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demand.\u003C/p\u003E\n","\u003Cp\u003ESnowflake's new Interactive Warehouses are designed to deliver on these needs. They provide a high-concurrency, low-latency serving layer for near real-time analytics, and can query your existing standard tables directly through zero-copy interactive analytics, with no data conversion required. This allows consistent, sub-second query performance for live dashboards and APIs with great price-for-performance. With this end-to-end solution, you can avoid operational complexities and tool sprawl.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe core concepts behind Snowflake's Interactive Warehouses and how they provide low-latency analytics.\u003C/li\u003E\u003Cli\u003EHow to create and configure an Interactive Warehouse using SQL.\u003C/li\u003E\u003Cli\u003EHow zero-copy interactive analytics lets an interactive warehouse query your standard tables directly, with no data conversion required.\u003C/li\u003E\u003Cli\u003EHow to attach a table to an Interactive Warehouse to pre-warm the data cache for faster queries.\u003C/li\u003E\u003Cli\u003EA methodology for benchmarking and comparing the query latency and throughput of an interactive warehouse versus a standard warehouse.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EYou will build a complete, functioning interactive analytics environment in Snowflake, including a dedicated Interactive Warehouse configured to query your data directly. You will also create a Python-based performance test that executes queries against both your interactive warehouse and a standard warehouse, culminating in benchmark charts that visually demonstrate the latency and throughput improvements.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBasic knowledge of SQL and Python.\u003C/li\u003E\u003Cli\u003EFamiliarity with data warehousing and performance concepts.\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\u003C/li\u003E\u003Cli\u003EA Snowflake role with privileges to create warehouses and databases (\u003Cem\u003Ei.e.\u003C/em\u003E, \u003Ccode\u003ESYSADMIN\u003C/code\u003E is used in the notebook).\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EUnderstand Interactive Warehouses\u003C/h2\u003E\n","\u003Cp\u003ETo boost query performance for interactive, sub-second analytics, Snowflake introduces the interactive warehouse: a specialized compute engine tuned for low-latency, high-concurrency workloads.\u003C/p\u003E\n","\u003Ch3\u003EInteractive Warehouses\u003C/h3\u003E\n","\u003Cp\u003EAn interactive warehouse tunes the Snowflake engine specially for low-latency, interactive workloads. This type of warehouse is optimized to run continuously, serving high volumes of concurrent queries. All interactive warehouses run on the latest generation of hardware. Through zero-copy interactive analytics, an interactive warehouse can query your standard tables, Iceberg tables, and dynamic tables directly, with no conversion required.\u003C/p\u003E\n","\u003Ch3\u003EZero-copy interactive analytics\u003C/h3\u003E\n","\u003Cp\u003EAn interactive warehouse can query the following table types directly:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStandard tables.\u003C/strong\u003E Your existing Snowflake tables are queryable with no \u003Ccode\u003ECREATE INTERACTIVE TABLE\u003C/code\u003E step.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIceberg tables.\u003C/strong\u003E Open-format Iceberg data served at interactive latency.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDynamic tables.\u003C/strong\u003E Incrementally refreshed results that an interactive warehouse can query directly.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe setup is straightforward. Create an interactive warehouse, optionally attach your highest-priority tables for proactive cache warming, then query any standard table directly:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- 1. Create an interactive warehouse\nCREATE OR REPLACE INTERACTIVE WAREHOUSE analytics_iwh\n  WAREHOUSE_SIZE = 'XSMALL';\n\n-- 2. (Optional) Attach high-priority tables for proactive caching\nALTER WAREHOUSE analytics_iwh\n  ADD TABLES (your_db.your_schema.critical_table_1, your_db.your_schema.critical_table_2);\n\n-- 3. Query any standard table, no conversion needed\nUSE WAREHOUSE analytics_iwh;\nSELECT * FROM your_db.your_schema.any_standard_table WHERE ...;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith this pattern, \u003Ccode\u003EADD TABLES\u003C/code\u003E is a performance optimization, not a requirement: attaching a table proactively warms the cache, but unattached tables are still fully queryable and cached on demand when first accessed. The hands-on demo below follows this exact pattern, querying a standard table directly on an interactive warehouse.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: Before zero-copy interactive analytics, the only way to query data at interactive latency was to convert it into an interactive table. Interactive tables still exist and remain supported, mainly for compatibility with earlier interactive analytics setups. For new work, Snowflake recommends querying your standard tables directly through zero-copy interactive analytics instead.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EUse cases\u003C/h3\u003E\n","\u003Cp\u003EInteractive warehouses are built for one specific shape of work: simple, repetitive queries that must return in well under a second, run at high concurrency, against fresh data, and at a low cost per query. These aren't the complex, long-running transformations you'd send to a standard warehouse. Instead, they're the same handful of query patterns executed over and over, by thousands of users and, increasingly, by AI agents. Wherever that pattern shows up, an interactive warehouse is a strong fit.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/use-cases.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThree domains capture where it matters most:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAI & Agents.\u003C/strong\u003E Agentic and AI-driven applications fire off large volumes of small, concurrent queries, such as a retrieval step here or a metric lookup there, and each one needs to come back instantly and cheaply. Interactive warehouses make this practical for low-cost RAG retrieval, AI observability (monitoring model and agent behavior in near real time), and high-concurrency MCP servers that expose your data to many agents at once.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomer-Facing Data Apps.\u003C/strong\u003E When query latency is visible to your end users, consistency matters as much as raw speed. Interactive warehouses power data APIs that serve predictable, sub-second responses to customer-facing applications, embedded analytics inside your product, and live dashboards that stay responsive even under heavy, simultaneous use.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOperational Analytics.\u003C/strong\u003E Internal, decision-driving workloads depend on fresh data and fast answers. Interactive warehouses suit trading and risk management, infrastructure observability and alerting (high-throughput monitoring where every second counts), and supply chain and inventory tracking that must reflect the latest state of the business.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhat unites all of these is the same set of requirements, namely low latency, high concurrency, fresh data, and low cost per query, met by simple queries repeated at scale. That is exactly the workload interactive warehouses were designed for.\u003C/p\u003E\n","\u003Ch3\u003ELimitations\u003C/h3\u003E\n","\u003Cp\u003EThe queries that work best with interactive warehouses are usually \u003Ccode\u003ESELECT\u003C/code\u003E statements with selective \u003Ccode\u003EWHERE\u003C/code\u003E clauses, optionally including a \u003Ccode\u003EGROUP BY\u003C/code\u003E clause on a few dimensions.\u003C/p\u003E\n","\u003Cp\u003EHere are some limitations of interactive warehouses:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAn interactive warehouse is designed to stay up and running. It supports auto-suspend and auto-resume, but the minimum auto-suspend interval is 24 hours (86400 seconds), so it suspends only after 24 hours of inactivity. You can also suspend and resume it manually. Either way, expect significant query latency right after a resume, while the data cache warms up again.