SUSPEND        = 60\n  AUTO_RESUME         = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n```\n\n### Orders table\n\n`ORDERS` holds the current tick snapshot: only the most recently generated set of orders. It is a regular table, because interactive warehouses query standard tables directly with no data copy or conversion. The `CLUSTER BY` key matches the dashboard's `WHERE` clause: a recent time window filtered by region. Truncating the timestamp to the minute keeps the clustering key low-cardinality and prunes efficiently for \"last N minutes\" queries.\n\n```sql\nCREATE OR REPLACE TABLE INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS (\n  ORDER_ID          STRING,\n  ORDER_TS          TIMESTAMP_NTZ,\n  REGION            STRING,\n  PRODUCT_CATEGORY  STRING,\n  QUANTITY          INT,\n  AMOUNT            NUMBER(10,2)\n)\nCLUSTER BY (DATE_TRUNC('MINUTE', ORDER_TS), REGION);\n```\n\n### Interactive warehouse\n\nCreate the interactive warehouse and add `ORDERS` to it. Adding a table is optional, since an interactive warehouse can query any table, but it tells the warehouse to warm that table's data cache proactively instead of on the first dashboard query.\n\n```sql\nCREATE INTERACTIVE WAREHOUSE IF NOT EXISTS INTERACTIVE_ANALYTICS_IWH\n  WAREHOUSE_SIZE      = 'XSMALL'\n  AUTO_RESUME         = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nALTER WAREHOUSE INTERACTIVE_ANALYTICS_IWH RESUME IF SUSPENDED;\n\nALTER WAREHOUSE INTERACTIVE_ANALYTICS_IWH\n  ADD TABLES (INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS);\n\nALTER WAREHOUSE INTERACTIVE_ANALYTICS_IWH\n  SET FALLBACK_WAREHOUSE = INTERACTIVE_ANALYTICS_WH;\n\n-- Switch back: interactive warehouses cannot run INSERT, UPDATE or DELETE.\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n```\n\nCreating a warehouse makes it the current warehouse for your session. The final `USE WAREHOUSE` switches back to the standard warehouse, because an interactive warehouse rejects DML such as the `INSERT` statements in the next steps with `Cannot run statement type 'INSERT' on an interactive warehouse`.\n\n\u003E Interactive warehouses enforce a **5-second query timeout** and a **24-hour minimum auto-suspend**. The `FALLBACK_WAREHOUSE` setting re-runs any query that exceeds the timeout on the standard warehouse instead of failing it, which mainly matters while the cache is still cold right after a resume. Keep dashboard queries selective (narrow projections, filters aligned with the clustering key) for consistent sub-second latency.\n\n### History and control tables\n\n`ORDERS_HISTORY` accumulates every tick so the dashboard can display multi-minute time-series charts and a Region x Category heatmap. `SIMULATION_CONTROL` is the command channel between the dashboard and the SPCS service.\n\n```sql\nCREATE TABLE IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS_HISTORY (\n  ORDER_ID          STRING,\n  ORDER_TS          TIMESTAMP_NTZ,\n  REGION            STRING,\n  PRODUCT_CATEGORY  STRING,\n  QUANTITY          NUMBER,\n  AMOUNT            NUMBER(10,2),\n  TICK_AT           TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP()\n);\n\nCREATE OR REPLACE TABLE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL (\n  is_running      BOOLEAN       NOT NULL DEFAULT FALSE,\n  mode            VARCHAR       NOT NULL DEFAULT 'normal',\n  flash_category  VARCHAR,\n  rate_mult       FLOAT         NOT NULL DEFAULT 1.0,\n  last_active_at  TIMESTAMP_NTZ NOT NULL DEFAULT CURRENT_TIMESTAMP(),\n  total_orders    NUMBER        NOT NULL DEFAULT 0\n);\n\nINSERT INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL\n  (is_running, mode, flash_category, rate_mult, last_active_at, total_orders)\nVALUES (FALSE, 'normal', NULL, 1.0, CURRENT_TIMESTAMP(), 0);\n```\n\n### Simulation stored procedure\n\n`SP_SIMULATE_TICK` is the heart of the simulation. The SPCS service calls it every second. In a single round-trip it:\n\n1. Replaces the `ORDERS` snapshot with `INSERT OVERWRITE`.