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scripts that process data already sitting in Snowflake, but the scripts run somewhere else: on a VM, a cron server, a notebook host, or a Spark cluster you provision and pay for whether it's running or not. You could rewrite everything as stored procedures, but that means matching handler signatures, re-declaring packages, and splitting a project that works as a unit into separate database objects.\u003C/p\u003E\n","\u003Cp\u003EIn this Quickstart, we'll skip all of that. We'll take a clickstream pipeline as-is – a Python sessionizer, a PySpark analytics job, shared helpers – package it as a single \u003Cstrong\u003ECode Bundle\u003C/strong\u003E, and run each file on Snowflake. The Python job runs on a warehouse. The PySpark job runs as a native Spark job, with \u003Cstrong\u003Eno Spark cluster\u003C/strong\u003E required. By the end, you'll have the pipeline scheduled on a Task and producing fictional funnel metrics from ~50K clickstream events.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to package a project as a Code Bundle and run it on Snowflake compute\u003C/li\u003E\u003Cli\u003EHow to run a Python job on a \u003Cstrong\u003Ewarehouse\u003C/strong\u003E with \u003Ccode\u003EEXECUTE CODE BUNDLE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to override a bundle's specification at execution time with \u003Ccode\u003EWITH SPECIFICATION\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to submit a PySpark job via the REST API, SQL, or from a stage – no Spark cluster required\u003C/li\u003E\u003Cli\u003EHow to orchestrate a multi-stage pipeline with a Snowflake \u003Cstrong\u003ETask\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EHow to version, run asynchronously, and monitor your jobs in production\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account with access to Code Bundles, with a role that can create warehouses and databases (for example, \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E)\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/index\"\u003ESnowflake CLI\u003C/a\u003E:\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake-cli\n# or: uv tool install snowflake-cli\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/li\u003E\u003Cli\u003EA configured Snowflake CLI connection (verify with \u003Ccode\u003Esnow connection list\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EBasic familiarity with Python, PySpark, and SQL\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETip:\u003C/strong\u003E You can run this Quickstart with \u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code\"\u003ECortex Code\u003C/a\u003E, Snowflake's AI coding assistant. Point Cortex Code at this guide and it can execute each step for you, explain what's happening, and help you adapt the pipeline to your own data. You can also use it side-by-side as you work through the guide in your terminal.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA clickstream pipeline with two independent stages – a warehouse Python sessionizer and a native Spark analytics job – packaged as a Code Bundle and sequenced by a Task:\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-console\"\u003E   RAW_EVENTS (50K clickstream events)\n      │\n      ├──────────────────────────────┐\n      ▼                              ▼\n ┌─────────────────────┐   ┌─────────────────────────┐\n │  sessionize.py      │   │  spark_analytics.py      │\n │  (warehouse Python) │   │  (Spark job)             │\n └─────────────────────┘   └─────────────────────────┘\n      │  SESSIONS              │  FUNNEL_METRICS\n      │                        │  PRODUCT_PAIRS\n      ▼                        ▼\n   Sequenced by a Snowflake Task\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELet's get started!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhat are Code Bundles?\u003C/h2\u003E\n","\u003Cp\u003EA Code Bundle is a named Snowflake object that contains your project source files – Python scripts, PySpark jobs, shared helpers, a spec file – created from a stage, workspace, or local directory. You run any file in the bundle with \u003Ccode\u003EEXECUTE CODE BUNDLE\u003C/code\u003E, choosing the compute type per-run.\u003C/p\u003E\n","\u003Cp\u003EKey properties:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOne deployment unit, multiple entrypoints. A multi-file project stays together as one object. You don't create a separate bundle (or stored procedure) for each script – you run different files from the same bundle.\u003C/li\u003E\u003Cli\u003EA spec declares the default runtime. A \u003Cstrong\u003Ecode_bundle.yml\u003C/strong\u003E file in the root of your project describes how the bundle runs by default (language, compute type, packages). You can override it per-run with \u003Ccode\u003EWITH SPECIFICATION\u003C/code\u003E when a different file needs a different runtime.\u003C/li\u003E\u003Cli\u003EProduction-ready out of the box. Code Bundles support versioning, asynchronous execution, and run history – no additional scaffolding.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EHow it differs from stored procedures\u003C/h3\u003E\n","\u003Cp\u003EWith stored procedures, you rewrite each script to a handler signature, declare packages per-object, and split a multi-file project into separate database objects. With Code Bundles, you bring your project directory as-is – the files, the imports between them, the argument parsing – bundle them together and run all of it directly.\u003C/p\u003E\n","\u003Ch3\u003EWhen to use each compute type\u003C/h3\u003E\n","\u003Cp\u003EA Code Bundle can target different workloads depending on the runtime:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWorkload\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECompute Type\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EHow it runs\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPython\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustom\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ewarehouse\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPushes SQL/UDF work down to the engine\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESessionizing events with window functions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPySpark\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Espark\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ewarehouse\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDataFrame API runs on the Snowflake engine via Snowpark Connect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFunnel analysis, co-occurrence with self-joins\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPython on a compute pool\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecustom\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ecompute_pool\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFull Python runtime on container compute\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETraining a scikit-learn model, GPU inference\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESet up your account\u003C/h2\u003E\n","\u003Cp\u003ELet's create the objects our pipeline needs and generate the clickstream data. We'll produce ~50K events across 1,000 users, 200 products, 10 categories, and 30 days – enough to produce meaningful funnels and co-occurrence patterns.\u003C/p\u003E\n","\u003Cp\u003EClone the companion repo and open the project:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-run-python-and-spark-jobs-with-code-bundles\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd sfguide-run-python-and-spark-jobs-with-code-bundles\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the setup script:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -f setup.sql\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the setup creates:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS CB_WH\n  WAREHOUSE_SIZE = 'XSMALL' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nCREATE DATABASE IF NOT EXISTS CODE_BUNDLES_DB;\nCREATE SCHEMA   IF NOT EXISTS CODE_BUNDLES_DB.PIPELINE;\n\nUSE WAREHOUSE CB_WH;\nUSE DATABASE  CODE_BUNDLES_DB;\nUSE SCHEMA    PIPELINE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe full \u003Cstrong\u003Esetup.sql\u003C/strong\u003E generates a \u003Ccode\u003EPRODUCTS\u003C/code\u003E dimension table (200 products across 10 categories) and a \u003Ccode\u003ERAW_EVENTS\u003C/code\u003E table with ~50K clickstream events. Events follow a realistic funnel shape: ~40% page views, ~30% product views, ~20% add-to-carts, and ~10% purchases, distributed across five traffic sources (organic, paid search, social, email, direct) and three device types.\u003C/p\u003E\n","\u003Cp\u003EThe verification query at the end confirms ~50K total events with the expected funnel distribution.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore the pipeline code\u003C/h2\u003E\n","\u003Cp\u003EThe pipeline you're migrating is just Python. Let's look at both stages before we deploy them – the important part is that we don't change them to run on Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EStage 1: The sessionizer (\u003Cstrong\u003Esessionize.py\u003C/strong\u003E)\u003C/h3\u003E\n","\u003Cp\u003EThis job reads raw events and assigns session IDs based on 30-minute inactivity gaps – a standard sessionization pattern. It then computes per-session metrics: duration, event counts by type, revenue, and whether the session converted.