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Warehouse\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EEverything runs inside a \u003Cstrong\u003EGitHub Codespace\u003C/strong\u003E — no local installs required beyond a browser and a Snowflake account.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interactive-tables-snowpipe-streaming-arcade-lab/architecture.png?v=6101be7f\" alt=\"Architecture diagram\"\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E Python Generator\n ─────────────────\n  Arcade Score Events          Snowpipe Streaming SDK\n  (unlimited rows/sec) ──────► StreamingIngestClient\n  20 games · 45 cities          └─ Channel 0      ──► ARCADE_SCORES\n  500 players                                          (Interactive Table)\n                                                       CLUSTER BY (GAME_ENDED_AT)\n                                                            │\n                                                            ▼\n                                                     SUMMIT_INT_WH\n                                                  (Interactive Warehouse, XS)\n                                                  Always-on · sub-second latency queries\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowpipe Streaming uses the \u003Cstrong\u003Echannel API\u003C/strong\u003E — rows are written directly into the Interactive Table without any intermediate landing table, staging area, or \u003Ccode\u003ECOPY INTO\u003C/code\u003E command.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/\"\u003ESnowflake account\u003C/a\u003E in a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive#region-availability\"\u003Esupported Interactive Tables region\u003C/a\u003E with \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E access\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to stream rows into Snowflake using the \u003Cstrong\u003ESnowpipe Streaming Python SDK\u003C/strong\u003E channel API\u003C/li\u003E\u003Cli\u003EHow \u003Cstrong\u003EInteractive Tables\u003C/strong\u003E differ from standard Snowflake tables and why they enable sub-second queries\u003C/li\u003E\u003Cli\u003EHow an \u003Cstrong\u003EInteractive Warehouse\u003C/strong\u003E uses pre-computed indexes and a warm local SSD cache to answer queries quickly\u003C/li\u003E\u003Cli\u003EHow \u003Ccode\u003ECLUSTER BY\u003C/code\u003E on a timestamp column eliminates irrelevant micro-partition scans on time-range queries\u003C/li\u003E\u003Cli\u003EHow to measure real-time data \u003Cstrong\u003Efreshness\u003C/strong\u003E from within Snowsight\u003C/li\u003E\u003Cli\u003EHow the Interactive Warehouse handles \u003Cstrong\u003E50 concurrent users\u003C/strong\u003E without query queuing\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://github.com/\"\u003EGitHub account\u003C/a\u003E (free tier is sufficient for Codespaces)\u003C/li\u003E\u003Cli\u003EA web browser — no local installs required; Python, the Snowpipe Streaming SDK, and all other dependencies are installed automatically in the Codespace\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA live arcade score streaming pipeline (Python → Snowpipe Streaming → Interactive Table)\u003C/li\u003E\u003Cli\u003E11 analytical exercises covering freshness, leaderboards, geo heat maps, and concurrency benchmarking\u003C/li\u003E\u003Cli\u003EA side-by-side speed comparison between the Interactive Warehouse and a standard warehouse\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOpen the Lab in a GitHub Codespace\u003C/h2\u003E\n","\u003Cp\u003EAll lab code lives in a public GitHub repository. GitHub Codespaces gives you a fully configured cloud development environment — Python, the Snowpipe Streaming SDK, and all dependencies are installed automatically.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 — Open the Codespace\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Ca href=\"https://github.com/Snowflake-Labs/Summit26-InteractiveLab\"\u003Ehttps://github.com/Snowflake-Labs/Summit26-InteractiveLab\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EClick the green \u003Cstrong\u003ECode\u003C/strong\u003E button → \u003Cstrong\u003ECodespaces\u003C/strong\u003E tab → \u003Cstrong\u003ECreate codespace on main\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EGitHub will build the container and open VS Code in your browser. This takes about 1–2 minutes on first launch.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe Codespace automatically installs the \u003Ccode\u003Esnowpipe-streaming\u003C/code\u003E Python package and all other dependencies from \u003Ccode\u003Erequirements.txt\u003C/code\u003E — no manual \u003Ccode\u003Epip install\u003C/code\u003E needed.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 — Explore the repository structure\u003C/h3\u003E\n","\u003Cp\u003EOnce the Codespace is open, you will see:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESummit26-InteractiveLab/\n├── sql/\n│   ├── 01_setup.sql          Snowflake object provisioning\n│   ├── 02_service_auth.sh    RSA key generation + ALTER USER SQL\n│   ├── 03_lab_queries.sql    All 11 exercises + bonus queries\n│   ├── 04_generate_pat.sql   Optional: PAT for Cortex CLI dashboard deployment\n│   └── 05_cleanup.sql        Teardown script\n├── python/\n│   ├── arcade_streamer.py    Snowpipe Streaming SDK ingest\n│   ├── generator.py          Synthetic arcade data generator\n│   └── config.py             All tuneable parameters\n├── jmeter/                   Concurrency test plan (optional)\n├── profile.json.example      Snowflake connection template\n└── requirements.txt\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EProvision Snowflake Objects\u003C/h2\u003E\n","\u003Cp\u003EAll Snowflake objects are created by a single SQL script. Run it in Snowsight using a \u003Cstrong\u003Estandard warehouse\u003C/strong\u003E session.