\n2. Appends the new rows to `ORDERS_HISTORY`.\n3. Increments the `TOTAL_ORDERS` counter in `SIMULATION_CONTROL`.\n\nThe `EXECUTE AS OWNER` clause runs the writes with the privileges of the procedure's owner, `INTERACTIVE_ANALYTICS_ROLE`, so the procedure is the single path that modifies `ORDERS`, `ORDERS_HISTORY`, and `SIMULATION_CONTROL`.\n\n```sql\nCREATE OR REPLACE PROCEDURE INTERACTIVE_STREAMING_DEMO.PUBLIC.SP_SIMULATE_TICK(\n    MODE           VARCHAR,\n    FLASH_CATEGORY VARCHAR,\n    NUM_ROWS       FLOAT,\n    DOMINANT       VARCHAR,\n    DOM_PCT        FLOAT\n)\nRETURNS VARCHAR\nLANGUAGE JAVASCRIPT\nEXECUTE AS OWNER\nAS\n$$\n    var flash   = FLASH_CATEGORY || null;\n    var numRows = Math.floor(NUM_ROWS);\n\n    // ---- Tick categories ----\n    // Pick 2-5 categories randomly for this tick.\n    // If a Flash Sale is active, the promoted category is always included.\n    var all_cats = ['Electronics','Apparel','Home','Beauty','Sports','Toys'];\n    for (var i = all_cats.length - 1; i \u003E 0; i--) {   // Fisher-Yates shuffle\n        var j = Math.floor(Math.random() * (i + 1));\n        var tmp = all_cats[i]; all_cats[i] = all_cats[j]; all_cats[j] = tmp;\n    }\n    var n_cats    = Math.floor(Math.random() * 4) + 2;\n    var tick_cats = all_cats.slice(0, n_cats);\n    if (MODE === 'flash_sale' && flash && tick_cats.indexOf(flash) === -1) {\n        tick_cats[tick_cats.length - 1] = flash;\n    }\n\n    var n         = tick_cats.length;\n    var quoted    = tick_cats.map(function(c) { return \"'\" + c + \"'\"; });\n    var cat_array = 'ARRAY_CONSTRUCT(' + quoted.join(',') + ')';\n    var cat_idx   = 'UNIFORM(0,' + (n - 1) + ',RANDOM())';\n\n    var cat_sql, amount_sql;\n\n    if (MODE === 'flash_sale' && flash) {\n        // Flash Sale: 50 % of orders go to the promoted category; prices elevated.\n        cat_sql    = \"CASE WHEN UNIFORM(0,99,RANDOM()) \u003C 50 THEN '\" + flash + \"'\"\n                   + \" ELSE \" + cat_array + \"[\" + cat_idx + \"]::STRING END\";\n        amount_sql = \"CASE WHEN PRODUCT_CATEGORY = '\" + flash + \"'\"\n                   + \" THEN UNIFORM(50,800,RANDOM()) ELSE UNIFORM(5,400,RANDOM()) END\";\n    } else {\n        // Each region has a preferred product category with a graduated\n        // probability spectrum (28 % down to 8 %), creating a visible\n        // diagonal gradient in the Region x Category heatmap.\n        var rw = {\n            'North America': {'Electronics':28,'Sports':22,'Beauty':18,'Home':14,'Toys':10,'Apparel':8},\n            'Europe':        {'Apparel':28,'Electronics':22,'Beauty':20,'Toys':14,'Home':8,'Sports':8},\n            'APAC':          {'Toys':28,'Home':22,'Electronics':18,'Beauty':14,'Apparel':10,'Sports':8},\n            'LATAM':         {'Home':28,'Apparel':22,'Beauty':18,'Sports':14,'Toys':10,'Electronics':8},\n            'MEA':           {'Beauty':28,'Toys':22,'Home':18,'Apparel':14,'Electronics':10,'Sports':8}\n        };\n        var regs   = ['North America','Europe','APAC','LATAM','MEA'];\n        var rcases = [];\n        for (var ri = 0; ri \u003C regs.length; ri++) {\n            var reg = regs[ri];\n            var w   = rw[reg];\n            var wl  = tick_cats.map(function(c) { return {cat: c, w: w[c] || 8}; });\n            wl.sort(function(a, b) { return b.w - a.w; });\n            var tot = 0;\n            for (var wi = 0; wi \u003C wl.length; wi++) { tot += wl[wi].w; }\n            var cum = 0;\n            var inn = [];\n            for (var k = 0; k \u003C wl.length - 1; k++) {\n                cum += Math.round(wl[k].w * 100 / tot);\n                inn.push('WHEN rv \u003C ' + cum + \" THEN '\" + wl[k].cat + \"'\");\n            }\n            inn.push(\"ELSE '\" + wl[wl.length - 1].cat + \"'\");\n            // One UNIFORM draw per row via scalar subquery for accurate distribution.