\u003C/p\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUses \u003Ccode\u003ELAG\u003C/code\u003E to find the previous event per user, then flags new sessions wherever the gap exceeds 30 minutes\u003C/li\u003E\u003Cli\u003EAggregates events within each session into metrics (page views, carts, purchases, revenue)\u003C/li\u003E\u003Cli\u003EWrites a \u003Ccode\u003ESESSIONS\u003C/code\u003E table – one row per session with dimensions (device, traffic source) carried forward\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe key detail is \u003Ccode\u003Eget_session()\u003C/code\u003E in \u003Cstrong\u003Ehelpers.py\u003C/strong\u003E. It calls \u003Ccode\u003Eget_active_session()\u003C/code\u003E, which returns the Snowpark session that Snowflake injects at runtime when the bundle executes:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# sessionize.py\nfrom helpers import get_session, log_step\n\ndef main() -> None:\n    args = parse_args()\n    session = get_session()\n\n    log_step(f"Sessionizing {args.source_table} (timeout: {args.inactivity_minutes} min)")\n\n    session.sql(f"""\n        CREATE OR REPLACE TABLE {args.output_table} AS\n        WITH ordered_events AS (\n            SELECT *,\n                LAG(EVENT_TIMESTAMP) OVER (\n                    PARTITION BY USER_ID ORDER BY EVENT_TIMESTAMP\n                ) AS prev_event_ts\n            FROM {args.source_table}\n        ),\n        session_boundaries AS (\n            SELECT *,\n                CASE\n                    WHEN prev_event_ts IS NULL THEN 1\n                    WHEN DATEDIFF('minute', prev_event_ts, EVENT_TIMESTAMP) > {args.inactivity_minutes} THEN 1\n                    ELSE 0\n                END AS is_new_session\n            FROM ordered_events\n        ),\n        ...\n    """).collect()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# helpers.py\nfrom snowflake.snowpark.context import get_active_session\n\ndef get_session() -> Session:\n    try:\n        return get_active_session()\n    except Exception:\n        raise RuntimeError("No active Snowpark session found. ...")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen a file runs as a Code Bundle, Snowflake injects the session automatically – no credentials in your code, no connection setup. The helper's error message makes it clear that this script is meant to be executed via \u003Ccode\u003EEXECUTE CODE BUNDLE\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe wrapper in \u003Cstrong\u003Ehelpers.py\u003C/strong\u003E isn't required – all you need is \u003Ccode\u003Eget_active_session()\u003C/code\u003E. We use a shared helper here because \u003Cstrong\u003Esessionize.py\u003C/strong\u003E imports from it (for both \u003Ccode\u003Eget_session\u003C/code\u003E and \u003Ccode\u003Elog_step\u003C/code\u003E), and it's a natural pattern when a project has multiple entrypoints that may share utility code.\u003C/p\u003E\n","\u003Ch3\u003EStage 2: The Spark analytics (\u003Cstrong\u003Espark_analytics.py\u003C/strong\u003E)\u003C/h3\u003E\n","\u003Cp\u003EThis is PySpark – DataFrames and \u003Ccode\u003Efunctions.*\u003C/code\u003E. The only Snowflake-specific line is the session initialization, which uses Snowpark Connect to create a SparkSession that compiles DataFrame operations into SQL and runs them on the warehouse:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark_connect import init_spark_session\nfrom pyspark.sql import functions as F\n\ndef main() -> None:\n    args = parse_args()\n    spark = init_spark_session()\n\n    events = spark.table(args.events_table)\n\n    # Funnel analysis: step-by-step conversion per traffic_source × device\n    funnel = build_funnel(events)\n    funnel.write.mode("overwrite").saveAsTable(args.funnel_output)\n\n    # Product co-occurrence: which products are browsed together?\n    pairs = build_product_pairs(events)\n    pairs.write.mode("overwrite").saveAsTable(args.pairs_output)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the analytics compute:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFunnel analysis – for each traffic source and device type, how many sessions reached each step (page view → product view → cart → purchase), with conversion rates and drop-off percentages at each stage\u003C/li\u003E\u003Cli\u003EProduct co-occurrence – within each user's browsing history, which products appear together and how much more often than chance (lift score)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBoth of these are textbook PySpark patterns: iterative aggregation over user-level event sequences and combinatorial pair generation. On Snowflake, \u003Ccode\u003Einit_spark_session()\u003C/code\u003E returns a SparkSession powered by Snowpark Connect, which compiles the Spark DataFrame API into SQL and runs it on the warehouse – no Spark cluster to stand up or manage. Everything after the session init is standard PySpark: DataFrames, Column expressions, GroupedData, and \u003Ccode\u003Efunctions.*\u003C/code\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun the sessionizer on a warehouse\u003C/h2\u003E\n","\u003Cp\u003ENow let's run \u003Cstrong\u003Esessionize.py\u003C/strong\u003E on Snowflake. Because this job orchestrates SQL (window functions in this case) over Snowflake data, a warehouse is the correct compute type.\u003C/p\u003E\n","\u003Cp\u003EA single file, \u003Cstrong\u003Ecode_bundle.yml\u003C/strong\u003E, describes the default way the bundle runs. It's a default, but is also mutable. You can override it per-run when a different file in the project needs different settings:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ebundle:\n  type: custom\n  compute_type: warehouse\n  language: python\n  compute_options:\n    runtime_version: '3.11'\n  properties:\n    requirements_file: pyproject.toml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the specification says:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003Ecompute_type: warehouse\u003C/code\u003E – run on the warehouse set in the session\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Eruntime_version: '3.11'\u003C/code\u003E – the Python version for the run (always quote the value)\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Erequirements_file\u003C/code\u003E – packages to install, resolved from Snowflake's PyPI proxy\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFrom the project root, create the Code Bundle from your local files and run it. We'll name the bundle CLICKSTREAM_PIPELINE:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The CLI needs to know which database and schema to create the bundle in. You can pass \u003Ccode\u003E--database\u003C/code\u003E and \u003Ccode\u003E--schema\u003C/code\u003E on each command (as shown below), or add \u003Ccode\u003Edatabase = "CODE_BUNDLES_DB"\u003C/code\u003E and \u003Ccode\u003Eschema = "PIPELINE"\u003C/code\u003E to your connection in \u003Cstrong\u003Econnections.toml\u003C/strong\u003E so you don't have to repeat them.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow bundle create CLICKSTREAM_PIPELINE --source . --exclude ".venv/**" --exclude "setup.sql" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow run the sessionizer. Everything after \u003Ccode\u003E--\u003C/code\u003E is passed straight to your script's \u003Ccode\u003Eargparse\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow bundle execute CLICKSTREAM_PIPELINE \\\n  --entrypoint sessionize.py \\\n  --database CODE_BUNDLES_DB --schema PIPELINE \\\n  -- --source-table RAW_EVENTS \\\n     --output-table SESSIONS \\\n     --inactivity-minutes 30\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYour first job is running on Snowflake – sessionizing 50K events into structured sessions with no rewrite, no stored procedure.\u003C/p\u003E\n","\u003Cp\u003EWhen the run finishes, confirm the output. You should see thousands of sessions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "SELECT COUNT(*) AS total_sessions, SUM(CASE WHEN CONVERTED THEN 1 ELSE 0 END) AS converted, ROUND(AVG(DURATION_SECONDS)) AS avg_duration_sec FROM CODE_BUNDLES_DB.PIPELINE.SESSIONS;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E+-----------------------------------------------+\n| TOTAL_SESSIONS | CONVERTED | AVG_DURATION_SEC |\n|----------------+-----------+------------------|\n| 5952           | 3460      | 637              |\n+-----------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun the Spark analytics\u003C/h2\u003E\n","\u003Cp\u003EIf your work is Python-only, you've already learned the core Code Bundles workflow – packaging, deploying, and running a project on Snowflake. The following sections show how the same bundle handles Spark and compute pool workloads.\u003C/p\u003E\n","\u003Cp\u003EBoth \u003Cstrong\u003Esessionize.py\u003C/strong\u003E and \u003Cstrong\u003Espark_analytics.py\u003C/strong\u003E live in the same project alongside shared helpers. The bundle keeps them together as a single deployment unit – you don't create a second bundle for a second script. Instead, you run a different file with a different spec.\u003C/p\u003E\n","\u003Cp\u003EA Spark job uses \u003Ccode\u003Etype: spark\u003C/code\u003E instead of \u003Ccode\u003Etype: custom\u003C/code\u003E, and an empty \u003Ccode\u003Ecompute_options: {}\u003C/code\u003E to run on the latest Snowpark Connect client version. You can pin a specific version with \u003Ccode\u003Eruntime_version\u003C/code\u003E for reproducibility. Everything else – entrypoint, arguments, compute on a warehouse – works the same way.