\u003C/li\u003E\u003Cli\u003EInteractive warehouses cancel any query that runs longer than 5 seconds, since they're tuned for short, low-latency queries. To protect p99 latency, configure a fallback warehouse so those queries are transparently re-run on a standard warehouse (see the "Configure a fallback warehouse" section below).\u003C/li\u003E\u003Cli\u003EYou can't run \u003Ccode\u003ECALL\u003C/code\u003E commands to call stored procedures through interactive warehouse\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Ch3\u003EData operations\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The companion notebook creates all of these objects automatically using the \u003Ccode\u003E{{DB_NAME}}\u003C/code\u003E and \u003Ccode\u003E{{STANDARD_WH_NAME}}\u003C/code\u003E variables defined in the "Set common variables" cell. The steps below show the equivalent manual SQL. If running outside the notebook, replace \u003Ccode\u003E{{DB_NAME}}\u003C/code\u003E and \u003Ccode\u003E{{STANDARD_WH_NAME}}\u003C/code\u003E with your own names (e.g. \u003Ccode\u003EJSMITH_MY_DEMO_DB\u003C/code\u003E and \u003Ccode\u003EJSMITH_STD_WH\u003C/code\u003E).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOptional: Create warehouse\u003C/h4\u003E\n","\u003Cp\u003EIn order to load data into a standard table, you'll need to use a standard warehouse.\nYou can use any existing warehouse or create a new one, here we'll create a new warehouse called \u003Ccode\u003E{{STANDARD_WH_NAME}}\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE WAREHOUSE {{STANDARD_WH_NAME}} WITH WAREHOUSE_SIZE='X-SMALL';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 1: Create a Database and Schema\u003C/h4\u003E\n","\u003Cp\u003EFirst, we'll start by creating a database called \u003Ccode\u003E{{DB_NAME}}\u003C/code\u003E and \u003Ccode\u003EBENCHMARK_FDN\u003C/code\u003E as a schema:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS {{DB_NAME}};\nCREATE SCHEMA IF NOT EXISTS {{DB_NAME}}.BENCHMARK_FDN;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 2: Create a new stage\u003C/h4\u003E\n","\u003Cp\u003ENext, we'll create a stage called \u003Ccode\u003Emy_csv_stage\u003C/code\u003E where the CSV file will soon be stored:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Define database and schema to use\nUSE SCHEMA {{DB_NAME}}.BENCHMARK_FDN;\n\n-- Create a stage that includes the definition for the CSV file format\nCREATE OR REPLACE STAGE my_csv_stage\n  FILE_FORMAT = (\n    TYPE = 'CSV'\n    SKIP_HEADER = 1\n    FIELD_OPTIONALLY_ENCLOSED_BY = '"'\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 3: Upload CSV to a stage\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn the Snowflake UI, navigate to the database/schema that you've created (\u003Ccode\u003E{{DB_NAME}}.BENCHMARK_FDN\u003C/code\u003E).\u003C/li\u003E\u003Cli\u003EGo to the \u003Ccode\u003Emy_csv_stage\u003C/code\u003E stage\u003C/li\u003E\u003Cli\u003EUpload the \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/synthetic_hits_data.csv\"\u003E\u003Ccode\u003Esynthetic_hits_data.csv\u003C/code\u003E\u003C/a\u003E file to this stage.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 4: Create the Table and Load Data\u003C/h4\u003E\n","\u003Cp\u003ENow that we have the CSV file in the stage, we'll need to create the \u003Ccode\u003EHITS2_CSV\u003C/code\u003E table and extract contents from the CSV file into it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Use your database and schema\nUSE SCHEMA {{DB_NAME}}.BENCHMARK_FDN;\n\n-- Create the table with the correct data types\nCREATE OR REPLACE TABLE HITS2_CSV (\n    EventDate DATE,\n    CounterID INT,\n    ClientIP STRING,\n    SearchEngineID INT,\n    SearchPhrase STRING,\n    ResolutionWidth INT,\n    Title STRING,\n    IsRefresh INT,\n    DontCountHits INT\n);\n\n-- Copy the data from your stage into the table\n-- Make sure to replace 'my_csv_stage' with your stage name\nCOPY INTO HITS2_CSV FROM @my_csv_stage/synthetic_hits_data.csv\n  FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 5: Query the data\u003C/h4\u003E\n","\u003Cp\u003EFinally, we'll now retrieve contents from the table by performing a simple query with the \u003Ccode\u003ESELECT\u003C/code\u003E statement:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE {{STANDARD_WH_NAME}};\nSELECT * FROM {{DB_NAME}}.BENCHMARK_FDN.HITS2_CSV LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis essentially retrieves data from the \u003Ccode\u003E{{DB_NAME}}\u003C/code\u003E database, \u003Ccode\u003EBENCHMARK_FDN\u003C/code\u003E schema and \u003Ccode\u003EHITS2_CSV\u003C/code\u003E table:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/hits2csv-data.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPerformance demo\u003C/h2\u003E\n","\u003Cp\u003ETo proceed with carrying out this performance comparison of an interactive warehouse against a standard one, you can download notebook file \u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/Getting_Started_with_Interactive_Analytics.ipynb\"\u003EGetting_Started_with_Interactive_Analytics.ipynb\u003C/a\u003E provided in the repo.\u003C/p\u003E\n","\u003Ch3\u003ESet common variables\u003C/h3\u003E\n","\u003Cp\u003EFirst, we'll derive session-scoped variable names from your Snowflake username. This ensures database and warehouse names are unique per user and avoids conflicts when multiple users run the notebook on the same account:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nUSER = session.sql("SELECT CURRENT_USER()").collect()[0][0]\nDB_NAME = f'{USER}_MY_DEMO_DB'\nINTERACTIVE_WH_NAME = f'{USER}_INT_WH'\nSTANDARD_WH_NAME = f'{USER}_STD_WH'\n\nprint(f"User: {USER}\\nDatabase: {DB_NAME}\\nInteractive WH: {INTERACTIVE_WH_NAME}\\nStandard WH: {STANDARD_WH_NAME}")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESet up role, warehouse, and database\u003C/h3\u003E\n","\u003Cp\u003EInteractive Warehouses are now generally available (GA) and enabled by default on your account, so there's no need to check the Snowflake version or verify any account parameters.\u003C/p\u003E\n","\u003Cp\u003EThe following SQL cell creates the standard warehouse, database, and schemas used throughout the notebook. All statements use \u003Ccode\u003EIF NOT EXISTS\u003C/code\u003E, so this cell is safe to re-run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE SYSADMIN;\n\n-- Create the compute and database objects used throughout this notebook (idempotent)\nCREATE WAREHOUSE IF NOT EXISTS {{STANDARD_WH_NAME}} WITH WAREHOUSE_SIZE = 'X-SMALL';\nCREATE DATABASE IF NOT EXISTS {{DB_NAME}};\n\nCREATE SCHEMA IF NOT EXISTS {{DB_NAME}}.BENCHMARK_FDN;\n\nUSE WAREHOUSE {{STANDARD_WH_NAME}};\nUSE DATABASE {{DB_NAME}};\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: In a Snowflake Notebook, SQL and Python cells share the same session. Any \u003Ccode\u003EUSE ROLE\u003C/code\u003E, \u003Ccode\u003EUSE DATABASE\u003C/code\u003E, or \u003Ccode\u003EUSE WAREHOUSE\u003C/code\u003E statement you run in a SQL cell also applies to subsequent Python cells (and vice versa).\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate an interactive warehouse\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/create-turn-on-interactive-warehouse.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, let's create our interactive warehouse using a SQL cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE INTERACTIVE WAREHOUSE {{INTERACTIVE_WH_NAME}}\n    WAREHOUSE_SIZE = 'XSMALL'\n    MIN_CLUSTER_COUNT = 1\n    MAX_CLUSTER_COUNT = 1\n    COMMENT = 'Interactive warehouse demo';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EData setup and loading\u003C/h3\u003E\n","\u003Cp\u003EBefore loading data, ensure the standard warehouse is active:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE {{STANDARD_WH_NAME}};\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe following Python cell creates the \u003Ccode\u003EHITS2_CSV\u003C/code\u003E table and loads it from the \u003Ccode\u003Esynthetic_hits_data.csv\u003C/code\u003E file bundled with the notebook. The load is idempotent: it checks whether the table already contains rows and, if so, skips the load on subsequent runs.