\n            rcases.push(\n                \"WHEN REGION = '\" + reg + \"' THEN \"\n                + '(SELECT CASE ' + inn.join(' ') + ' END'\n                + ' FROM (SELECT UNIFORM(0,99,RANDOM()) AS rv) _t)'\n            );\n        }\n        rcases.push('ELSE ' + cat_array + '[' + cat_idx + ']::STRING');\n        cat_sql    = 'CASE ' + rcases.join(' ') + ' END';\n        amount_sql = 'UNIFORM(5,400,RANDOM())';\n    }\n\n    var sql = [\n        'INSERT OVERWRITE INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS',\n        'SELECT ORDER_ID, ORDER_TS, REGION, PRODUCT_CATEGORY, QUANTITY,',\n        '       ROUND(UNIT_PRICE * QUANTITY, 2) AS AMOUNT',\n        'FROM (',\n        '  SELECT UUID_STRING() AS ORDER_ID,',\n        \"    DATEADD('second',-UNIFORM(0,300,RANDOM()),CURRENT_TIMESTAMP())\",\n        '      ::TIMESTAMP_NTZ AS ORDER_TS,',\n        \"    ARRAY_CONSTRUCT('North America','Europe','APAC','LATAM','MEA')\",\n        '      [UNIFORM(0,4,RANDOM())]::STRING AS REGION,',\n        '    ' + cat_sql + ' AS PRODUCT_CATEGORY,',\n        '    UNIFORM(1,5,RANDOM()) AS QUANTITY,',\n        '    ' + amount_sql + ' AS UNIT_PRICE',\n        '  FROM TABLE(GENERATOR(ROWCOUNT =\u003E ' + numRows + '))',\n        ')'\n    ];\n    snowflake.execute({sqlText: sql.join(' ')});\n\n    snowflake.execute({sqlText:\n        'INSERT INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS_HISTORY'\n        + ' (ORDER_ID, ORDER_TS, REGION, PRODUCT_CATEGORY, QUANTITY, AMOUNT, TICK_AT)'\n        + ' SELECT ORDER_ID, ORDER_TS, REGION, PRODUCT_CATEGORY, QUANTITY, AMOUNT,'\n        + ' CURRENT_TIMESTAMP() FROM INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS'\n    });\n\n    snowflake.execute({sqlText:\n        'UPDATE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL'\n        + ' SET TOTAL_ORDERS = COALESCE(TOTAL_ORDERS, 0) + ' + numRows\n        + ',    LAST_ACTIVE_AT = CURRENT_TIMESTAMP()'\n    });\n    return 'OK';\n$$;\n```\n\n### Compute pool and image repository\n\nThe SPCS service runs on a compute pool. The image repository is a Snowflake-managed private Docker registry.\n\n```sql\nCREATE COMPUTE POOL IF NOT EXISTS INTERACTIVE_ANALYTICS_POOL\n  MIN_NODES         = 1\n  MAX_NODES         = 1\n  INSTANCE_FAMILY   = CPU_X64_XS\n  AUTO_RESUME       = TRUE\n  AUTO_SUSPEND_SECS = 3600;\n\nCREATE IMAGE REPOSITORY IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.SIM_REPO;\n\n-- Note the repository_url in the output; you will use it in the next step.\nSHOW IMAGE REPOSITORIES LIKE 'SIM_REPO'\n  IN SCHEMA INTERACTIVE_STREAMING_DEMO.PUBLIC;\n```\n\n### App stage\n\n```sql\nCREATE STAGE IF NOT EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE;\n```\n\n### Maintenance tasks\n\nThese tasks run in the background to manage costs. The autostop task is a safety net: if `is_running` is still TRUE but no tick has updated `last_active_at` for an hour (for example, the service crashed), it sets the flag back to FALSE. It does not stop a healthy simulation, because every tick refreshes `last_active_at`, so click **Stop** when you finish. The cleanup task deletes history rows older than one day to keep storage costs low.