\u003C/p\u003E\n","\u003Ch3\u003ESubmit via the REST API\u003C/h3\u003E\n","\u003Cp\u003EThe Code Bundles REST API is the recommended way to submit Spark jobs, especially from external orchestrators like Airflow, CI/CD pipelines, or custom UIs. If you're a Spark engineer, this pattern aligns with patterns you may already use on other platforms: POST a job definition, get back an ID, poll for status.\u003C/p\u003E\n","\u003Cp\u003EThe REST API submits jobs from a stage path rather than a stored bundle. Let's upload the project files to a stage first. Run the following commands from within the companion repo directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "CREATE STAGE IF NOT EXISTS CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE;" --database CODE_BUNDLES_DB --schema PIPELINE\nsnow stage copy spark_analytics.py @CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE --database CODE_BUNDLES_DB --schema PIPELINE\nsnow stage copy helpers.py @CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESubmit the Spark analytics job asynchronously by POSTing to the \u003Ccode\u003E/api/v2/code-bundle-executions\u003C/code\u003E endpoint. First, generate a JWT for authentication (this requires \u003Ca href=\"https://docs.snowflake.com/en/user-guide/key-pair-auth\"\u003Ekey-pair authentication\u003C/a\u003E configured on your CLI connection).\u003C/p\u003E\n","\u003Cp\u003ERun the following in your terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003ESNOWFLAKE_TOKEN=$(snow connection generate-jwt)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENow submit the job:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -X POST \\\n  "https://<account_identifier>.snowflakecomputing.com/api/v2/code-bundle-executions?asyncExec=true" \\\n  -H "Authorization: Bearer ${SNOWFLAKE_TOKEN}" \\\n  -H "X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT" \\\n  -H "Content-Type: application/json" \\\n  -H "Accept: application/json" \\\n  -H "User-Agent: myApplicationName/1.0" \\\n  -H "X-Snowflake-Role: ACCOUNTADMIN" \\\n  -H "X-Snowflake-Warehouse: CB_WH" \\\n  -H "X-Snowflake-Database: CODE_BUNDLES_DB" \\\n  -H "X-Snowflake-Schema: PIPELINE" \\\n  -d '{\n    "from_location": "@CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE",\n    "entrypoint": "spark_analytics.py",\n    "arguments": ["--events-table", "CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS",\n                   "--funnel-output", "CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS",\n                   "--pairs-output", "CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS"],\n    "specification": {\n      "bundle": {\n        "type": "spark",\n        "compute_type": "warehouse",\n        "language": "python",\n        "compute_options": {}\n      }\n    }\n  }'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAn async submission returns \u003Ccode\u003E202 Accepted\u003C/code\u003E with a \u003Ccode\u003Ejob_id\u003C/code\u003E you use to check status or cancel the run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "code": "392604",\n  "message": "Request execution in progress.",\n  "job_id": "01c51743-c819-4261-0000-5349586311aa"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck status with:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -X GET \\\n  "https://<account_identifier>.snowflakecomputing.com/api/v2/code-bundle-executions/<job_id>" \\\n  -H "Authorization: Bearer ${SNOWFLAKE_TOKEN}" \\\n  -H "X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT" \\\n  -H "Accept: application/json" \\\n  -H "User-Agent: myApplicationName/1.0" \\\n  -H "X-Snowflake-Role: ACCOUNTADMIN" \\\n  -H "X-Snowflake-Database: CODE_BUNDLES_DB" \\\n  -H "X-Snowflake-Schema: PIPELINE"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E The \u003Ccode\u003EX-Snowflake-Database\u003C/code\u003E and \u003Ccode\u003EX-Snowflake-Schema\u003C/code\u003E headers are required on the GET status endpoint. Without them, the response will be an empty array.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EFor details on the REST API, including idempotent submission and the full specification reference, see \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/code-bundles/spark-code-bundles#submit-with-rest\"\u003ESubmit Spark jobs on Snowflake\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ESubmit from a stage with SQL\u003C/h3\u003E\n","\u003Cp\u003EYou can also submit the Spark job with SQL instead of the REST API, which integrates natively with Snowflake Tasks for scheduling. This uses the same stage we created above:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXECUTE CODE BUNDLE FROM '@CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE'\n  ENTRYPOINT = 'spark_analytics.py'\n  ARGUMENTS  = (\n    '--events-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n    '--funnel-output', 'CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS',\n    '--pairs-output', 'CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS'\n  )\n  WITH SPECIFICATION $$\nbundle:\n  type: spark\n  compute_type: warehouse\n  language: python\n  compute_options: {}\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESubmit from a stored bundle\u003C/h3\u003E\n","\u003Cp\u003EAlternatively, if you've already created a stored bundle, you can run it as such:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "\nUSE DATABASE CODE_BUNDLES_DB;\nUSE SCHEMA PIPELINE;\nUSE WAREHOUSE CB_WH;\nEXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n  ENTRYPOINT = 'spark_analytics.py'\n  ARGUMENTS  = (\n    '--events-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n    '--funnel-output', 'CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS',\n    '--pairs-output', 'CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS'\n  )\n  WITH SPECIFICATION \\$\\$\nbundle:\n  type: spark\n  compute_type: warehouse\n  language: python\n  compute_options: {}\n\\$\\$;\n" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENote that in the stage-based and stored-bundle examples above, the \u003Ccode\u003EWITH SPECIFICATION\u003C/code\u003E override replaces the stored spec entirely. Every required field must be repeated, even if it hasn't changed. This is helpful when a single bundle contains files that need different runtimes. For example, a Python sessionizer and a Spark analytics job in the same project, each run with its own spec.\u003C/p\u003E\n","\u003Cp\u003EIn our pipeline, the Spark job runs on the session warehouse and writes FUNNEL_METRICS and PRODUCT_PAIRS. The output is an empty result row, which indicates a successful run. The actual results are in the tables the job wrote – we'll query them next.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery the results\u003C/h2\u003E\n","\u003Cp\u003ELet's explore the funnel metrics and product co-occurrence data.\u003C/p\u003E\n","\u003Cp\u003EWhich traffic sources convert best?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "SELECT TRAFFIC_SOURCE, DEVICE, SESSIONS, CONVERSION_RATE, DROP_OFF_PCT FROM FUNNEL_METRICS WHERE STEP = 'purchase' ORDER BY CONVERSION_RATE DESC;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see clear differences: some traffic sources convert significantly better than others, and device type matters.\u003C/p\u003E\n","\u003Cp\u003EWhere do users drop off in the funnel?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "SELECT STEP, SESSIONS, CONVERSION_RATE, DROP_OFF_PCT FROM FUNNEL_METRICS WHERE TRAFFIC_SOURCE = 'organic' AND DEVICE = 'desktop' ORDER BY STEP_ORDER;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWatch the \u003Ccode\u003ESESSIONS\u003C/code\u003E column shrink at each step. The \u003Ccode\u003EDROP_OFF_PCT\u003C/code\u003E column shows what percentage of sessions failed to advance from the previous step.\u003C/p\u003E\n","\u003Cp\u003EWhich products are frequently browsed together?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "SELECT PRODUCT_A, CATEGORY_A, PRODUCT_B, CATEGORY_B, CO_OCCURRENCE_COUNT, LIFT FROM PRODUCT_PAIRS ORDER BY LIFT DESC LIMIT 20;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA \u003Ccode\u003ELIFT\u003C/code\u003E score above 1.0 means the pair appears together more often than random chance. The top pairs with high lift and high co-occurrence count are your strongest "customers also viewed" candidates.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOrchestrate the pipeline with a Task\u003C/h2\u003E\n","\u003Cp\u003EA pipeline should run on a schedule without you manually triggering it. Because \u003Ccode\u003EEXECUTE CODE BUNDLE\u003C/code\u003E is a SQL statement, you can wrap each stage in a Snowflake Task and chain them into a graph without needing an external scheduler.