\u003C/p\u003E\n","\u003Ch3\u003EWhat gets provisioned\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EObject\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_STREAMING_ROLE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMinimum-privilege role for the streaming user\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_STREAMING_USER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUser\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EService account used by the Python SDK\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_KEYPAIR_POLICY\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAuth Policy\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERestricts the service user to key-pair auth only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_DB\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDatabase\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHolds all lab objects\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESUMMIT_TRAD_WH\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStandard Warehouse XS\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESetup tasks; comparison benchmarks\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_SCORES\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EInteractive Table\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDirect streaming target, \u003Ccode\u003ECLUSTER BY (GAME_ENDED_AT)\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESUMMIT_INT_WH\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EInteractive Warehouse XS\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAll lab queries — always-on, sub-second latency\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_REPORTING_POOL\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompute Pool\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFor the optional Streamlit dashboard\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EARCADE_LAB_READER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERead-only role for lab attendees\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EStep 1 — Open Snowsight\u003C/h3\u003E\n","\u003Cp\u003ELog in to your Snowflake account at \u003Ca href=\"https://app.snowflake.com\"\u003Eapp.snowflake.com\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 — Run the setup script\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EIn the Codespace, open \u003Cstrong\u003E\u003Ccode\u003Esql/01_setup.sql\u003C/code\u003E\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECopy the entire file contents\u003C/li\u003E\u003Cli\u003EIn Snowsight, open a new SQL worksheet, paste, and click \u003Cstrong\u003ERun All\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Run the script with a \u003Cstrong\u003EStandard\u003C/strong\u003E warehouse session (e.g. \u003Ccode\u003ESUMMIT_TRAD_WH\u003C/code\u003E once created, or any existing standard warehouse). \u003Ccode\u003ECREATE INTERACTIVE TABLE\u003C/code\u003E is not supported from an Interactive Warehouse session.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWhen the script completes you will see:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESetup complete – start the Python streamer, then wait ~2 min for cache warm-up.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat makes an Interactive Table different?\u003C/h3\u003E\n","\u003Cp\u003EInteractive Tables are optimised for low-latency streaming ingestion and sub-second lookups. Key properties:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDirect streaming writes\u003C/strong\u003E — Snowpipe Streaming SDK writes rows without staging files\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAlways-queryable\u003C/strong\u003E — rows are visible immediately after commit, no \u003Ccode\u003EREFRESH\u003C/code\u003E needed\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPre-computed indexes\u003C/strong\u003E — the Interactive Warehouse maintains in-memory index metadata\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETime Travel\u003C/strong\u003E — fully supported even with continuous streaming writes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003ECLUSTER BY\u003C/code\u003E\u003C/strong\u003E — aligns micro-partitions with query predicates; the warehouse skips irrelevant partitions automatically\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERegister the RSA Key Pair\u003C/h2\u003E\n","\u003Cp\u003EThe Python streamer authenticates as \u003Ccode\u003EARCADE_STREAMING_USER\u003C/code\u003E using RSA key-pair authentication. A shell script generates the key pair and prints the exact SQL needed to register the public key.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 — Run the keygen script\u003C/h3\u003E\n","\u003Cp\u003EIn the Codespace terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ebash sql/02_service_auth.sh\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExecute the SQL which is output from the script in your Snowflake account.\u003C/p\u003E\n","\u003Cp\u003EThis script:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGenerates \u003Ccode\u003Ersa_key.p8\u003C/code\u003E (private key) and \u003Ccode\u003Ersa_key.pub\u003C/code\u003E (public key) if they don't already exist\u003C/li\u003E\u003Cli\u003ECreates a network policy to allow the codespace to communicate with the service account\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003ESets public key for the service account\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EPrints a ready-to-use \u003Ccode\u003Eprofile.json\u003C/code\u003E block with the absolute path to \u003Ccode\u003Ersa_key.p8\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ESave the json from the output of this script for the next step\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure the Connection Profile\u003C/h2\u003E\n","\u003Cp\u003EThe streamer reads connection details from \u003Ccode\u003Eprofile.json\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 — Create profile.json\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp profile.json.example profile.json\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen the profile.json in the editor and paste in the json configuration generated by the sql script in the previous step.