\n\n```sql\nCREATE OR REPLACE TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP\n  SCHEDULE                                 = '15 MINUTE'\n  USER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE = 'XSMALL'\n  COMMENT                                  = 'Auto-stop simulation when idle \u003E 1 h'\nAS\n  UPDATE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL\n  SET is_running = FALSE\n  WHERE is_running = TRUE\n    AND last_active_at \u003C DATEADD('hour', -1, CURRENT_TIMESTAMP());\n\nCREATE OR REPLACE TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP\n  WAREHOUSE = INTERACTIVE_ANALYTICS_WH\n  SCHEDULE  = 'USING CRON 0 2 * * * UTC'\n  COMMENT   = 'Delete ORDERS_HISTORY rows older than 1 day'\nAS\n  DELETE FROM INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS_HISTORY\n  WHERE TICK_AT \u003C DATEADD('day', -1, CURRENT_TIMESTAMP());\n\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP RESUME;\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP RESUME;\n```\n\n### Seed initial orders\n\nSeed `ORDERS` with 2,000 rows so the dashboard renders immediately before the simulation starts.\n\n```sql\nINSERT OVERWRITE INTO INTERACTIVE_STREAMING_DEMO.PUBLIC.ORDERS\nSELECT\n  UUID_STRING()                                                       AS ORDER_ID,\n  DATEADD('second', -UNIFORM(0, 1800, RANDOM()), CURRENT_TIMESTAMP())\n    ::TIMESTAMP_NTZ                                                   AS ORDER_TS,\n  ARRAY_CONSTRUCT('North America','Europe','APAC','LATAM','MEA')\n    [UNIFORM(0, 4, RANDOM())]::STRING                                 AS REGION,\n  ARRAY_CONSTRUCT('Electronics','Apparel','Home','Beauty','Sports','Toys')\n    [UNIFORM(0, 5, RANDOM())]::STRING                                 AS PRODUCT_CATEGORY,\n  UNIFORM(1, 5, RANDOM())                                             AS QUANTITY,\n  ROUND(UNIFORM(5, 400, RANDOM()) * UNIFORM(1, 5, RANDOM()), 2)      AS AMOUNT\nFROM TABLE(GENERATOR(ROWCOUNT =\u003E 2000));\n```\n\n### Verify the objects\n\nOpen **Catalog \u003E Database Explorer \u003E INTERACTIVE_STREAMING_DEMO \u003E PUBLIC** in Snowsight. The schema should list eight objects:\n\n![Snowsight catalog view of the PUBLIC schema listing APP_STAGE, the two INTERACTIVE_ANALYTICS tasks, the ORDERS and ORDERS_HISTORY tables, SIM_REPO, SIMULATION_CONTROL, and SP_SIMULATE_TICK.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/snowsight-schema-objects.png?v=19c87ae3)\n\n`ORDERS` is listed as a regular **Table**: the interactive warehouse reads it in place, so there is no special table type or extra object to create. The warehouses and the compute pool are account-level objects, so they appear under **Compute** rather than in this list.\n\n\u003C!-- ------------------------ --\u003E\n## Build the Container\n\nThe simulation service is a lightweight Python container. It reads `SIMULATION_CONTROL` every few ticks and calls `SP_SIMULATE_TICK` in a tight loop, generating orders every second.\n\n### How the service authenticates\n\nWhen running inside SPCS, the container automatically receives an OAuth token from Snowflake at `/snowflake/session/token`. The service reads this token and uses it to connect, so no password or key-pair is needed. The session runs as the service's owner role, `INTERACTIVE_ANALYTICS_ROLE`.\n\n```python\n# spcs/simulation_service.py (key excerpt)\nWAREHOUSE = os.environ.get(\"SNOWFLAKE_WAREHOUSE\", \"INTERACTIVE_ANALYTICS_WH\")\nROLE = os.environ.get(\"SNOWFLAKE_ROLE\", \"INTERACTIVE_ANALYTICS_ROLE\")\nTOKEN_FILE = \"/snowflake/session/token\"\n\ndef connect():\n    with open(TOKEN_FILE) as f:\n        token = f.read().strip()\n    return snowflake.connector.connect(\n        host=os.environ[\"SNOWFLAKE_HOST\"],\n        account=os.environ[\"SNOWFLAKE_ACCOUNT\"],\n        authenticator=\"oauth\",\n        token=token,\n        database=\"INTERACTIVE_STREAMING_DEMO\",\n        schema=\"PUBLIC\",\n        role=ROLE,\n        warehouse=WAREHOUSE,\n    )\n```\n\n### Get the registry URL\n\nRun the following query and copy the `repository_url` value. It looks like:\n`\u003Corgname\u003E-\u003Caccountname\u003E.registry.snowflakecomputing.com/interactive_streaming_demo/public/sim_repo`\n\n```sql\nSHOW IMAGE REPOSITORIES LIKE 'SIM_REPO'\n  IN SCHEMA INTERACTIVE_STREAMING_DEMO.PUBLIC;\n```\n\n### Log in to the registry\n\n```bash\ndocker login \u003Cyour_registry_url\u003E\n```\n\nWhen prompted, enter your Snowflake username and password (or a [programmatic access token](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens)).