\u003C/p\u003E\n","\u003Cp\u003ELet's create two tasks. First, the root task – it runs \u003Cstrong\u003Esessionize.py\u003C/strong\u003E on a nightly schedule:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "\nCREATE OR REPLACE TASK SESSIONIZE_TASK\n  WAREHOUSE = CB_WH\n  SCHEDULE  = 'USING CRON 0 2 * * * UTC'\nAS\n  EXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n    ENTRYPOINT = 'sessionize.py'\n    ARGUMENTS  = ('--source-table', 'RAW_EVENTS',\n                  '--output-table', 'SESSIONS',\n                  '--inactivity-minutes', '30');\n" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe analytics task runs after the sessionizer succeeds. The \u003Ccode\u003EAFTER\u003C/code\u003E clause chains them into a dependency graph:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "\nCREATE OR REPLACE TASK ANALYTICS_TASK\n  WAREHOUSE = CB_WH\n  AFTER SESSIONIZE_TASK\nAS\n  EXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n    ENTRYPOINT = 'spark_analytics.py'\n    ARGUMENTS  = (\n      '--events-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n      '--funnel-output', 'CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS',\n      '--pairs-output', 'CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS'\n    )\n    WITH SPECIFICATION \\$\\$\nbundle:\n  type: spark\n  compute_type: warehouse\n  language: python\n  compute_options: {}\n\\$\\$;\n" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESESSIONIZE_TASK is the root task, scheduled nightly at 2:00 AM UTC\u003C/li\u003E\u003Cli\u003EANALYTICS_TASK runs \u003Ccode\u003EAFTER\u003C/code\u003E the root task, forming a two-step graph\u003C/li\u003E\u003Cli\u003EEach task runs one stage of the pipeline as a Code Bundle – same project, but with different entrypoints and specs\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETasks are created in a suspended state, by default. Resume the child task first, then the root – this ensures the graph is fully wired before the root fires. Then trigger a run to test it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "ALTER TASK ANALYTICS_TASK RESUME; ALTER TASK SESSIONIZE_TASK RESUME; EXECUTE TASK SESSIONIZE_TASK;" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E An active root task runs on its schedule and consumes credits. Suspend it with \u003Ccode\u003Esnow sql -q "ALTER TASK SESSIONIZE_TASK SUSPEND;" --database CODE_BUNDLES_DB --schema PIPELINE\u003C/code\u003E when you're done testing.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOperate in production\u003C/h2\u003E\n","\u003Cp\u003EGetting a job to run is one thing; operating it and iterating on it is another. Code Bundles give you versioning, asynchronous execution, and full run history out of the box.\u003C/p\u003E\n","\u003Ch3\u003EShip a new version\u003C/h3\u003E\n","\u003Cp\u003EAs you iterate on your pipeline – fixing a bug, tuning the sessionization window, adding a new analytics step – you need to push the latest code to Snowflake. You can ship newer versions of the Code Bundle by recreating the bundle with \u003Ccode\u003E--overwrite\u003C/code\u003E to replace it with your current local files (equivalent to \u003Ccode\u003ECREATE OR REPLACE\u003C/code\u003E in SQL):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow bundle create CLICKSTREAM_PIPELINE --source . --exclude ".venv/**" --exclude "setup.sql" --overwrite --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E If you create your bundle from a stage or workspace instead of a local directory, you can use \u003Ccode\u003Esnow bundle alter CLICKSTREAM_PIPELINE --add-version @STAGE/path\u003C/code\u003E to add immutable version snapshots without replacing the bundle.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERun asynchronously\u003C/h3\u003E\n","\u003Cp\u003EFor long jobs, the \u003Ccode\u003E--async\u003C/code\u003E flag submits the run and returns immediately with a query ID instead of waiting for it to finish:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow bundle execute CLICKSTREAM_PIPELINE --entrypoint sessionize.py --async \\\n  --database CODE_BUNDLES_DB --schema PIPELINE \\\n  -- --source-table RAW_EVENTS --output-table SESSIONS --inactivity-minutes 30\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou'll see output like \u003Ccode\u003ERequest submitted. Query ID: 01c51743-c819-4261-0000-5349586311aa\u003C/code\u003E. Use the ID returned to you to check on the run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Replace with the query ID returned by your --async run\nsnow bundle status 01c51743-c819-4261-0000-5349586311aa\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EReview run history\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003ECODE_BUNDLE_HISTORY\u003C/code\u003E table function returns a record for every job run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "SELECT ENTRYPOINT, STATUS, BUNDLE_TYPE, COMPUTE_TYPE, START_TIME, END_TIME, ERROR_MESSAGE FROM TABLE(INFORMATION_SCHEMA.CODE_BUNDLE_HISTORY(BUNDLE_NAME => 'CLICKSTREAM_PIPELINE', RESULT_LIMIT => 20)) ORDER BY START_TIME DESC;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExample output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E+----------------------------------------------------------------------------------------------------------------------------------+\n| ENTRYPOINT         | STATUS | BUNDLE_TYPE | COMPUTE_TYPE | START_TIME                | END_TIME                  | ERROR_MESSAGE |\n|--------------------+--------+-------------+--------------+---------------------------+---------------------------+---------------|\n| spark_analytics.py | DONE   | SPARK       | WAREHOUSE    | 2026-08-27 07:52:07-07:00 | 2026-08-27 07:53:16-07:00 |               |\n| sessionize.py      | DONE   | CUSTOM      | WAREHOUSE    | 2026-08-26 15:09:22-07:00 | 2026-08-26 15:09:32-07:00 |               |\n+----------------------------------------------------------------------------------------------------------------------------------+\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESearch logs and stack traces\u003C/h3\u003E\n","\u003Cp\u003EIf you configure an \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/logging-tracing/event-table-setting-up\"\u003Eevent table\u003C/a\u003E for your account, Code Bundle runs automatically emit application logs there – tagged with the run's query ID. You can filter by severity (\u003Ccode\u003EERROR\u003C/code\u003E, \u003Ccode\u003EFATAL\u003C/code\u003E) to surface stack traces when a run fails, without needing to reproduce the issue.\u003C/p\u003E\n","\u003Cp\u003ELook at the \u003Ccode\u003ESTATUS\u003C/code\u003E column in the history results – a successful run shows \u003Ccode\u003EDONE\u003C/code\u003E, and a failure carries details in \u003Ccode\u003EERROR_MESSAGE\u003C/code\u003E. That's your production loop: version, run, monitor, debug.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E(Optional) Train a model on a compute pool\u003C/h2\u003E\n","\u003Cp\u003EWhen your job requires powerful compute – training a model, running on GPUs, or installing packages from any source – a compute pool is the right compute type for your job run. Compute pools provide you with a full Python runtime powered by Snowpark Container Services.\u003C/p\u003E\n","\u003Cp\u003ECreate a small compute pool:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "CREATE COMPUTE POOL IF NOT EXISTS CB_ML_POOL MIN_NODES = 1 MAX_NODES = 1 INSTANCE_FAMILY = CPU_X64_S;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWait for the pool to reach IDLE or ACTIVE state before proceeding (this typically takes 1–3 minutes):\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "DESCRIBE COMPUTE POOL CB_ML_POOL;" --database CODE_BUNDLES_DB --schema PIPELINE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's the compute pool specification – compare it to the warehouse and Spark specs we've used so far. The key differences are \u003Ccode\u003Ecompute_type: compute_pool\u003C/code\u003E, a named pool, and a Container Runtime version:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ebundle:\n  type: custom\n  compute_type: compute_pool\n  language: python\n  compute_options:\n    compute_pool: CB_ML_POOL\n    query_warehouse: CB_WH\n    runtime_version: 'V2.5-CPU-PY3.11'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Etrain_model.py\u003C/strong\u003E trains a scikit-learn model to predict purchase amount from product and category, then logs it to the Snowflake Model Registry:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport argparse\nimport pandas as pd\nfrom snowflake.ml.registry import Registry\nfrom snowflake.snowpark.context import get_active_session\n\ndef main() -> None:\n    args = parse_args()  # --source-table, --database, --schema, --model-name, --version\n    session = get_active_session()\n\n    rows = session.table(args.source_table).filter(\n        "EVENT_TYPE = 'purchase'"\n    ).select("PRODUCT_ID", "CATEGORY", "REVENUE").collect()\n    df = pd.DataFrame([row.as_dict() for row in rows])\n    # ... fit a scikit-learn pipeline ...\n    registry = Registry(session=session, database_name=args.database, schema_name=args.schema)\n    registry.log_model(model, model_name=args.model_name, version_name=args.version,\n                       sample_input_data=X.head())\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun it with \u003Ccode\u003EWITH SPECIFICATION\u003C/code\u003E to override the stored warehouse spec, just like we did for the Spark job:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "\nUSE DATABASE CODE_BUNDLES_DB;\nUSE SCHEMA PIPELINE;\nUSE WAREHOUSE CB_WH;\nEXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n  ENTRYPOINT = 'train_model.py'\n  ARGUMENTS  = (\n    '--source-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n    '--database', 'CODE_BUNDLES_DB',\n    '--schema', 'PIPELINE'\n  )\n  WITH SPECIFICATION \\$\\$\nbundle:\n  type: custom\n  compute_type: compute_pool\n  language: python\n  compute_options:\n    compute_pool: CB_ML_POOL\n    query_warehouse: CB_WH\n    runtime_version: 'V2.5-CPU-PY3.11'\n\\$\\$;\n" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere's what the code does:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReads purchase events directly from Snowflake with the injected session\u003C/li\u003E\u003Cli\u003ETrains the model in the Python process on the compute pool\u003C/li\u003E\u003Cli\u003ERegisters the model so it's versioned and ready for batch inference\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EA successful run will return a confirmation that the statement was executed successfully.