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: The data is loaded from the bundled CSV using \u003Ccode\u003Epandas\u003C/code\u003E and \u003Ccode\u003Ewrite_pandas\u003C/code\u003E with no external network access required. Make sure \u003Ccode\u003Esynthetic_hits_data.csv\u003C/code\u003E is added to the notebook's files.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport pandas as pd\n\nDB, SCHEMA, TABLE = DB_NAME, "BENCHMARK_FDN", "HITS2_CSV"\nFQ = f"{DB}.{SCHEMA}.{TABLE}"\nCSV_FILE = "synthetic_hits_data.csv"  # bundled with this notebook\n\n# Create the source table if it doesn't already exist\nsession.sql(f"""\nCREATE TABLE IF NOT EXISTS {FQ} (\n    EventDate DATE,\n    CounterID INT,\n    ClientIP STRING,\n    SearchEngineID INT,\n    SearchPhrase STRING,\n    ResolutionWidth INT,\n    Title STRING,\n    IsRefresh INT,\n    DontCountHits INT\n)\n""").collect()\n\n# Idempotent load: only load when the table is empty\nrow_count = session.sql(f"SELECT COUNT(*) FROM {FQ}").collect()[0][0]\nif row_count > 0:\n    print(f"{FQ} already has {row_count:,} rows. Skipping data load.")\nelse:\n    print(f"Loading data into {FQ} ...")\n    pdf = pd.read_csv(CSV_FILE)\n    pdf["EventDate"] = pd.to_datetime(pdf["EventDate"]).dt.date    \n    session.write_pandas(pdf, TABLE, database=DB, schema=SCHEMA, quote_identifiers=False)\n    row_count = session.sql(f"SELECT COUNT(*) FROM {FQ}").collect()[0][0]\n    print(f"Loaded {row_count:,} rows into {FQ}.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe bundled CSV has 100,000 rows, which is too small to show a clear concurrency advantage. The following Python cell scales the table up to roughly 2 million rows by replicating the loaded data 20 times with small jittered variations (a randomized \u003Ccode\u003EClientIP\u003C/code\u003E and \u003Ccode\u003EResolutionWidth\u003C/code\u003E, and a small \u003Ccode\u003EEventDate\u003C/code\u003E offset), so each replicated batch looks like distinct traffic rather than exact duplicates. This step is also idempotent: it checks the row count first and skips the expansion if the table has already been scaled up.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ETARGET_MULTIPLIER = 20\n\nrow_count = session.sql(f"SELECT COUNT(*) FROM {FQ}").collect()[0][0]\nif row_count >= TARGET_MULTIPLIER * 100_000 * 0.9:\n    print(f"{FQ} already has {row_count:,} rows. Skipping data expansion.")\nelse:\n    print(f"Expanding {FQ} to roughly {TARGET_MULTIPLIER * 100_000:,} rows ...")\n    session.sql(f"""\n        INSERT INTO {FQ}\n        SELECT\n            DATEADD(day, UNIFORM(-3, 3, RANDOM()), t.EventDate),\n            t.CounterID,\n            CONCAT(TO_VARCHAR(UNIFORM(1, 255, RANDOM())), '.', TO_VARCHAR(UNIFORM(1, 255, RANDOM())), '.',\n                   TO_VARCHAR(UNIFORM(1, 255, RANDOM())), '.', TO_VARCHAR(UNIFORM(1, 255, RANDOM())))),\n            t.SearchEngineID,\n            t.SearchPhrase,\n            GREATEST(1, t.ResolutionWidth + UNIFORM(-100, 100, RANDOM())),\n            t.Title,\n            t.IsRefresh,\n            t.DontCountHits\n        FROM {FQ} AS t, TABLE(GENERATOR(ROWCOUNT => {TARGET_MULTIPLIER - 1})) AS g\n    """).collect()\n    row_count = session.sql(f"SELECT COUNT(*) FROM {FQ}").collect()[0][0]\n    print(f"Expanded {FQ} to {row_count:,} rows.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can then verify the loaded data with a quick query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE {{STANDARD_WH_NAME}};\nSELECT * FROM {{DB_NAME}}.BENCHMARK_FDN.HITS2_CSV LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis essentially retrieves data from the database, \u003Ccode\u003EBENCHMARK_FDN\u003C/code\u003E schema and \u003Ccode\u003EHITS2_CSV\u003C/code\u003E table:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/hits2csv-data.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAttach a table to the interactive warehouse\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/attach-standard-table-to-warehouse.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, we'll attach our standard table to the interactive warehouse, which pre-warms the data cache for optimal query performance:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE {{DB_NAME}};\nALTER WAREHOUSE {{INTERACTIVE_WH_NAME}} ADD TABLES(BENCHMARK_FDN.HITS2_CSV);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENote: \u003Ccode\u003EADD TABLES\u003C/code\u003E is a performance optimization, not a requirement. It proactively warms the warehouse's data cache so queries avoid a cold start. Any table you don't attach is still queryable and gets cached on demand the first time it's accessed. Proactive warming is currently limited to 10 tables.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EConfigure a fallback warehouse\u003C/h3\u003E\n","\u003Cp\u003EInteractive warehouses are tuned for short, sub-second queries, so Snowflake fixes their statement timeout at a maximum of 5 seconds and automatically cancels any query that runs longer. To make sure an occasional heavy or ad-hoc query still completes instead of failing, you can designate a \u003Cstrong\u003Efallback warehouse\u003C/strong\u003E: a standard warehouse that automatically re-runs any query that exceeds the 5-second timeout on the interactive warehouse.\u003C/p\u003E\n","\u003Cp\u003EThis retry is transparent to the client (it behaves as an internal retry), so the query still returns its result. It keeps fast dashboard queries responsive while isolating them from the occasional long-running query.\u003C/p\u003E\n","\u003Cp\u003EWe'll reuse the standard warehouse created earlier as the fallback, then confirm the setting via the \u003Ccode\u003EFALLBACK_WAREHOUSE\u003C/code\u003E column:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER WAREHOUSE {{INTERACTIVE_WH_NAME}} SET FALLBACK_WAREHOUSE = {{STANDARD_WH_NAME}};\n\nSHOW WAREHOUSES LIKE '{{INTERACTIVE_WH_NAME}}';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA few things to keep in mind about fallback warehouses:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe fallback is a \u003Cstrong\u003Estandard\u003C/strong\u003E warehouse and can be shared with non-interactive workloads. Choose a size that's the same as or larger than the interactive warehouse.\u003C/li\u003E\u003Cli\u003EIt must be started (or set to auto-resume) to accept retried queries, and standard credit consumption applies once it's active.\u003C/li\u003E\u003Cli\u003EThe querying role needs \u003Ccode\u003EUSAGE\u003C/code\u003E on both the interactive warehouse and its fallback warehouse. Setting a fallback requires \u003Ccode\u003EALTER WAREHOUSE\u003C/code\u003E on the interactive warehouse and \u003Ccode\u003EUSAGE\u003C/code\u003E on the fallback.\u003C/li\u003E\u003Cli\u003EWhen a retry occurs, the time spent on the interactive warehouse before the retry appears as \u003Ccode\u003Efault_handling_time\u003C/code\u003E in the query profile.\u003C/li\u003E\u003Cli\u003ETo remove the fallback warehouse later, run \u003Ccode\u003EALTER WAREHOUSE {{INTERACTIVE_WH_NAME}} UNSET FALLBACK_WAREHOUSE;\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESequential Query Benchmark\u003C/h3\u003E\n","\u003Cp\u003ETo directly compare performance, we'll benchmark both the interactive and standard warehouses over 50 sequential runs and plot their latencies side-by-side in a grouped bar chart:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport time\nimport numpy as np\n\ncursor = session.connection.cursor()\n\nruns = 50\n\ndef run_and_measure(count, mode):\n    wh = INTERACTIVE_WH_NAME if mode == "iw" else STANDARD_WH_NAME\n    table = "BENCHMARK_FDN.HITS2_CSV"\n    query = f"""\n        SELECT SearchEngineID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth)\n        FROM {table}\n        WHERE SearchPhrase <> ''\n        GROUP BY SearchEngineID, ClientIP\n        ORDER BY c DESC LIMIT 10\n    """\n    cursor.execute(f"USE WAREHOUSE {wh}")\n    cursor.execute('ALTER SESSION SET USE_CACHED_RESULT = FALSE;')\n\n    timings = []\n    for _ in range(count + 1):\n        t0 = time.time()\n        cursor.execute(query).fetchall()\n        timings.append(time.time() - t0)\n    return timings[1:] # skip warm-up run\n\ncounts_iw = run_and_measure(runs, "iw")\nprint(counts_iw)\n\ncounts_std = run_and_measure(runs, "std")\nprint(counts_std)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe first chart plots per-run latency side-by-side:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport matplotlib.pyplot as plt\n\ntitles = [(i+1) for i in range(0, len(counts_iw))]\n\nx = np.arange(len(titles))  # the label locations\nwidth = 0.35  # bar width\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.bar(x - width/2, counts_std, width, label="Standard", color="#5B5B5B")\nax.bar(x + width/2, counts_iw, width, label="Interactive", color="#29B5E8")\n\nax.set_ylabel("Latency")\nax.set_xlabel("Query run")\nax.set_title("Standard vs Interactive warehouse")\nax.set_xticks(x)\nax.set_xticklabels(titles)\nax.legend(\n    loc='upper center',\n    bbox_to_anchor=(0.5, -0.15),\n    ncol=2\n)\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/sequential-benchmark-std-vs-int-wh.