\n\n\u003E If you have the [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index) installed and a connection configured, `snow spcs image-registry login` logs Docker in to your account's registry using that connection, so you don't have to type a password.\n\n### Build, tag, and push the image\n\nFrom the root of the cloned repository:\n\n```bash\ndocker build -t simulation ./spcs/\n\ndocker tag simulation \\\n  \u003Cyour_registry_url\u003E/simulation:latest\n\ndocker push \u003Cyour_registry_url\u003E/simulation:latest\n```\n\n\u003E Building on Apple Silicon (arm64)? Add `--platform linux/amd64` to `docker build` so the image runs correctly on Snowflake's x86_64 compute pool nodes.\n\nTo confirm the push, open `SIM_REPO` in Snowsight under **Catalog \u003E Database Explorer \u003E INTERACTIVE_STREAMING_DEMO \u003E PUBLIC \u003E Image Repositories**. The `simulation` image should be listed with a fresh timestamp and its `sha256` digest, which matches the digest that `docker push` printed:\n\n![Snowsight view of the SIM_REPO image repository showing the simulation image, created just now, with its sha256 digest.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/snowsight-image-repository.png?v=19c87ae3)\n\n\u003C!-- ------------------------ --\u003E\n## Deploy Everything\n\n### Create the SPCS simulation service\n\nThe `simulation_spec.yaml` file in the repository defines the container environment. Create the service from the inline specification below (the image path uses the path relative to your registry, which Snowflake resolves automatically):\n\n```sql\nUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n\nCREATE SERVICE IF NOT EXISTS\n  INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_SVC\n  IN COMPUTE POOL INTERACTIVE_ANALYTICS_POOL\n  FROM SPECIFICATION $$\n  spec:\n    container:\n    - name: simulator\n      image: /interactive_streaming_demo/public/sim_repo/simulation:latest\n      env:\n        SNOWFLAKE_WAREHOUSE: INTERACTIVE_ANALYTICS_WH\n        SIM_INTERVAL: \"1\"\n        IDLE_SUSPEND_SECS: \"3600\"\n        ROWS_BASE: \"100\"\n        CTRL_CACHE_TICKS: \"5\"\n  $$\n  MIN_INSTANCES = 1\n  MAX_INSTANCES = 1;\n```\n\nWait until the status column shows `RUNNING` (typically 30 to 60 seconds):\n\n```sql\nSHOW SERVICES LIKE 'INTERACTIVE_ANALYTICS_SVC'\n  IN SCHEMA INTERACTIVE_STREAMING_DEMO.PUBLIC;\n```\n\nIf the service takes longer or shows errors, inspect the container logs:\n\n```sql\nSELECT SYSTEM$GET_SERVICE_LOGS(\n  'INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_SVC',\n  '0', 'simulator', 50\n);\n```\n\nA healthy service logs its configuration, connects with the SPCS OAuth token, and resets the idle timer. It then waits quietly until you press **Start** in the dashboard:\n\n```\nINFO Simulation service starting (interval=1.0s, idle_suspend=3600s, rows_base=100, cache_ticks=5)\nINFO Connecting to GLOBAL Snowflake domain\nINFO Connected to Snowflake as INTERACTIVE_ANALYTICS_ROLE\nINFO Idle timer reset\n```\n\nOnce the simulation is running, a `tick N: ... rows` line appears for every call to `SP_SIMULATE_TICK`.