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean up\u003C/h2\u003E\n","\u003Cp\u003ELet's remove everything we created. First, suspend and drop the tasks, then the bundle and remaining objects:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow sql -q "\nALTER TASK IF EXISTS SESSIONIZE_TASK SUSPEND;\nDROP TASK IF EXISTS ANALYTICS_TASK;\nDROP TASK IF EXISTS SESSIONIZE_TASK;\nDROP CODE BUNDLE IF EXISTS CLICKSTREAM_PIPELINE;\nDROP COMPUTE POOL IF EXISTS CB_ML_POOL;\nDROP DATABASE IF EXISTS CODE_BUNDLES_DB;\nDROP WAREHOUSE IF EXISTS CB_WH;\n" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You migrated a clickstream pipeline onto Snowflake as a Code Bundle – without rewriting your jobs as stored procedures. You sessionized 50K events on a warehouse, ran PySpark analytics as a native Spark job from the same bundle, orchestrated both stages with a Task, and operated the pipeline with versioning, async execution, and run history.\u003C/p\u003E\n","\u003Cp\u003EThe theme throughout was matching compute to the job – a warehouse for the SQL-heavy sessionization, a Spark job for the combinatorial analytics – while keeping the project together as one deployment unit. You deployed once and ran different files with different runtimes, without duplicating shared code or decomposing into one object per script.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to package a project as a Code Bundle and run it on Snowflake compute\u003C/li\u003E\u003Cli\u003EHow to run a Python job on a warehouse and a PySpark job as a native Spark job\u003C/li\u003E\u003Cli\u003EHow to override a bundle's specification at execution time with \u003Ccode\u003EWITH SPECIFICATION\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to orchestrate a multi-stage pipeline with a Snowflake Task\u003C/li\u003E\u003Cli\u003EHow to version, run asynchronously, and monitor Code Bundle jobs in production\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/code-bundles/code-bundles\"\u003ESnowflake Code Bundles documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/code-bundles/spark-code-bundles\"\u003ESubmit Spark jobs on Snowflake\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-run-python-and-spark-jobs-with-code-bundles\"\u003ECode Bundles Quickstart companion repo\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003ESnowflake Tasks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview\"\u003ESnowpark Connect for Apache Spark\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Run Python or Spark Jobs on Snowflake with Code Bundles",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\n\u003E **Note:** Code Bundles are in Public Preview.\n\nYou have Python scripts that process data already sitting in Snowflake, but the scripts run somewhere else: on a VM, a cron server, a notebook host, or a Spark cluster you provision and pay for whether it's running or not. You could rewrite everything as stored procedures, but that means matching handler signatures, re-declaring packages, and splitting a project that works as a unit into separate database objects.\n\nIn this Quickstart, we'll skip all of that. We'll take a clickstream pipeline as-is – a Python sessionizer, a PySpark analytics job, shared helpers – package it as a single **Code Bundle**, and run each file on Snowflake. The Python job runs on a warehouse. The PySpark job runs as a native Spark job, with **no Spark cluster** required. By the end, you'll have the pipeline scheduled on a Task and producing fictional funnel metrics from ~50K clickstream events.\n\n### What You'll Learn\n- How to package a project as a Code Bundle and run it on Snowflake compute\n- How to run a Python job on a **warehouse** with `EXECUTE CODE BUNDLE`\n- How to override a bundle's specification at execution time with `WITH SPECIFICATION`\n- How to submit a PySpark job via the REST API, SQL, or from a stage – no Spark cluster required\n- How to orchestrate a multi-stage pipeline with a Snowflake **Task**\n- How to version, run asynchronously, and monitor your jobs in production\n\n### What You'll Need\n- A Snowflake account with access to Code Bundles, with a role that can create warehouses and databases (for example, **ACCOUNTADMIN**)\n- The [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/index):\n  ```bash\n  pip install snowflake-cli\n  # or: uv tool install snowflake-cli\n  ```\n- A configured Snowflake CLI connection (verify with `snow connection list`)\n- Basic familiarity with Python, PySpark, and SQL\n\n\u003E **Tip:** You can run this Quickstart with [Cortex Code](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code), Snowflake's AI coding assistant. Point Cortex Code at this guide and it can execute each step for you, explain what's happening, and help you adapt the pipeline to your own data. You can also use it side-by-side as you work through the guide in your terminal.\n\n\n### What You'll Build\n- A clickstream pipeline with two independent stages – a warehouse Python sessionizer and a native Spark analytics job – packaged as a Code Bundle and sequenced by a Task:\n\n```console\n   RAW_EVENTS (50K clickstream events)\n      │\n      ├──────────────────────────────┐\n      ▼                              ▼\n ┌─────────────────────┐   ┌─────────────────────────┐\n │  sessionize.py      │   │  spark_analytics.py      │\n │  (warehouse Python) │   │  (Spark job)             │\n └─────────────────────┘   └─────────────────────────┘\n      │  SESSIONS              │  FUNNEL_METRICS\n      │                        │  PRODUCT_PAIRS\n      ▼                        ▼\n   Sequenced by a Snowflake Task\n```\n\nLet's get started!\n\n\u003C!-- ------------------------ --\u003E\n## What are Code Bundles?\n\nA Code Bundle is a named Snowflake object that contains your project source files – Python scripts, PySpark jobs, shared helpers, a spec file – created from a stage, workspace, or local directory. You run any file in the bundle with `EXECUTE CODE BUNDLE`, choosing the compute type per-run.\n\nKey properties:\n\n- One deployment unit, multiple entrypoints. A multi-file project stays together as one object. You don't create a separate bundle (or stored procedure) for each script – you run different files from the same bundle.\n- A spec declares the default runtime. A **code_bundle.yml** file in the root of your project describes how the bundle runs by default (language, compute type, packages). You can override it per-run with `WITH SPECIFICATION` when a different file needs a different runtime.\n- Production-ready out of the box. Code Bundles support versioning, asynchronous execution, and run history – no additional scaffolding.\n\n### How it differs from stored procedures\n\nWith stored procedures, you rewrite each script to a handler signature, declare packages per-object, and split a multi-file project into separate database objects. With Code Bundles, you bring your project directory as-is – the files, the imports between them, the argument parsing – bundle them together and run all of it directly.\n\n### When to use each compute type\n\nA Code Bundle can target different workloads depending on the runtime:\n\n| Workload | Type | Compute Type | How it runs | Example |\n|----------|------|-------------|-------------|---------|\n| Python | `custom` | `warehouse` | Pushes SQL/UDF work down to the engine | Sessionizing events with window functions |\n| PySpark | `spark` | `warehouse` | DataFrame API runs on the Snowflake engine via Snowpark Connect | Funnel analysis, co-occurrence with self-joins |\n| Python on a compute pool | `custom` | `compute_pool` | Full Python runtime on container compute | Training a scikit-learn model, GPU inference |\n\n\u003C!-- ------------------------ --\u003E\n## Set up your account\n\nLet's create the objects our pipeline needs and generate the clickstream data. We'll produce ~50K events across 1,000 users, 200 products, 10 categories, and 30 days – enough to produce meaningful funnels and co-occurrence patterns.\n\nClone the companion repo and open the project:\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-run-python-and-spark-jobs-with-code-bundles\n```\n\n```bash\ncd sfguide-run-python-and-spark-jobs-with-code-bundles\n```\n\nRun the setup script:\n\n```bash\nsnow sql -f setup.sql\n```\n\nHere's what the setup creates:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS CB_WH\n  WAREHOUSE_SIZE = 'XSMALL' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nCREATE DATABASE IF NOT EXISTS CODE_BUNDLES_DB;\nCREATE SCHEMA   IF NOT EXISTS CODE_BUNDLES_DB.PIPELINE;\n\nUSE WAREHOUSE CB_WH;\nUSE DATABASE  CODE_BUNDLES_DB;\nUSE SCHEMA    PIPELINE;\n```\n\nThe full **setup.sql** generates a `PRODUCTS` dimension table (200 products across 10 categories) and a `RAW_EVENTS` table with ~50K clickstream events. Events follow a realistic funnel shape: ~40% page views, ~30% product views, ~20% add-to-carts, and ~10% purchases, distributed across five traffic sources (organic, paid search, social, email, direct) and three device types.\n\nThe verification query at the end confirms ~50K total events with the expected funnel distribution.\n\n\u003C!-- ------------------------ --\u003E\n## Explore the pipeline code\n\nThe pipeline you're migrating is just Python. Let's look at both stages before we deploy them – the important part is that we don't change them to run on Snowflake.