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe second chart compares mean latency with standard deviation error bars:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Calculate means and standard deviations for error bars\nmean_std = np.mean(counts_std)\nmean_iw = np.mean(counts_iw)\nstd_std = np.std(counts_std)\nstd_iw = np.std(counts_iw)\n\nfig, ax = plt.subplots(figsize=(6, 5))\nbars = ax.bar(["Standard", "Interactive"], [mean_std, mean_iw],\n              yerr=[std_std, std_iw], capsize=8,\n              color=["#5B5B5B", "#29B5E8"], width=0.5)\n\nax.set_ylabel("Latency (seconds)")\nax.set_title("Standard vs Interactive warehouse\\n(mean over {} runs with std dev)".format(len(counts_std)))\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/sequential-benchmark-std-vs-int-wh-50runs.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EConcurrent Query Benchmark\u003C/h3\u003E\n","\u003Cp\u003ETo simulate real-world dashboard load, we'll stress-test both warehouses with concurrent queries. The benchmark uses a mixed query pool (light, medium, and heavy queries) with staggered Poisson-distributed arrivals, ramping from 1 to 8 concurrent workers. It measures server-side latency (p50, p90, p99) and throughput (queries per second) across multiple rounds for statistical reliability.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport random, time, numpy as np\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\nQUERY_TEMPLATES = {\n    "light": "SELECT * FROM {table} WHERE CounterID = 62 LIMIT 1",\n    "medium": """SELECT SearchEngineID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth)\n        FROM {table} WHERE SearchPhrase <> ''\n        GROUP BY SearchEngineID, ClientIP ORDER BY c DESC LIMIT 10""",\n    "heavy": """SELECT EventDate, COUNT(*) AS hits, COUNT(DISTINCT ClientIP) AS unique_ips,\n        AVG(ResolutionWidth), SUM(CASE WHEN SearchPhrase <> '' THEN 1 ELSE 0 END)\n        FROM {table} GROUP BY EventDate ORDER BY EventDate""",\n}\n\ndef build_query_pool(table):\n    return [(k, v.format(table=table)) for k, v in QUERY_TEMPLATES.items()]\n\ndef worker(conn, wh_name, query_pool, n_queries=6, arrival_rate=5):\n    cur = conn.cursor()\n    cur.execute(f"USE WAREHOUSE {wh_name}")\n    cur.execute("ALTER SESSION SET USE_CACHED_RESULT = FALSE")\n    latencies = []\n    for _ in range(n_queries):\n        time.sleep(random.expovariate(arrival_rate))\n        _, query = random.choice(query_pool)\n        cur.execute(query).fetchall()\n        qid = cur.sfqid\n        ms = cur.execute(f"SELECT TOTAL_ELAPSED_TIME FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY_BY_SESSION()) WHERE QUERY_ID = '{qid}'").fetchone()[0]\n        latencies.append(ms / 1000.0)\n    cur.close()\n    return latencies\n\ndef run_concurrent_benchmark(conn, wh_name, table, concurrency_levels, rounds=10):\n    query_pool = build_query_pool(table)\n    all_results = []\n    for n in concurrency_levels:\n        print(f"  concurrency={n} ({rounds} rounds) ...", end=" ", flush=True)\n        round_stats = []\n        for _ in range(rounds):\n            t0 = time.time()\n            with ThreadPoolExecutor(max_workers=n) as pool:\n                futures = [pool.submit(worker, conn, wh_name, query_pool) for _ in range(n)]\n                lats = [l for f in as_completed(futures) for l in f.result()]\n            wall = time.time() - t0\n            round_stats.append({"p50": np.percentile(lats, 50), "p90": np.percentile(lats, 90),\n                                "p99": np.percentile(lats, 99), "throughput_qps": len(lats) / wall})\n        result = {"concurrency": n}\n        for m in ["p50", "p90", "p99", "throughput_qps"]:\n            vals = [r[m] for r in round_stats]\n            result[f"{m}_mean"], result[f"{m}_std"] = np.mean(vals), np.std(vals)\n        all_results.append(result)\n        print(f"p50={result['p50_mean']:.3f}s(±{result['p50_std']:.3f})  p90={result['p90_mean']:.3f}s(±{result['p90_std']:.3f})  qps={result['throughput_qps_mean']:.1f}(±{result['throughput_qps_std']:.1f})")\n    return all_results\n\nconcurrency_levels = [1, 2, 4, 8]\nconn = session.connection\n\nprint("Interactive warehouse:")\nresults_iw = run_concurrent_benchmark(conn, INTERACTIVE_WH_NAME, "BENCHMARK_FDN.HITS2_CSV", concurrency_levels)\n\nprint("\\nStandard warehouse:")\nresults_std = run_concurrent_benchmark(conn, STANDARD_WH_NAME, "BENCHMARK_FDN.HITS2_CSV", concurrency_levels)\n\nprint(f"\\nBenchmark complete ({run_concurrent_benchmark.__defaults__[0]} rounds per level).")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe results are visualized in two side-by-side charts:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConcurrency vs Latency.\u003C/strong\u003E Shows how p50, p90, and p99 change as concurrent workers increase. A flat line means the warehouse handles more load without slowing down.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EConcurrency vs Throughput.\u003C/strong\u003E Shows queries per second at each concurrency level. Higher is better; a plateau indicates the warehouse is saturated.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport matplotlib.pyplot as plt\n\nbenchmark_rounds = run_concurrent_benchmark.__defaults__[0]\nlevels = [r["concurrency"] for r in results_iw]\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n\nfor results, label, color in [(results_std, "Standard", "#5B5B5B"), (results_iw, "Interactive", "#29B5E8")]:\n    for metric, marker, ls, alpha in [("p50", "o", "-", 1.0), ("p90", "s", "--", 0.6), ("p99", "^", ":", 0.4)]:\n        ax1.errorbar(levels, [r[f"{metric}_mean"] for r in results],\n                     yerr=[r[f"{metric}_std"] for r in results],\n                     fmt=f"{marker}{ls}", color=color, alpha=alpha, capsize=4, label=f"{label} {metric}")\n\nax1.set(xlabel="Concurrent Workers", ylabel="Latency (seconds)", xticks=levels)\nax1.set_ylim(bottom=0)\nax1.set_title(f"Concurrency vs Latency (lower is better)\\nmean ± std over {benchmark_rounds} rounds")\nax1.legend(fontsize=7, ncol=2)\nax1.grid(True, alpha=0.3)\n\nx = np.arange(len(levels))\nw = 0.35\nfor results, label, color, offset in [(results_std, "Standard", "#5B5B5B", -w/2), (results_iw, "Interactive", "#29B5E8", w/2)]:\n    ax2.bar(x + offset, [r["throughput_qps_mean"] for r in results], w,\n            yerr=[r["throughput_qps_std"] for r in results], capsize=4, label=label, color=color)\n\nax2.set(xlabel="Concurrent Workers", ylabel="Queries / Second", xticks=x)\nax2.set_xticklabels(levels)\nax2.set_title(f"Concurrency vs Throughput (higher is better)\\nmean ± std over {benchmark_rounds} rounds")\nax2.legend()\nax2.grid(True, alpha=0.3, axis="y")\n\nplt.tight_layout()\nplt.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/concurrency-benchmark.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EA final cell dynamically generates a written interpretation of the results, comparing the two warehouses across every concurrency level and surfacing scaling issues, tail latency spikes, throughput plateaus, and actionable suggestions when the interactive warehouse underperforms.