\n\n### Upload the Streamlit dashboard\n\nRun these commands from the `streamlit/` directory using **SnowSQL** or the **Snowflake Python connector**:\n\n```sql\nUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\n\nPUT file://streamlit_app.py\n    @INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE/streamlit/\n    OVERWRITE=TRUE AUTO_COMPRESS=FALSE;\n\nPUT file://environment.yml\n    @INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE/streamlit/\n    OVERWRITE=TRUE AUTO_COMPRESS=FALSE;\n\nCREATE OR REPLACE STREAMLIT INTERACTIVE_STREAMING_DEMO.PUBLIC.LIVE_ORDERS_DASHBOARD\n  ROOT_LOCATION   = '@INTERACTIVE_STREAMING_DEMO.PUBLIC.APP_STAGE/streamlit'\n  MAIN_FILE       = 'streamlit_app.py'\n  QUERY_WAREHOUSE = INTERACTIVE_ANALYTICS_WH\n  TITLE           = 'Live Orders Dashboard';\n```\n\nOpen the app in Snowsight under **Projects \u003E Streamlit \u003E LIVE_ORDERS_DASHBOARD**. The first launch takes a minute or two while Snowflake builds the Python environment from `environment.yml`. That file pins `streamlit=1.52.2` from the Snowflake Anaconda channel because the dashboard's auto-refresh relies on `st.fragment(run_every=...)`, which the older default runtime does not support. Leave the pin in place unless you have tested the app on another version.\n\nBefore you start the simulation, the dashboard looks like this:\n\n![The Live Orders Dashboard before the simulation starts: the sidebar shows Stopped with an auto-suspend countdown, the KPI tiles read 0, and a banner says the simulation is stopped.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-first-open.png?v=19c87ae3)\n\nThe sidebar status reads **Stopped**, with a countdown to the service's idle auto-suspend. The KPI tiles read zero because `ORDERS_HISTORY` is still empty. The 2,000 rows you seeded live only in `ORDERS`, which holds the current tick snapshot, so they don't count as history. The yellow banner and the empty chart areas both point you to the **Start** button.\n\n\u003C!-- ------------------------ --\u003E\n## Explore the Dashboard\n\n### Start the simulation\n\nIn the sidebar, click **Start** in the **Simulation** section. If the SPCS service is suspended, the button reads **Start (resumes service)** and the app resumes the service automatically before enabling the data feed. Within a few seconds the status turns green and reads **Running**, and the **Orders over time** chart begins filling in as each tick arrives:\n\n![The dashboard with the simulation running: the sidebar shows Running, the KPI tiles show 25.5K total orders, 4.5K orders in the last 15 minutes, $2.7M revenue, 5 regions and 297 orders per minute, and the Orders over time chart shows a live area series.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-running.png?v=19c87ae3)\n\nEach point on the chart is a 10-second bucket from `ORDERS_HISTORY`, and the orange dot marks the latest bucket. The flat ramp in the middle of this screenshot is a period when the simulation was stopped and later restarted, which shows that history survives a stop and start.\n\n### KPI tiles\n\nThe top row shows five live metrics:\n\n- **Total orders**: the cumulative order count since the last session reset, read from `SIMULATION_CONTROL`. The green badge underneath (for example **+20/tick**) is the size of the current tick, read from the `ORDERS` table through `INTERACTIVE_ANALYTICS_IWH`.\n- **Orders (15m)**: the order count within the selected time window. The label follows the **Time window** setting, and reads **Orders (all)** when the window is **All time** or the simulation is stopped.\n- **Revenue**: total revenue within the time window.\n- **Regions**: the number of regions with orders in the window.\n- **Orders/min**: the average order rate across the window.\n\nLarge values are shortened (for example 25.5K or $2.7M) so the tiles stay readable on narrow screens.\n\n### Adjust settings\n\nThe **Settings** sidebar section exposes three controls:\n\n- **Time window**: how far back the charts look (5 min, 15 min, 30 min, 60 min, or All time).