\n\n### Stage 1: The sessionizer (**sessionize.py**)\n\nThis job reads raw events and assigns session IDs based on 30-minute inactivity gaps – a standard sessionization pattern. It then computes per-session metrics: duration, event counts by type, revenue, and whether the session converted.\n\nHere's what the code does:\n\n- Uses `LAG` to find the previous event per user, then flags new sessions wherever the gap exceeds 30 minutes\n- Aggregates events within each session into metrics (page views, carts, purchases, revenue)\n- Writes a `SESSIONS` table – one row per session with dimensions (device, traffic source) carried forward\n\nThe key detail is `get_session()` in **helpers.py**. It calls `get_active_session()`, which returns the Snowpark session that Snowflake injects at runtime when the bundle executes:\n\n```python\n# sessionize.py\nfrom helpers import get_session, log_step\n\ndef main() -\u003E None:\n    args = parse_args()\n    session = get_session()\n\n    log_step(f\"Sessionizing {args.source_table} (timeout: {args.inactivity_minutes} min)\")\n\n    session.sql(f\"\"\"\n        CREATE OR REPLACE TABLE {args.output_table} AS\n        WITH ordered_events AS (\n            SELECT *,\n                LAG(EVENT_TIMESTAMP) OVER (\n                    PARTITION BY USER_ID ORDER BY EVENT_TIMESTAMP\n                ) AS prev_event_ts\n            FROM {args.source_table}\n        ),\n        session_boundaries AS (\n            SELECT *,\n                CASE\n                    WHEN prev_event_ts IS NULL THEN 1\n                    WHEN DATEDIFF('minute', prev_event_ts, EVENT_TIMESTAMP) \u003E {args.inactivity_minutes} THEN 1\n                    ELSE 0\n                END AS is_new_session\n            FROM ordered_events\n        ),\n        ...\n    \"\"\").collect()\n```\n\n```python\n# helpers.py\nfrom snowflake.snowpark.context import get_active_session\n\ndef get_session() -\u003E Session:\n    try:\n        return get_active_session()\n    except Exception:\n        raise RuntimeError(\"No active Snowpark session found. ...\")\n```\n\nWhen a file runs as a Code Bundle, Snowflake injects the session automatically – no credentials in your code, no connection setup. The helper's error message makes it clear that this script is meant to be executed via `EXECUTE CODE BUNDLE`.\n\nThe wrapper in **helpers.py** isn't required – all you need is `get_active_session()`. We use a shared helper here because **sessionize.py** imports from it (for both `get_session` and `log_step`), and it's a natural pattern when a project has multiple entrypoints that may share utility code.\n\n### Stage 2: The Spark analytics (**spark_analytics.py**)\n\nThis is PySpark – DataFrames and `functions.*`. The only Snowflake-specific line is the session initialization, which uses Snowpark Connect to create a SparkSession that compiles DataFrame operations into SQL and runs them on the warehouse:\n\n```python\nfrom snowflake.snowpark_connect import init_spark_session\nfrom pyspark.sql import functions as F\n\ndef main() -\u003E None:\n    args = parse_args()\n    spark = init_spark_session()\n\n    events = spark.table(args.events_table)\n\n    # Funnel analysis: step-by-step conversion per traffic_source × device\n    funnel = build_funnel(events)\n    funnel.write.mode(\"overwrite\").saveAsTable(args.funnel_output)\n\n    # Product co-occurrence: which products are browsed together?\n    pairs = build_product_pairs(events)\n    pairs.write.mode(\"overwrite\").saveAsTable(args.pairs_output)\n```\n\nHere's what the analytics compute:\n\n- Funnel analysis – for each traffic source and device type, how many sessions reached each step (page view → product view → cart → purchase), with conversion rates and drop-off percentages at each stage\n- Product co-occurrence – within each user's browsing history, which products appear together and how much more often than chance (lift score)\n\nBoth of these are textbook PySpark patterns: iterative aggregation over user-level event sequences and combinatorial pair generation. On Snowflake, `init_spark_session()` returns a SparkSession powered by Snowpark Connect, which compiles the Spark DataFrame API into SQL and runs it on the warehouse – no Spark cluster to stand up or manage. Everything after the session init is standard PySpark: DataFrames, Column expressions, GroupedData, and `functions.*`.\n\n\u003C!-- ------------------------ --\u003E\n## Run the sessionizer on a warehouse\n\nNow let's run **sessionize.py** on Snowflake. Because this job orchestrates SQL (window functions in this case) over Snowflake data, a warehouse is the correct compute type.\n\nA single file, **code_bundle.yml**, describes the default way the bundle runs. It's a default, but is also mutable. You can override it per-run when a different file in the project needs different settings:\n\n```yaml\nbundle:\n  type: custom\n  compute_type: warehouse\n  language: python\n  compute_options:\n    runtime_version: '3.11'\n  properties:\n    requirements_file: pyproject.toml\n```\n\nHere's what the specification says:\n\n- `compute_type: warehouse` – run on the warehouse set in the session\n- `runtime_version: '3.11'` – the Python version for the run (always quote the value)\n- `requirements_file` – packages to install, resolved from Snowflake's PyPI proxy\n\nFrom the project root, create the Code Bundle from your local files and run it. We'll name the bundle CLICKSTREAM_PIPELINE:\n\n\u003E **Note:** The CLI needs to know which database and schema to create the bundle in. You can pass `--database` and `--schema` on each command (as shown below), or add `database = \"CODE_BUNDLES_DB\"` and `schema = \"PIPELINE\"` to your connection in **connections.toml** so you don't have to repeat them.\n\n```bash\nsnow bundle create CLICKSTREAM_PIPELINE --source . --exclude \".venv/**\" --exclude \"setup.sql\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nNow run the sessionizer. Everything after `--` is passed straight to your script's `argparse`. \n\n```bash\nsnow bundle execute CLICKSTREAM_PIPELINE \\\n  --entrypoint sessionize.py \\\n  --database CODE_BUNDLES_DB --schema PIPELINE \\\n  -- --source-table RAW_EVENTS \\\n     --output-table SESSIONS \\\n     --inactivity-minutes 30\n```\n\nYour first job is running on Snowflake – sessionizing 50K events into structured sessions with no rewrite, no stored procedure. \n\nWhen the run finishes, confirm the output. You should see thousands of sessions:\n\n```bash\nsnow sql -q \"SELECT COUNT(*) AS total_sessions, SUM(CASE WHEN CONVERTED THEN 1 ELSE 0 END) AS converted, ROUND(AVG(DURATION_SECONDS)) AS avg_duration_sec FROM CODE_BUNDLES_DB.PIPELINE.SESSIONS;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nExample output:\n\n```bash\n+-----------------------------------------------+\n| TOTAL_SESSIONS | CONVERTED | AVG_DURATION_SEC |\n|----------------+-----------+------------------|\n| 5952           | 3460      | 637              |\n+-----------------------------------------------+\n```\n\n\n\u003C!-- ------------------------ --\u003E\n## Run the Spark analytics\n\nIf your work is Python-only, you've already learned the core Code Bundles workflow – packaging, deploying, and running a project on Snowflake. The following sections show how the same bundle handles Spark and compute pool workloads.\n\nBoth **sessionize.py** and **spark_analytics.py** live in the same project alongside shared helpers. The bundle keeps them together as a single deployment unit – you don't create a second bundle for a second script. Instead, you run a different file with a different spec.\n\nA Spark job uses `type: spark` instead of `type: custom`, and an empty `compute_options: {}` to run on the latest Snowpark Connect client version. You can pin a specific version with `runtime_version` for reproducibility. Everything else – entrypoint, arguments, compute on a warehouse – works the same way.\n\n### Submit via the REST API\n\nThe Code Bundles REST API is the recommended way to submit Spark jobs, especially from external orchestrators like Airflow, CI/CD pipelines, or custom UIs. If you're a Spark engineer, this pattern aligns with patterns you may already use on other platforms: POST a job definition, get back an ID, poll for status.\n\nThe REST API submits jobs from a stage path rather than a stored bundle. Let's upload the project files to a stage first. Run the following commands from within the companion repo directory:\n\n```bash\nsnow sql -q \"CREATE STAGE IF NOT EXISTS CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE;\" --database CODE_BUNDLES_DB --schema PIPELINE\nsnow stage copy spark_analytics.py @CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE --database CODE_BUNDLES_DB --schema PIPELINE\nsnow stage copy helpers.py @CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nSubmit the Spark analytics job asynchronously by POSTing to the `/api/v2/code-bundle-executions` endpoint. First, generate a JWT for authentication (this requires [key-pair authentication](https://docs.snowflake.com/en/user-guide/key-pair-auth) configured on your CLI connection).