\u003C/p\u003E\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003ETo avoid ongoing compute and storage costs, drop the objects created in this guide once you're done exploring:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP WAREHOUSE IF EXISTS {{INTERACTIVE_WH_NAME}};\nDROP WAREHOUSE IF EXISTS {{STANDARD_WH_NAME}};\nDROP DATABASE IF EXISTS {{DB_NAME}};\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EIn this guide, we explored how to address the challenge of low-latency, near real-time analytics using Snowflake's interactive warehouses. We walked through the complete setup process, from creating the necessary database objects and loading data to configuring and attaching a standard table to an interactive warehouse via zero-copy interactive analytics. The sequential and concurrent performance benchmarks clearly demonstrated the substantial latency and throughput improvements this provides over a standard warehouse, across both individual query runs and high-concurrency workloads. This confirms its value as a powerful solution for demanding use cases like live dashboards and high-throughput data APIs, where sub-second performance is critical.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe core concepts behind Snowflake's Interactive Warehouses and how they deliver low-latency analytics for use cases like live dashboards and APIs.\u003C/li\u003E\u003Cli\u003EHow zero-copy interactive analytics lets an interactive warehouse query your standard, Iceberg, and dynamic tables directly, with no conversion required.\u003C/li\u003E\u003Cli\u003EHow to create, configure, and attach a table to an interactive warehouse using SQL to prepare a high-performance analytics environment.\u003C/li\u003E\u003Cli\u003EHow to run a sequential benchmark and visualize per-run latency and mean latency with standard deviation to prove interactive performance gains.\u003C/li\u003E\u003Cli\u003EHow to simulate real-world concurrent dashboard load and measure p50, p90, p99 latency and throughput across multiple concurrency levels.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Cp\u003EData and Notebook:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/synthetic_hits_data.csv\"\u003Esynthetic_hits_data.csv\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/Getting_Started_with_Interactive_Analytics.ipynb\"\u003EGetting_Started_with_Interactive_Analytics.ipynb\u003C/a\u003E\u003C/li\u003E\u003C/ul\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#using-standard-and-iceberg-tables-public-preview\"\u003EZero-copy interactive analytics: using standard and Iceberg tables\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Base Quickstart CF","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nWhen it comes to near real-time (or sub-second) analytics, the ideal scenario involves achieving consistent, rapid query performance and managing costs effectively, even with large datasets and high user demand. \n\nSnowflake's new Interactive Warehouses are designed to deliver on these needs. They provide a high-concurrency, low-latency serving layer for near real-time analytics, and can query your existing standard tables directly through zero-copy interactive analytics, with no data conversion required. This allows consistent, sub-second query performance for live dashboards and APIs with great price-for-performance. With this end-to-end solution, you can avoid operational complexities and tool sprawl.\n\n### What You'll Learn\n- The core concepts behind Snowflake's Interactive Warehouses and how they provide low-latency analytics.\n- How to create and configure an Interactive Warehouse using SQL.\n- How zero-copy interactive analytics lets an interactive warehouse query your standard tables directly, with no data conversion required.\n- How to attach a table to an Interactive Warehouse to pre-warm the data cache for faster queries.\n- A methodology for benchmarking and comparing the query latency and throughput of an interactive warehouse versus a standard warehouse.\n\n### What You'll Build\n\nYou will build a complete, functioning interactive analytics environment in Snowflake, including a dedicated Interactive Warehouse configured to query your data directly. You will also create a Python-based performance test that executes queries against both your interactive warehouse and a standard warehouse, culminating in benchmark charts that visually demonstrate the latency and throughput improvements.\n\n### Prerequisites\n- Basic knowledge of SQL and Python.\n- Familiarity with data warehousing and performance concepts.\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)\n- A Snowflake role with privileges to create warehouses and databases (*i.e.*, `SYSADMIN` is used in the notebook).\n\n## Understand Interactive Warehouses\n\nTo boost query performance for interactive, sub-second analytics, Snowflake introduces the interactive warehouse: a specialized compute engine tuned for low-latency, high-concurrency workloads.\n\n### Interactive Warehouses\nAn interactive warehouse tunes the Snowflake engine specially for low-latency, interactive workloads. This type of warehouse is optimized to run continuously, serving high volumes of concurrent queries. All interactive warehouses run on the latest generation of hardware. Through zero-copy interactive analytics, an interactive warehouse can query your standard tables, Iceberg tables, and dynamic tables directly, with no conversion required.\n\n### Zero-copy interactive analytics\n\nAn interactive warehouse can query the following table types directly:\n\n- **Standard tables.** Your existing Snowflake tables are queryable with no `CREATE INTERACTIVE TABLE` step.\n- **Iceberg tables.** Open-format Iceberg data served at interactive latency.\n- **Dynamic tables.** Incrementally refreshed results that an interactive warehouse can query directly.\n\nThe setup is straightforward. Create an interactive warehouse, optionally attach your highest-priority tables for proactive cache warming, then query any standard table directly:\n\n```sql\n-- 1. Create an interactive warehouse\nCREATE OR REPLACE INTERACTIVE WAREHOUSE analytics_iwh\n  WAREHOUSE_SIZE = 'XSMALL';\n\n-- 2. (Optional) Attach high-priority tables for proactive caching\nALTER WAREHOUSE analytics_iwh\n  ADD TABLES (your_db.your_schema.critical_table_1, your_db.your_schema.critical_table_2);\n\n-- 3. Query any standard table, no conversion needed\nUSE WAREHOUSE analytics_iwh;\nSELECT * FROM your_db.your_schema.any_standard_table WHERE ...;\n```\n\nWith this pattern, `ADD TABLES` is a performance optimization, not a requirement: attaching a table proactively warms the cache, but unattached tables are still fully queryable and cached on demand when first accessed. The hands-on demo below follows this exact pattern, querying a standard table directly on an interactive warehouse.\n\n\u003E Note: Before zero-copy interactive analytics, the only way to query data at interactive latency was to convert it into an interactive table. Interactive tables still exist and remain supported, mainly for compatibility with earlier interactive analytics setups. For new work, Snowflake recommends querying your standard tables directly through zero-copy interactive analytics instead.\n\n### Use cases\nInteractive warehouses are built for one specific shape of work: simple, repetitive queries that must return in well under a second, run at high concurrency, against fresh data, and at a low cost per query. These aren't the complex, long-running transformations you'd send to a standard warehouse. Instead, they're the same handful of query patterns executed over and over, by thousands of users and, increasingly, by AI agents. Wherever that pattern shows up, an interactive warehouse is a strong fit.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/use-cases.png)\n\nThree domains capture where it matters most:\n\n- **AI & Agents.** Agentic and AI-driven applications fire off large volumes of small, concurrent queries, such as a retrieval step here or a metric lookup there, and each one needs to come back instantly and cheaply. Interactive warehouses make this practical for low-cost RAG retrieval, AI observability (monitoring model and agent behavior in near real time), and high-concurrency MCP servers that expose your data to many agents at once.\n- **Customer-Facing Data Apps.** When query latency is visible to your end users, consistency matters as much as raw speed. Interactive warehouses power data APIs that serve predictable, sub-second responses to customer-facing applications, embedded analytics inside your product, and live dashboards that stay responsive even under heavy, simultaneous use.\n- **Operational Analytics.