\n- **Tick interval**: how often the dashboard re-queries Snowflake (1 to 30 seconds).\n- **Rate multiplier**: scales the order volume per tick (0.5× to 3×). Changes take effect within the next tick cycle.\n\n### Flash Sale\n\nSelect a product category from the **Flash Sale category** dropdown and click **Start**. Half of all new orders go to the selected category, their unit prices are raised to simulate promotional pricing, and each tick generates about 2.5 times as many orders. The sidebar status changes to **Running · Flash Sale: \u003Ccategory\u003E**:\n\n![The dashboard during a Flash Sale on Sports: the sidebar reads Running, Flash Sale: Sports, the Total orders tile shows a +50/tick badge, and the Orders over time chart jumps from about 80 to about 200 orders per 10-second bucket.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-flash-sale.png?v=19c87ae3)\n\nThe jump on the right of the chart is the Flash Sale taking effect: volume rises from roughly 80 to roughly 200 orders per 10-second bucket, and the tick badge goes from **+20/tick** to **+50/tick**. Because the service caches `SIMULATION_CONTROL` for five ticks, the change can take a few seconds to show. Click **Stop** next to the Flash Sale **Start** button to return to normal mode.\n\n### Region x Category heatmap\n\nThe heatmap shows order volume for every region-category pair, with **Revenue by region** and **Top product categories** side by side below it. Each region has a preferred product category with a graduated probability spectrum, so the preferred category is roughly 3.5 times more common than the least-preferred one. Over a few minutes this produces a visible diagonal of bright cells:\n\n![The Orders by Region x Category heatmap after a few minutes of normal traffic, with the brightest cells at North America/Electronics, Europe/Apparel, APAC/Toys and MEA/Beauty, above the Revenue by region bar chart and the Top product categories table.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-real-time-bi-dashboard-interactive-warehouses/dashboard-heatmap-breakdowns.png?v=19c87ae3)\n\n| Region | Top category |\n|---|---|\n| North America | Electronics |\n| Europe | Apparel |\n| APAC | Toys |\n| LATAM | Home |\n| MEA | Beauty |\n\nIn this run, North America/Electronics (436), Europe/Apparel (392), APAC/Toys (452) and MEA/Beauty (388) are the brightest cells in their columns. LATAM's top two weights are close (Home 28 %, Apparel 22 %), and each tick only draws from a random subset of categories, so over a short window LATAM's second choice can briefly lead. In the **Top product categories** table, categories that appear in the current tick are highlighted in color, and the rest are grey.\n\n### New session\n\nClick **New session** at the bottom of the **Simulation** sidebar section, then click **Confirm** in the warning that appears. The app stops the simulation, waits a few seconds for in-flight ticks to land, clears `ORDERS_HISTORY`, and resets the total counter to zero. The dashboard returns to the empty, stopped state you saw when you first opened it, which is handy when switching audiences or rerunning the demo from scratch.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nRemove all resources when finished to stop incurring costs.\n\n\u003E **Warning:** Interactive warehouses have a **24-hour minimum billing period** once created. Drop the warehouse as soon as you are done to avoid unexpected charges.\n\n```sql\nUSE ROLE INTERACTIVE_ANALYTICS_ROLE;\nUSE WAREHOUSE INTERACTIVE_ANALYTICS_WH;\n\n-- Stop the data feed before dropping objects.