\n\nRun the following in your terminal:\n\n```bash\nSNOWFLAKE_TOKEN=$(snow connection generate-jwt)\n```\n\nNow submit the job:\n\n\n```bash\ncurl -X POST \\\n  \"https://\u003Caccount_identifier\u003E.snowflakecomputing.com/api/v2/code-bundle-executions?asyncExec=true\" \\\n  -H \"Authorization: Bearer ${SNOWFLAKE_TOKEN}\" \\\n  -H \"X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT\" \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Accept: application/json\" \\\n  -H \"User-Agent: myApplicationName/1.0\" \\\n  -H \"X-Snowflake-Role: ACCOUNTADMIN\" \\\n  -H \"X-Snowflake-Warehouse: CB_WH\" \\\n  -H \"X-Snowflake-Database: CODE_BUNDLES_DB\" \\\n  -H \"X-Snowflake-Schema: PIPELINE\" \\\n  -d '{\n    \"from_location\": \"@CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE\",\n    \"entrypoint\": \"spark_analytics.py\",\n    \"arguments\": [\"--events-table\", \"CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS\",\n                   \"--funnel-output\", \"CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS\",\n                   \"--pairs-output\", \"CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS\"],\n    \"specification\": {\n      \"bundle\": {\n        \"type\": \"spark\",\n        \"compute_type\": \"warehouse\",\n        \"language\": \"python\",\n        \"compute_options\": {}\n      }\n    }\n  }'\n```\n\nAn async submission returns `202 Accepted` with a `job_id` you use to check status or cancel the run:\n\n```json\n{\n  \"code\": \"392604\",\n  \"message\": \"Request execution in progress.\",\n  \"job_id\": \"01c51743-c819-4261-0000-5349586311aa\"\n}\n```\n\nCheck status with:\n\n```bash\ncurl -X GET \\\n  \"https://\u003Caccount_identifier\u003E.snowflakecomputing.com/api/v2/code-bundle-executions/\u003Cjob_id\u003E\" \\\n  -H \"Authorization: Bearer ${SNOWFLAKE_TOKEN}\" \\\n  -H \"X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT\" \\\n  -H \"Accept: application/json\" \\\n  -H \"User-Agent: myApplicationName/1.0\" \\\n  -H \"X-Snowflake-Role: ACCOUNTADMIN\" \\\n  -H \"X-Snowflake-Database: CODE_BUNDLES_DB\" \\\n  -H \"X-Snowflake-Schema: PIPELINE\"\n```\n\n\u003E **Note:** The `X-Snowflake-Database` and `X-Snowflake-Schema` headers are required on the GET status endpoint. Without them, the response will be an empty array.\n\nFor details on the REST API, including idempotent submission and the full specification reference, see [Submit Spark jobs on Snowflake](https://docs.snowflake.com/en/developer-guide/code-bundles/spark-code-bundles#submit-with-rest).\n\n### Submit from a stage with SQL\n\nYou can also submit the Spark job with SQL instead of the REST API, which integrates natively with Snowflake Tasks for scheduling. This uses the same stage we created above:\n\n```sql\nEXECUTE CODE BUNDLE FROM '@CODE_BUNDLES_DB.PIPELINE.SPARK_STAGE'\n  ENTRYPOINT = 'spark_analytics.py'\n  ARGUMENTS  = (\n    '--events-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n    '--funnel-output', 'CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS',\n    '--pairs-output', 'CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS'\n  )\n  WITH SPECIFICATION $$\nbundle:\n  type: spark\n  compute_type: warehouse\n  language: python\n  compute_options: {}\n$$;\n```\n\n### Submit from a stored bundle\n\nAlternatively, if you've already created a stored bundle, you can run it as such:\n\n```bash\nsnow sql -q \"\nUSE DATABASE CODE_BUNDLES_DB;\nUSE SCHEMA PIPELINE;\nUSE WAREHOUSE CB_WH;\nEXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n  ENTRYPOINT = 'spark_analytics.py'\n  ARGUMENTS  = (\n    '--events-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n    '--funnel-output', 'CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS',\n    '--pairs-output', 'CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS'\n  )\n  WITH SPECIFICATION \\$\\$\nbundle:\n  type: spark\n  compute_type: warehouse\n  language: python\n  compute_options: {}\n\\$\\$;\n\" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n```\n\nNote that in the stage-based and stored-bundle examples above, the `WITH SPECIFICATION` override replaces the stored spec entirely. Every required field must be repeated, even if it hasn't changed. This is helpful when a single bundle contains files that need different runtimes. For example, a Python sessionizer and a Spark analytics job in the same project, each run with its own spec.\n\nIn our pipeline, the Spark job runs on the session warehouse and writes FUNNEL_METRICS and PRODUCT_PAIRS. The output is an empty result row, which indicates a successful run. The actual results are in the tables the job wrote – we'll query them next.\n\n\u003C!-- ------------------------ --\u003E\n## Query the results\n\nLet's explore the funnel metrics and product co-occurrence data.\n\nWhich traffic sources convert best?\n\n```bash\nsnow sql -q \"SELECT TRAFFIC_SOURCE, DEVICE, SESSIONS, CONVERSION_RATE, DROP_OFF_PCT FROM FUNNEL_METRICS WHERE STEP = 'purchase' ORDER BY CONVERSION_RATE DESC;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nYou should see clear differences: some traffic sources convert significantly better than others, and device type matters.\n\nWhere do users drop off in the funnel?\n\n```bash\nsnow sql -q \"SELECT STEP, SESSIONS, CONVERSION_RATE, DROP_OFF_PCT FROM FUNNEL_METRICS WHERE TRAFFIC_SOURCE = 'organic' AND DEVICE = 'desktop' ORDER BY STEP_ORDER;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nWatch the `SESSIONS` column shrink at each step. The `DROP_OFF_PCT` column shows what percentage of sessions failed to advance from the previous step.\n\nWhich products are frequently browsed together?\n\n```bash\nsnow sql -q \"SELECT PRODUCT_A, CATEGORY_A, PRODUCT_B, CATEGORY_B, CO_OCCURRENCE_COUNT, LIFT FROM PRODUCT_PAIRS ORDER BY LIFT DESC LIMIT 20;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nA `LIFT` score above 1.0 means the pair appears together more often than random chance. The top pairs with high lift and high co-occurrence count are your strongest \"customers also viewed\" candidates.\n\n\u003C!-- ------------------------ --\u003E\n## Orchestrate the pipeline with a Task\n\nA pipeline should run on a schedule without you manually triggering it. Because `EXECUTE CODE BUNDLE` is a SQL statement, you can wrap each stage in a Snowflake Task and chain them into a graph without needing an external scheduler.\n\nLet's create two tasks. First, the root task – it runs **sessionize.py** on a nightly schedule:\n\n```bash\nsnow sql -q \"\nCREATE OR REPLACE TASK SESSIONIZE_TASK\n  WAREHOUSE = CB_WH\n  SCHEDULE  = 'USING CRON 0 2 * * * UTC'\nAS\n  EXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n    ENTRYPOINT = 'sessionize.py'\n    ARGUMENTS  = ('--source-table', 'RAW_EVENTS',\n                  '--output-table', 'SESSIONS',\n                  '--inactivity-minutes', '30');\n\" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n```\n\nThe analytics task runs after the sessionizer succeeds. The `AFTER` clause chains them into a dependency graph:\n\n```bash\nsnow sql -q \"\nCREATE OR REPLACE TASK ANALYTICS_TASK\n  WAREHOUSE = CB_WH\n  AFTER SESSIONIZE_TASK\nAS\n  EXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n    ENTRYPOINT = 'spark_analytics.py'\n    ARGUMENTS  = (\n      '--events-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n      '--funnel-output', 'CODE_BUNDLES_DB.PIPELINE.FUNNEL_METRICS',\n      '--pairs-output', 'CODE_BUNDLES_DB.PIPELINE.PRODUCT_PAIRS'\n    )\n    WITH SPECIFICATION \\$\\$\nbundle:\n  type: spark\n  compute_type: warehouse\n  language: python\n  compute_options: {}\n\\$\\$;\n\" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n```\n\nHere's what the code does:\n\n- SESSIONIZE_TASK is the root task, scheduled nightly at 2:00 AM UTC\n- ANALYTICS_TASK runs `AFTER` the root task, forming a two-step graph\n- Each task runs one stage of the pipeline as a Code Bundle – same project, but with different entrypoints and specs\n\nTasks are created in a suspended state, by default. Resume the child task first, then the root – this ensures the graph is fully wired before the root fires. Then trigger a run to test it:\n\n```bash\nsnow sql -q \"ALTER TASK ANALYTICS_TASK RESUME; ALTER TASK SESSIONIZE_TASK RESUME; EXECUTE TASK SESSIONIZE_TASK;\" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n```\n\n\u003E **Important:** An active root task runs on its schedule and consumes credits. Suspend it with `snow sql -q \"ALTER TASK SESSIONIZE_TASK SUSPEND;\" --database CODE_BUNDLES_DB --schema PIPELINE` when you're done testing.\n\n\u003C!-- ------------------------ --\u003E\n## Operate in production\n\nGetting a job to run is one thing; operating it and iterating on it is another. Code Bundles give you versioning, asynchronous execution, and full run history out of the box.\n\n### Ship a new version\n\nAs you iterate on your pipeline – fixing a bug, tuning the sessionization window, adding a new analytics step – you need to push the latest code to Snowflake. You can ship newer versions of the Code Bundle by recreating the bundle with `--overwrite` to replace it with your current local files (equivalent to `CREATE OR REPLACE` in SQL):\n\n```bash\nsnow bundle create CLICKSTREAM_PIPELINE --source . --exclude \".venv/**\" --exclude \"setup.sql\" --overwrite --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\n\u003E **Note:** If you create your bundle from a stage or workspace instead of a local directory, you can use `snow bundle alter CLICKSTREAM_PIPELINE --add-version @STAGE/path` to add immutable version snapshots without replacing the bundle.