** Internal, decision-driving workloads depend on fresh data and fast answers. Interactive warehouses suit trading and risk management, infrastructure observability and alerting (high-throughput monitoring where every second counts), and supply chain and inventory tracking that must reflect the latest state of the business.\n\nWhat unites all of these is the same set of requirements, namely low latency, high concurrency, fresh data, and low cost per query, met by simple queries repeated at scale. That is exactly the workload interactive warehouses were designed for.\n\n\n### Limitations\n\nThe queries that work best with interactive warehouses are usually `SELECT` statements with selective `WHERE` clauses, optionally including a `GROUP BY` clause on a few dimensions.\n\nHere are some limitations of interactive warehouses:\n- An interactive warehouse is designed to stay up and running. It supports auto-suspend and auto-resume, but the minimum auto-suspend interval is 24 hours (86400 seconds), so it suspends only after 24 hours of inactivity. You can also suspend and resume it manually. Either way, expect significant query latency right after a resume, while the data cache warms up again.\n- Interactive warehouses cancel any query that runs longer than 5 seconds, since they're tuned for short, low-latency queries. To protect p99 latency, configure a fallback warehouse so those queries are transparently re-run on a standard warehouse (see the \"Configure a fallback warehouse\" section below).\n- You can't run `CALL` commands to call stored procedures through interactive warehouse\n\n\u003C!-- ------------------------ --\u003E\n## Setup\n\n### Data operations\n\n\u003E Note: The companion notebook creates all of these objects automatically using the `{{DB_NAME}}` and `{{STANDARD_WH_NAME}}` variables defined in the \"Set common variables\" cell. The steps below show the equivalent manual SQL. If running outside the notebook, replace `{{DB_NAME}}` and `{{STANDARD_WH_NAME}}` with your own names (e.g. `JSMITH_MY_DEMO_DB` and `JSMITH_STD_WH`).\n\n#### Optional: Create warehouse\n\nIn order to load data into a standard table, you'll need to use a standard warehouse.\nYou can use any existing warehouse or create a new one, here we'll create a new warehouse called `{{STANDARD_WH_NAME}}`:\n\n```sql\nCREATE OR REPLACE WAREHOUSE {{STANDARD_WH_NAME}} WITH WAREHOUSE_SIZE='X-SMALL';\n```\n\n#### Step 1: Create a Database and Schema\n\nFirst, we'll start by creating a database called `{{DB_NAME}}` and `BENCHMARK_FDN` as a schema:\n\n```sql\nCREATE DATABASE IF NOT EXISTS {{DB_NAME}};\nCREATE SCHEMA IF NOT EXISTS {{DB_NAME}}.BENCHMARK_FDN;\n```\n\n#### Step 2: Create a new stage\nNext, we'll create a stage called `my_csv_stage` where the CSV file will soon be stored:\n\n```sql\n-- Define database and schema to use\nUSE SCHEMA {{DB_NAME}}.BENCHMARK_FDN;\n\n-- Create a stage that includes the definition for the CSV file format\nCREATE OR REPLACE STAGE my_csv_stage\n  FILE_FORMAT = (\n    TYPE = 'CSV'\n    SKIP_HEADER = 1\n    FIELD_OPTIONALLY_ENCLOSED_BY = '\"'\n  );\n```\n\n#### Step 3: Upload CSV to a stage\n\n1. In the Snowflake UI, navigate to the database/schema that you've created (`{{DB_NAME}}.BENCHMARK_FDN`).\n2. Go to the `my_csv_stage` stage\n3. Upload the [`synthetic_hits_data.csv`](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/synthetic_hits_data.csv) file to this stage.\n\n#### Step 4: Create the Table and Load Data\n\nNow that we have the CSV file in the stage, we'll need to create the `HITS2_CSV` table and extract contents from the CSV file into it.\n\n```sql\n-- Use your database and schema\nUSE SCHEMA {{DB_NAME}}.BENCHMARK_FDN;\n\n-- Create the table with the correct data types\nCREATE OR REPLACE TABLE HITS2_CSV (\n    EventDate DATE,\n    CounterID INT,\n    ClientIP STRING,\n    SearchEngineID INT,\n    SearchPhrase STRING,\n    ResolutionWidth INT,\n    Title STRING,\n    IsRefresh INT,\n    DontCountHits INT\n);\n\n-- Copy the data from your stage into the table\n-- Make sure to replace 'my_csv_stage' with your stage name\nCOPY INTO HITS2_CSV FROM @my_csv_stage/synthetic_hits_data.csv\n  FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1);\n```\n\n#### Step 5: Query the data\n\nFinally, we'll now retrieve contents from the table by performing a simple query with the `SELECT` statement:\n\n```sql\nUSE WAREHOUSE {{STANDARD_WH_NAME}};\nSELECT * FROM {{DB_NAME}}.BENCHMARK_FDN.HITS2_CSV LIMIT 100;\n```\n\nThis essentially retrieves data from the `{{DB_NAME}}` database, `BENCHMARK_FDN` schema and `HITS2_CSV` table:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/hits2csv-data.png)\n\n\u003C!-- ------------------------ --\u003E\n## Performance demo\n\nTo proceed with carrying out this performance comparison of an interactive warehouse against a standard one, you can download notebook file [Getting_Started_with_Interactive_Analytics.ipynb](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/Getting_Started_with_Interactive_Analytics.ipynb) provided in the repo.\n\n### Set common variables\n\nFirst, we'll derive session-scoped variable names from your Snowflake username. This ensures database and warehouse names are unique per user and avoids conflicts when multiple users run the notebook on the same account:\n\n```python\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nUSER = session.sql(\"SELECT CURRENT_USER()\").collect()[0][0]\nDB_NAME = f'{USER}_MY_DEMO_DB'\nINTERACTIVE_WH_NAME = f'{USER}_INT_WH'\nSTANDARD_WH_NAME = f'{USER}_STD_WH'\n\nprint(f\"User: {USER}\\nDatabase: {DB_NAME}\\nInteractive WH: {INTERACTIVE_WH_NAME}\\nStandard WH: {STANDARD_WH_NAME}\")\n```\n\n### Set up role, warehouse, and database\n\nInteractive Warehouses are now generally available (GA) and enabled by default on your account, so there's no need to check the Snowflake version or verify any account parameters.\n\nThe following SQL cell creates the standard warehouse, database, and schemas used throughout the notebook. All statements use `IF NOT EXISTS`, so this cell is safe to re-run:\n\n```sql\nUSE ROLE SYSADMIN;\n\n-- Create the compute and database objects used throughout this notebook (idempotent)\nCREATE WAREHOUSE IF NOT EXISTS {{STANDARD_WH_NAME}} WITH WAREHOUSE_SIZE = 'X-SMALL';\nCREATE DATABASE IF NOT EXISTS {{DB_NAME}};\n\nCREATE SCHEMA IF NOT EXISTS {{DB_NAME}}.BENCHMARK_FDN;\n\nUSE WAREHOUSE {{STANDARD_WH_NAME}};\nUSE DATABASE {{DB_NAME}};\n```\n\n\u003E Note: In a Snowflake Notebook, SQL and Python cells share the same session. Any `USE ROLE`, `USE DATABASE`, or `USE WAREHOUSE` statement you run in a SQL cell also applies to subsequent Python cells (and vice versa).\n\n### Create an interactive warehouse\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/create-turn-on-interactive-warehouse.png)\n\nNext, let's create our interactive warehouse using a SQL cell:\n\n```sql\nCREATE OR REPLACE INTERACTIVE WAREHOUSE {{INTERACTIVE_WH_NAME}}\n    WAREHOUSE_SIZE = 'XSMALL'\n    MIN_CLUSTER_COUNT = 1\n    MAX_CLUSTER_COUNT = 1\n    COMMENT = 'Interactive warehouse demo';\n```\n\n### Data setup and loading\n\nBefore loading data, ensure the standard warehouse is active:\n\n```sql\nUSE WAREHOUSE {{STANDARD_WH_NAME}};\n```\n\nThe following Python cell creates the `HITS2_CSV` table and loads it from the `synthetic_hits_data.csv` file bundled with the notebook. The load is idempotent: it checks whether the table already contains rows and, if so, skips the load on subsequent runs.\n\n\u003E Note: The data is loaded from the bundled CSV using `pandas` and `write_pandas` with no external network access required. Make sure `synthetic_hits_data.csv` is added to the notebook's files.\n\n```python\nimport pandas as pd\n\nDB, SCHEMA, TABLE = DB_NAME, \"BENCHMARK_FDN\", \"HITS2_CSV\"\nFQ = f\"{DB}.{SCHEMA}.{TABLE}\"\nCSV_FILE = \"synthetic_hits_data.csv\"  # bundled with this notebook\n\n# Create the source table if it doesn't already exist\nsession.sql(f\"\"\"\nCREATE TABLE IF NOT EXISTS {FQ} (\n    EventDate DATE,\n    CounterID INT,\n    ClientIP STRING,\n    SearchEngineID INT,\n    SearchPhrase STRING,\n    ResolutionWidth INT,\n    Title STRING,\n    IsRefresh INT,\n    DontCountHits INT\n)\n\"\"\").collect()\n\n# Idempotent load: only load when the table is empty\nrow_count = session.sql(f\"SELECT COUNT(*) FROM {FQ}\").collect()[0][0]\nif row_count \u003E 0:\n    print(f\"{FQ} already has {row_count:,} rows. Skipping data load.