\nUPDATE INTERACTIVE_STREAMING_DEMO.PUBLIC.SIMULATION_CONTROL\n  SET is_running = FALSE;\n\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP SUSPEND;\nALTER TASK INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP SUSPEND;\n\nDROP SERVICE      IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_SVC;\nDROP STREAMLIT    IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.LIVE_ORDERS_DASHBOARD;\nDROP TASK         IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_AUTOSTOP;\nDROP TASK         IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.INTERACTIVE_ANALYTICS_HISTORY_CLEANUP;\nDROP PROCEDURE    IF EXISTS INTERACTIVE_STREAMING_DEMO.PUBLIC.SP_SIMULATE_TICK(VARCHAR, VARCHAR, FLOAT, VARCHAR, FLOAT);\nDROP WAREHOUSE    IF EXISTS INTERACTIVE_ANALYTICS_IWH;\nDROP WAREHOUSE    IF EXISTS INTERACTIVE_ANALYTICS_WH;\nDROP COMPUTE POOL IF EXISTS INTERACTIVE_ANALYTICS_POOL;\nDROP DATABASE     IF EXISTS INTERACTIVE_STREAMING_DEMO;\n\n-- Remove the demo role last.\nUSE ROLE ACCOUNTADMIN;\nDROP ROLE         IF EXISTS INTERACTIVE_ANALYTICS_ROLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations! You built a complete real-time BI dashboard on Snowflake without any external infrastructure. You clustered a standard table for fast time-window queries, served it through an interactive warehouse for sub-second read latency with no data copy, deployed a containerized simulation service on Snowpark Container Services, and wired everything together through a live Streamlit in Snowflake dashboard with full simulation controls.\n\n### What You Learned\n- Clustering a standard table on `(DATE_TRUNC('MINUTE', ORDER_TS), REGION)` for fast \"last N minutes by region\" aggregations\n- Querying a standard table zero-copy through an interactive warehouse, and adding it to the warehouse to keep its data cache warm for millisecond-latency queries\n- Deploying a long-running Python service on SPCS with internal OAuth authentication, with no keys or passwords in the container\n- Running every demo object, including the SPCS service, under a dedicated role instead of `ACCOUNTADMIN`\n- Using `EXECUTE AS OWNER` so the stored procedure is the single write path for the simulation\n- Building an auto-refreshing dashboard with `st.fragment(run_every=...)` that re-queries Snowflake on a configurable timer\n\n### Related Resources\n\nDocumentation:\n- [Snowflake interactive analytics](https://docs.snowflake.com/en/user-guide/interactive)\n- [Performance for Snowflake interactive analytics](https://docs.snowflake.com/en/user-guide/interactive-performance)\n- [Snowpark Container Services overview](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/overview)\n- [CREATE INTERACTIVE WAREHOUSE](https://docs.snowflake.com/en/sql-reference/sql/create-interactive-warehouse)\n- [Streamlit in Snowflake](https://docs.snowflake.com/en/developer-guide/streamlit/about-streamlit)\n- [st.fragment: Streamlit docs](https://docs.streamlit.io/develop/api-reference/execution-flow/st.fragment)\n- [Snowflake Programmatic Access Tokens](https://docs.snowflake.com/en/user-guide/programmatic-access-tokens)\n\nAdditional Reading:\n- [Zero-copy support for all table formats in interactive warehouses: release note](https://docs.snowflake.com/en/release-notes/2026/other/2026-09-14-interactive-zero-copy-ga)\n- [Snowpark Container Services: Build and Deploy guide](https://docs.snowflake.com/en/developer-guide/snowpark-container-services/tutorials/tutorial-1)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-fc28da2229","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-4a68178e2a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-c4b5a27121","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-16",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a6736dcc2e","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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