\n\n### Run asynchronously\n\nFor long jobs, the `--async` flag submits the run and returns immediately with a query ID instead of waiting for it to finish:\n\n```bash\nsnow bundle execute CLICKSTREAM_PIPELINE --entrypoint sessionize.py --async \\\n  --database CODE_BUNDLES_DB --schema PIPELINE \\\n  -- --source-table RAW_EVENTS --output-table SESSIONS --inactivity-minutes 30\n```\n\nYou'll see output like `Request submitted. Query ID: 01c51743-c819-4261-0000-5349586311aa`. Use the ID returned to you to check on the run:\n\n```bash\n# Replace with the query ID returned by your --async run\nsnow bundle status 01c51743-c819-4261-0000-5349586311aa\n```\n\n### Review run history\n\nThe `CODE_BUNDLE_HISTORY` table function returns a record for every job run:\n\n```bash\nsnow sql -q \"SELECT ENTRYPOINT, STATUS, BUNDLE_TYPE, COMPUTE_TYPE, START_TIME, END_TIME, ERROR_MESSAGE FROM TABLE(INFORMATION_SCHEMA.CODE_BUNDLE_HISTORY(BUNDLE_NAME =\u003E 'CLICKSTREAM_PIPELINE', RESULT_LIMIT =\u003E 20)) ORDER BY START_TIME DESC;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nExample output:\n\n```bash\n+----------------------------------------------------------------------------------------------------------------------------------+\n| ENTRYPOINT         | STATUS | BUNDLE_TYPE | COMPUTE_TYPE | START_TIME                | END_TIME                  | ERROR_MESSAGE |\n|--------------------+--------+-------------+--------------+---------------------------+---------------------------+---------------|\n| spark_analytics.py | DONE   | SPARK       | WAREHOUSE    | 2026-08-27 07:52:07-07:00 | 2026-08-27 07:53:16-07:00 |               |\n| sessionize.py      | DONE   | CUSTOM      | WAREHOUSE    | 2026-08-26 15:09:22-07:00 | 2026-08-26 15:09:32-07:00 |               |\n+----------------------------------------------------------------------------------------------------------------------------------+\n```\n\n\n### Search logs and stack traces\n\nIf you configure an [event table](https://docs.snowflake.com/en/developer-guide/logging-tracing/event-table-setting-up) for your account, Code Bundle runs automatically emit application logs there – tagged with the run's query ID. You can filter by severity (`ERROR`, `FATAL`) to surface stack traces when a run fails, without needing to reproduce the issue.\n\nLook at the `STATUS` column in the history results – a successful run shows `DONE`, and a failure carries details in `ERROR_MESSAGE`. That's your production loop: version, run, monitor, debug.\n\n\u003C!-- ------------------------ --\u003E\n## (Optional) Train a model on a compute pool\n\nWhen your job requires powerful compute – training a model, running on GPUs, or installing packages from any source – a compute pool is the right compute type for your job run. Compute pools provide you with a full Python runtime powered by Snowpark Container Services.\n\nCreate a small compute pool:\n\n```bash\nsnow sql -q \"CREATE COMPUTE POOL IF NOT EXISTS CB_ML_POOL MIN_NODES = 1 MAX_NODES = 1 INSTANCE_FAMILY = CPU_X64_S;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nWait for the pool to reach IDLE or ACTIVE state before proceeding (this typically takes 1–3 minutes):\n\n```bash\nsnow sql -q \"DESCRIBE COMPUTE POOL CB_ML_POOL;\" --database CODE_BUNDLES_DB --schema PIPELINE\n```\n\nHere's the compute pool specification – compare it to the warehouse and Spark specs we've used so far. The key differences are `compute_type: compute_pool`, a named pool, and a Container Runtime version:\n\n```yaml\nbundle:\n  type: custom\n  compute_type: compute_pool\n  language: python\n  compute_options:\n    compute_pool: CB_ML_POOL\n    query_warehouse: CB_WH\n    runtime_version: 'V2.5-CPU-PY3.11'\n```\n\n**train_model.py** trains a scikit-learn model to predict purchase amount from product and category, then logs it to the Snowflake Model Registry:\n\n```python\nimport argparse\nimport pandas as pd\nfrom snowflake.ml.registry import Registry\nfrom snowflake.snowpark.context import get_active_session\n\ndef main() -\u003E None:\n    args = parse_args()  # --source-table, --database, --schema, --model-name, --version\n    session = get_active_session()\n\n    rows = session.table(args.source_table).filter(\n        \"EVENT_TYPE = 'purchase'\"\n    ).select(\"PRODUCT_ID\", \"CATEGORY\", \"REVENUE\").collect()\n    df = pd.DataFrame([row.as_dict() for row in rows])\n    # ... fit a scikit-learn pipeline ...\n    registry = Registry(session=session, database_name=args.database, schema_name=args.schema)\n    registry.log_model(model, model_name=args.model_name, version_name=args.version,\n                       sample_input_data=X.head())\n```\n\nRun it with `WITH SPECIFICATION` to override the stored warehouse spec, just like we did for the Spark job:\n\n```bash\nsnow sql -q \"\nUSE DATABASE CODE_BUNDLES_DB;\nUSE SCHEMA PIPELINE;\nUSE WAREHOUSE CB_WH;\nEXECUTE CODE BUNDLE CLICKSTREAM_PIPELINE\n  ENTRYPOINT = 'train_model.py'\n  ARGUMENTS  = (\n    '--source-table', 'CODE_BUNDLES_DB.PIPELINE.RAW_EVENTS',\n    '--database', 'CODE_BUNDLES_DB',\n    '--schema', 'PIPELINE'\n  )\n  WITH SPECIFICATION \\$\\$\nbundle:\n  type: custom\n  compute_type: compute_pool\n  language: python\n  compute_options:\n    compute_pool: CB_ML_POOL\n    query_warehouse: CB_WH\n    runtime_version: 'V2.5-CPU-PY3.11'\n\\$\\$;\n\" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n```\n\nHere's what the code does:\n\n- Reads purchase events directly from Snowflake with the injected session\n- Trains the model in the Python process on the compute pool\n- Registers the model so it's versioned and ready for batch inference\n\nA successful run will return a confirmation that the statement was executed successfully.\n\n\u003C!-- ------------------------ --\u003E\n## Clean up\n\nLet's remove everything we created. First, suspend and drop the tasks, then the bundle and remaining objects:\n\n```bash\nsnow sql -q \"\nALTER TASK IF EXISTS SESSIONIZE_TASK SUSPEND;\nDROP TASK IF EXISTS ANALYTICS_TASK;\nDROP TASK IF EXISTS SESSIONIZE_TASK;\nDROP CODE BUNDLE IF EXISTS CLICKSTREAM_PIPELINE;\nDROP COMPUTE POOL IF EXISTS CB_ML_POOL;\nDROP DATABASE IF EXISTS CODE_BUNDLES_DB;\nDROP WAREHOUSE IF EXISTS CB_WH;\n\" --database CODE_BUNDLES_DB --schema PIPELINE --warehouse CB_WH\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nCongratulations! You migrated a clickstream pipeline onto Snowflake as a Code Bundle – without rewriting your jobs as stored procedures. You sessionized 50K events on a warehouse, ran PySpark analytics as a native Spark job from the same bundle, orchestrated both stages with a Task, and operated the pipeline with versioning, async execution, and run history.\n\nThe theme throughout was matching compute to the job – a warehouse for the SQL-heavy sessionization, a Spark job for the combinatorial analytics – while keeping the project together as one deployment unit. You deployed once and ran different files with different runtimes, without duplicating shared code or decomposing into one object per script.\n\n### What You Learned\n- How to package a project as a Code Bundle and run it on Snowflake compute\n- How to run a Python job on a warehouse and a PySpark job as a native Spark job\n- How to override a bundle's specification at execution time with `WITH SPECIFICATION`\n- How to orchestrate a multi-stage pipeline with a Snowflake Task\n- How to version, run asynchronously, and monitor Code Bundle jobs in production\n\n### Related Resources\n- [Snowflake Code Bundles documentation](https://docs.snowflake.com/en/developer-guide/code-bundles/code-bundles)\n- [Submit Spark jobs on Snowflake](https://docs.snowflake.com/en/developer-guide/code-bundles/spark-code-bundles)\n- [Code Bundles Quickstart companion repo](https://github.com/Snowflake-Labs/sfguide-run-python-and-spark-jobs-with-code-bundles)\n- [Snowflake Tasks](https://docs.snowflake.com/en/user-guide/tasks-intro)\n- [Snowpark Connect for Apache Spark](https://docs.snowflake.com/en/developer-guide/snowpark-connect/snowpark-connect-overview)\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-92d7901d36","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-bba06e7f8d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-b6d8c2c63c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-07",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-93ce085abe","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 instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-95a5261d48",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-4df52b356a",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-42c88e567a","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-38a6baa0b4","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EWhat are Code Bundles?\u003C/h2\u003E","\u003Ch2\u003ESet up your account\u003C/h2\u003E","\u003Ch2\u003EExplore the pipeline code\u003C/h2\u003E","\u003Ch2\u003ERun the sessionizer on a warehouse\u003C/h2\u003E","\u003Ch2\u003ERun the Spark analytics\u003C/h2\u003E","\u003Ch2\u003EQuery the results\u003C/h2\u003E","\u003Ch2\u003EOrchestrate the pipeline with a Task\u003C/h2\u003E","\u003Ch2\u003EOperate in production\u003C/h2\u003E","\u003Ch2\u003E(Optional) Train a model on a compute pool\u003C/h2\u003E","\u003Ch2\u003EClean up\u003C/h2\u003E","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E"],"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/run-python-and-spark-jobs-with-snowflake-code-bundles",":type":"snowflake-site/components/quickstart/quickstart-table-of-content"},"quickstart_button":{"id":"quickstart-button-5bc19e6562","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/run-python-and-spark-jobs-with-snowflake-code-bundles",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"markup_editor":{"id":"markup-editor-95054d94e1","title":"Page 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