\")\nelse:\n    print(f\"Loading data into {FQ} ...\")\n    pdf = pd.read_csv(CSV_FILE)\n    pdf[\"EventDate\"] = pd.to_datetime(pdf[\"EventDate\"]).dt.date    \n    session.write_pandas(pdf, TABLE, database=DB, schema=SCHEMA, quote_identifiers=False)\n    row_count = session.sql(f\"SELECT COUNT(*) FROM {FQ}\").collect()[0][0]\n    print(f\"Loaded {row_count:,} rows into {FQ}.\")\n```\n\nThe bundled CSV has 100,000 rows, which is too small to show a clear concurrency advantage. The following Python cell scales the table up to roughly 2 million rows by replicating the loaded data 20 times with small jittered variations (a randomized `ClientIP` and `ResolutionWidth`, and a small `EventDate` offset), so each replicated batch looks like distinct traffic rather than exact duplicates. This step is also idempotent: it checks the row count first and skips the expansion if the table has already been scaled up.\n\n```python\nTARGET_MULTIPLIER = 20\n\nrow_count = session.sql(f\"SELECT COUNT(*) FROM {FQ}\").collect()[0][0]\nif row_count \u003E= TARGET_MULTIPLIER * 100_000 * 0.9:\n    print(f\"{FQ} already has {row_count:,} rows. Skipping data expansion.\")\nelse:\n    print(f\"Expanding {FQ} to roughly {TARGET_MULTIPLIER * 100_000:,} rows ...\")\n    session.sql(f\"\"\"\n        INSERT INTO {FQ}\n        SELECT\n            DATEADD(day, UNIFORM(-3, 3, RANDOM()), t.EventDate),\n            t.CounterID,\n            CONCAT(TO_VARCHAR(UNIFORM(1, 255, RANDOM())), '.', TO_VARCHAR(UNIFORM(1, 255, RANDOM())), '.',\n                   TO_VARCHAR(UNIFORM(1, 255, RANDOM())), '.', TO_VARCHAR(UNIFORM(1, 255, RANDOM())))),\n            t.SearchEngineID,\n            t.SearchPhrase,\n            GREATEST(1, t.ResolutionWidth + UNIFORM(-100, 100, RANDOM())),\n            t.Title,\n            t.IsRefresh,\n            t.DontCountHits\n        FROM {FQ} AS t, TABLE(GENERATOR(ROWCOUNT =\u003E {TARGET_MULTIPLIER - 1})) AS g\n    \"\"\").collect()\n    row_count = session.sql(f\"SELECT COUNT(*) FROM {FQ}\").collect()[0][0]\n    print(f\"Expanded {FQ} to {row_count:,} rows.\")\n```\n\nWe can then verify the loaded data with a quick query:\n\n```sql\nUSE WAREHOUSE {{STANDARD_WH_NAME}};\nSELECT * FROM {{DB_NAME}}.BENCHMARK_FDN.HITS2_CSV LIMIT 100;\n```\n\nThis essentially retrieves data from the database, `BENCHMARK_FDN` schema and `HITS2_CSV` table:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/hits2csv-data.png)\n\n### Attach a table to the interactive warehouse\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/attach-standard-table-to-warehouse.png)\n\nNext, we'll attach our standard table to the interactive warehouse, which pre-warms the data cache for optimal query performance:\n\n```sql\nUSE DATABASE {{DB_NAME}};\nALTER WAREHOUSE {{INTERACTIVE_WH_NAME}} ADD TABLES(BENCHMARK_FDN.HITS2_CSV);\n```\n\n\u003E Note: `ADD TABLES` is a performance optimization, not a requirement. It proactively warms the warehouse's data cache so queries avoid a cold start. Any table you don't attach is still queryable and gets cached on demand the first time it's accessed. Proactive warming is currently limited to 10 tables.\n\n### Configure a fallback warehouse\n\nInteractive warehouses are tuned for short, sub-second queries, so Snowflake fixes their statement timeout at a maximum of 5 seconds and automatically cancels any query that runs longer. To make sure an occasional heavy or ad-hoc query still completes instead of failing, you can designate a **fallback warehouse**: a standard warehouse that automatically re-runs any query that exceeds the 5-second timeout on the interactive warehouse.\n\nThis retry is transparent to the client (it behaves as an internal retry), so the query still returns its result. It keeps fast dashboard queries responsive while isolating them from the occasional long-running query.\n\nWe'll reuse the standard warehouse created earlier as the fallback, then confirm the setting via the `FALLBACK_WAREHOUSE` column:\n\n```sql\nALTER WAREHOUSE {{INTERACTIVE_WH_NAME}} SET FALLBACK_WAREHOUSE = {{STANDARD_WH_NAME}};\n\nSHOW WAREHOUSES LIKE '{{INTERACTIVE_WH_NAME}}';\n```\n\nA few things to keep in mind about fallback warehouses:\n- The fallback is a **standard** warehouse and can be shared with non-interactive workloads. Choose a size that's the same as or larger than the interactive warehouse.\n- It must be started (or set to auto-resume) to accept retried queries, and standard credit consumption applies once it's active.\n- The querying role needs `USAGE` on both the interactive warehouse and its fallback warehouse. Setting a fallback requires `ALTER WAREHOUSE` on the interactive warehouse and `USAGE` on the fallback.\n- When a retry occurs, the time spent on the interactive warehouse before the retry appears as `fault_handling_time` in the query profile.\n- To remove the fallback warehouse later, run `ALTER WAREHOUSE {{INTERACTIVE_WH_NAME}} UNSET FALLBACK_WAREHOUSE;`.\n\n### Sequential Query Benchmark\n\nTo directly compare performance, we'll benchmark both the interactive and standard warehouses over 50 sequential runs and plot their latencies side-by-side in a grouped bar chart:\n\n```python\nimport time\nimport numpy as np\n\ncursor = session.connection.cursor()\n\nruns = 50\n\ndef run_and_measure(count, mode):\n    wh = INTERACTIVE_WH_NAME if mode == \"iw\" else STANDARD_WH_NAME\n    table = \"BENCHMARK_FDN.HITS2_CSV\"\n    query = f\"\"\"\n        SELECT SearchEngineID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth)\n        FROM {table}\n        WHERE SearchPhrase \u003C\u003E ''\n        GROUP BY SearchEngineID, ClientIP\n        ORDER BY c DESC LIMIT 10\n    \"\"\"\n    cursor.execute(f\"USE WAREHOUSE {wh}\")\n    cursor.execute('ALTER SESSION SET USE_CACHED_RESULT = FALSE;')\n\n    timings = []\n    for _ in range(count + 1):\n        t0 = time.time()\n        cursor.execute(query).fetchall()\n        timings.append(time.time() - t0)\n    return timings[1:] # skip warm-up run\n\ncounts_iw = run_and_measure(runs, \"iw\")\nprint(counts_iw)\n\ncounts_std = run_and_measure(runs, \"std\")\nprint(counts_std)\n```\n\nThe first chart plots per-run latency side-by-side:\n\n```python\nimport matplotlib.pyplot as plt\n\ntitles = [(i+1) for i in range(0, len(counts_iw))]\n\nx = np.arange(len(titles))  # the label locations\nwidth = 0.35  # bar width\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.bar(x - width/2, counts_std, width, label=\"Standard\", color=\"#5B5B5B\")\nax.bar(x + width/2, counts_iw, width, label=\"Interactive\", color=\"#29B5E8\")\n\nax.set_ylabel(\"Latency\")\nax.set_xlabel(\"Query run\")\nax.set_title(\"Standard vs Interactive warehouse\")\nax.set_xticks(x)\nax.set_xticklabels(titles)\nax.legend(\n    loc='upper center',\n    bbox_to_anchor=(0.5, -0.15),\n    ncol=2\n)\nplt.show()\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/sequential-benchmark-std-vs-int-wh.png)\n\nThe second chart compares mean latency with standard deviation error bars:\n\n```python\n# Calculate means and standard deviations for error bars\nmean_std = np.mean(counts_std)\nmean_iw = np.mean(counts_iw)\nstd_std = np.std(counts_std)\nstd_iw = np.std(counts_iw)\n\nfig, ax = plt.subplots(figsize=(6, 5))\nbars = ax.bar([\"Standard\", \"Interactive\"], [mean_std, mean_iw],\n              yerr=[std_std, std_iw], capsize=8,\n              color=[\"#5B5B5B\", \"#29B5E8\"], width=0.5)\n\nax.set_ylabel(\"Latency (seconds)\")\nax.set_title(\"Standard vs Interactive warehouse\\n(mean over {} runs with std dev)\".format(len(counts_std)))\nplt.tight_layout()\nplt.show()\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/sequential-benchmark-std-vs-int-wh-50runs.png)\n\n### Concurrent Query Benchmark\n\nTo simulate real-world dashboard load, we'll stress-test both warehouses with concurrent queries. The benchmark uses a mixed query pool (light, medium, and heavy queries) with staggered Poisson-distributed arrivals, ramping from 1 to 8 concurrent workers. It measures server-side latency (p50, p90, p99) and throughput (queries per second) across multiple rounds for statistical reliability.\n\n```python\nimport random, time, numpy as np\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\n\nQUERY_TEMPLATES = {\n    \"light\": \"SELECT * FROM {table} WHERE CounterID = 62 LIMIT 1\",\n    \"medium\": \"\"\"SELECT SearchEngineID, ClientIP, COUNT(*) AS c, SUM(IsRefresh), AVG(ResolutionWidth)\n        FROM {table} WHERE SearchPhrase \u003C\u003E ''\n        GROUP BY SearchEngineID, ClientIP ORDER BY c DESC LIMIT 10\"\"\",\n    \"heavy\": \"\"\"SELECT EventDate, COUNT(*) AS hits, COUNT(DISTINCT ClientIP) AS unique_ips,\n        AVG(ResolutionWidth), SUM(CASE WHEN SearchPhrase \u003C\u003E '' THEN 1 ELSE 0 END)\n        FROM {table} GROUP BY EventDate ORDER BY EventDate\"\"\",\n}\n\ndef build_query_pool(table):\n    return [(k, v.format(table=table)) for k, v in QUERY_TEMPLATES.items()]\n\ndef worker(conn, wh_name, query_pool, n_queries=6, arrival_rate=5):\n    cur = conn.cursor()\n    cur.execute(f\"USE WAREHOUSE {wh_name}\")\n    cur.execute(\"ALTER SESSION SET USE_CACHED_RESULT = FALSE\")\n    latencies = []\n    for _ in range(n_queries):\n        time.sleep(random.expovariate(arrival_rate))\n        _, query = random.choice(query_pool)\n        cur.execute(query).fetchall()\n        qid = cur.sfqid\n        ms = cur.execute(f\"SELECT TOTAL_ELAPSED_TIME FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY_BY_SESSION()) WHERE QUERY_ID = '{qid}'\").fetchone()[0]\n        latencies.append(ms / 1000.0)\n    cur.close()\n    return latencies\n\ndef run_concurrent_benchmark(conn, wh_name, table, concurrency_levels, rounds=10):\n    query_pool = build_query_pool(table)\n    all_results = []\n    for n in concurrency_levels:\n        print(f\"  concurrency={n} ({rounds} rounds) ...\", end=\" \", flush=True)\n        round_stats = []\n        for _ in range(rounds):\n            t0 = time.time()\n            with ThreadPoolExecutor(max_workers=n) as pool:\n                futures = [pool.submit(worker, conn, wh_name, query_pool) for _ in range(n)]\n                lats = [l for f in as_completed(futures) for l in f.result()]\n            wall = time.time() - t0\n            round_stats.append({\"p50\": np.percentile(lats, 50), \"p90\": np.percentile(lats, 90),\n                                \"p99\": np.percentile(lats, 99), \"throughput_qps\": len(lats) / wall})\n        result = {\"concurrency\": n}\n        for m in [\"p50\", \"p90\", \"p99\", \"throughput_qps\"]:\n            vals = [r[m] for r in round_stats]\n            result[f\"{m}_mean\"], result[f\"{m}_std\"] = np.mean(vals), np.std(vals)\n        all_results.append(result)\n        print(f\"p50={result['p50_mean']:.3f}s(±{result['p50_std']:.3f})  p90={result['p90_mean']:.3f}s(±{result['p90_std']:.3f})  qps={result['throughput_qps_mean']:.1f}(±{result['throughput_qps_std']:.1f})\")\n    return all_results\n\nconcurrency_levels = [1, 2, 4, 8]\nconn = session.connection\n\nprint(\"Interactive warehouse:\")\nresults_iw = run_concurrent_benchmark(conn, INTERACTIVE_WH_NAME, \"BENCHMARK_FDN.HITS2_CSV\", concurrency_levels)\n\nprint(\"\\nStandard warehouse:\")\nresults_std = run_concurrent_benchmark(conn, STANDARD_WH_NAME, \"BENCHMARK_FDN.HITS2_CSV\", concurrency_levels)\n\nprint(f\"\\nBenchmark complete ({run_concurrent_benchmark.__defaults__[0]} rounds per level).\")\n```\n\nThe results are visualized in two side-by-side charts:\n\n- **Concurrency vs Latency.** Shows how p50, p90, and p99 change as concurrent workers increase. A flat line means the warehouse handles more load without slowing down.\n- **Concurrency vs Throughput.** Shows queries per second at each concurrency level. Higher is better; a plateau indicates the warehouse is saturated.\n\n```python\nimport matplotlib.pyplot as plt\n\nbenchmark_rounds = run_concurrent_benchmark.__defaults__[0]\nlevels = [r[\"concurrency\"] for r in results_iw]\n\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n\nfor results, label, color in [(results_std, \"Standard\", \"#5B5B5B\"), (results_iw, \"Interactive\", \"#29B5E8\")]:\n    for metric, marker, ls, alpha in [(\"p50\", \"o\", \"-\", 1.0), (\"p90\", \"s\", \"--\", 0.6), (\"p99\", \"^\", \":\", 0.4)]:\n        ax1.errorbar(levels, [r[f\"{metric}_mean\"] for r in results],\n                     yerr=[r[f\"{metric}_std\"] for r in results],\n                     fmt=f\"{marker}{ls}\", color=color, alpha=alpha, capsize=4, label=f\"{label} {metric}\")\n\nax1.set(xlabel=\"Concurrent Workers\", ylabel=\"Latency (seconds)\", xticks=levels)\nax1.set_ylim(bottom=0)\nax1.set_title(f\"Concurrency vs Latency (lower is better)\\nmean ± std over {benchmark_rounds} rounds\")\nax1.legend(fontsize=7, ncol=2)\nax1.grid(True, alpha=0.3)\n\nx = np.arange(len(levels))\nw = 0.35\nfor results, label, color, offset in [(results_std, \"Standard\", \"#5B5B5B\", -w/2), (results_iw, \"Interactive\", \"#29B5E8\", w/2)]:\n    ax2.bar(x + offset, [r[\"throughput_qps_mean\"] for r in results], w,\n            yerr=[r[\"throughput_qps_std\"] for r in results], capsize=4, label=label, color=color)\n\nax2.set(xlabel=\"Concurrent Workers\", ylabel=\"Queries / Second\", xticks=x)\nax2.set_xticklabels(levels)\nax2.set_title(f\"Concurrency vs Throughput (higher is better)\\nmean ± std over {benchmark_rounds} rounds\")\nax2.legend()\nax2.grid(True, alpha=0.3, axis=\"y\")\n\nplt.tight_layout()\nplt.show()\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-interactive-analytics/concurrency-benchmark.png)\n\nA final cell dynamically generates a written interpretation of the results, comparing the two warehouses across every concurrency level and surfacing scaling issues, tail latency spikes, throughput plateaus, and actionable suggestions when the interactive warehouse underperforms.\n\n## Cleanup\n\nTo avoid ongoing compute and storage costs, drop the objects created in this guide once you're done exploring:\n\n```sql\nDROP WAREHOUSE IF EXISTS {{INTERACTIVE_WH_NAME}};\nDROP WAREHOUSE IF EXISTS {{STANDARD_WH_NAME}};\nDROP DATABASE IF EXISTS {{DB_NAME}};\n```\n\n## Conclusion and Resources\n\nIn this guide, we explored how to address the challenge of low-latency, near real-time analytics using Snowflake's interactive warehouses. We walked through the complete setup process, from creating the necessary database objects and loading data to configuring and attaching a standard table to an interactive warehouse via zero-copy interactive analytics. The sequential and concurrent performance benchmarks clearly demonstrated the substantial latency and throughput improvements this provides over a standard warehouse, across both individual query runs and high-concurrency workloads. This confirms its value as a powerful solution for demanding use cases like live dashboards and high-throughput data APIs, where sub-second performance is critical.\n\n### What You Learned\n- The core concepts behind Snowflake's Interactive Warehouses and how they deliver low-latency analytics for use cases like live dashboards and APIs.\n- How zero-copy interactive analytics lets an interactive warehouse query your standard, Iceberg, and dynamic tables directly, with no conversion required.\n- How to create, configure, and attach a table to an interactive warehouse using SQL to prepare a high-performance analytics environment.\n- How to run a sequential benchmark and visualize per-run latency and mean latency with standard deviation to prove interactive performance gains.\n- How to simulate real-world concurrent dashboard load and measure p50, p90, p99 latency and throughput across multiple concurrency levels.\n\n### Related Resources\n\nData and Notebook:\n- [synthetic_hits_data.csv](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/synthetic_hits_data.csv)\n- [Getting_Started_with_Interactive_Analytics.ipynb](https://github.com/Snowflake-Labs/snowflake-demo-notebooks/blob/main/Interactive_Analytics/Getting_Started_with_Interactive_Analytics.ipynb)\n\nDocumentation:\n- [Snowflake interactive analytics](https://docs.snowflake.com/en/user-guide/interactive)\n- [Zero-copy interactive analytics: using standard and Iceberg tables](https://docs.snowflake.com/en/user-guide/interactive#using-standard-and-iceberg-tables-public-preview)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-bb4bc0f144","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-c411524651",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-26c6519d29","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-09-15",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a76bc02642","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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