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHow the Snowpipe Streaming SDK Works\u003C/h2\u003E\n","\u003Cp\u003EBefore running the streamer, let's walk through the key parts of \u003Ccode\u003Epython/arcade_streamer.py\u003C/code\u003E to understand exactly how rows get from Python into Snowflake — no files, no \u003Ccode\u003ECOPY INTO\u003C/code\u003E, no batch window.\u003C/p\u003E\n","\u003Ch3\u003E1 — Create the client\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EStreamingIngestClient\u003C/code\u003E is the top-level SDK object. One client maps to exactly one account / database / schema / pipe combination. It handles authentication, channel lifecycle, and the underlying HTTPS transport to Snowflake's ingest endpoint.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# arcade_streamer.py  (main)\nfrom snowflake.ingest.streaming import StreamingIngestClient\n\nwith StreamingIngestClient(\n    client_name  = f"ARCADE_CLIENT_{uuid.uuid4().hex[:8].upper()}",\n    db_name      = config.SNOWFLAKE_DATABASE,   # "ARCADE_DB"\n    schema_name  = config.SNOWFLAKE_SCHEMA,     # "PUBLIC"\n    pipe_name    = config.SNOWFLAKE_PIPE,        # "ARCADE_SCORES-STREAMING"\n    profile_json = args.profile,                 # path to profile.json\n) as client:\n    ...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Eprofile_json\u003C/code\u003E points at the \u003Ccode\u003Eprofile.json\u003C/code\u003E you just created — the client reads the RSA private key path from there and signs its own JWT tokens for every request.\u003C/p\u003E\n","\u003Ch3\u003E2 — Open a channel\u003C/h3\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Echannel\u003C/strong\u003E is the logical write path within a client. Each channel maintains its own internal write buffer and commit position. Opening a channel is idempotent — if a channel with the same name already exists (e.g. after a restart), the SDK resumes from the last committed offset token.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# arcade_streamer.py  (channel_worker)\nchannel_name = f"ARCADE_CHANNEL_{channel_id}_{uuid.uuid4().hex[:8].upper()}"\n\nwith client.open_channel(channel_name)[0] as channel:\n    print(f"  [channel-{channel_id}] opened: {channel.channel_name}")\n    ...\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Eopen_channel()\u003C/code\u003E returns \u003Ccode\u003E(StreamingIngestChannel, ChannelStatus)\u003C/code\u003E — the \u003Ccode\u003E[0]\u003C/code\u003E unwraps the channel. The context manager automatically closes and flushes the channel when the \u003Ccode\u003Ewith\u003C/code\u003E block exits.\u003C/p\u003E\n","\u003Ch3\u003E3 — Append rows\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Eappend_row()\u003C/code\u003E is the core ingestion call. It accepts a plain Python \u003Ccode\u003Edict\u003C/code\u003E where \u003Cstrong\u003Ekeys are column names\u003C/strong\u003E (case-insensitive) and \u003Cstrong\u003Evalues are native Python types\u003C/strong\u003E. No serialisation to JSON or Parquet needed — the SDK handles that internally.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# arcade_streamer.py  (channel_worker inner loop)\nbatch = generate_batch(rows_per_batch)   # list of row dicts from generator.py\n\nfor row in batch:\n    channel.append_row(row, str(offset_token))\n    offset_token += 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach call to \u003Ccode\u003Eappend_row\u003C/code\u003E is non-blocking. The SDK buffers rows in memory and flushes them to Snowflake in the background, automatically respecting Snowflake's micro-batch commit cadence.\u003C/p\u003E\n","\u003Ch3\u003E4 — What a row looks like\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Egenerator.py\u003C/code\u003E produces rows as plain Python dicts. The dict keys map directly to \u003Ccode\u003EARCADE_SCORES\u003C/code\u003E column names:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# generator.py  (generate_score)\nreturn {\n    "score_id":         str(uuid.uuid4()),   # VARCHAR(36)\n    "player_id":        player["player_id"], # VARCHAR(36)\n    "player_name":      player["player_name"],\n    "player_country":   player["player_country"],\n    "player_city":      player["player_city"],\n    "latitude":         player["latitude"],  # FLOAT\n    "longitude":        player["longitude"],\n    "game_name":        game_name,\n    "game_mode":        game_mode,\n    "platform":         platform,\n    "score":            score,               # NUMBER(12,0)\n    "level_reached":    level,\n    "duration_seconds": duration_sec,\n    "lives_remaining":  lives,\n    "accuracy_pct":     accuracy,            # nullable FLOAT\n    "achievement":      achievement,         # nullable VARCHAR(64)\n    "game_ended_at":    game_ended_at,       # TIMESTAMP_NTZ (UTC datetime)\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe SDK maps Python \u003Ccode\u003Eint\u003C/code\u003E → \u003Ccode\u003ENUMBER\u003C/code\u003E, \u003Ccode\u003Efloat\u003C/code\u003E → \u003Ccode\u003EFLOAT\u003C/code\u003E, \u003Ccode\u003Estr\u003C/code\u003E → \u003Ccode\u003EVARCHAR\u003C/code\u003E, \u003Ccode\u003Edatetime\u003C/code\u003E → \u003Ccode\u003ETIMESTAMP_NTZ\u003C/code\u003E, and \u003Ccode\u003ENone\u003C/code\u003E → \u003Ccode\u003ENULL\u003C/code\u003E automatically.\u003C/p\u003E\n","\u003Ch3\u003E5 — Offset tokens\u003C/h3\u003E\n","\u003Cp\u003EEvery \u003Ccode\u003Eappend_row\u003C/code\u003E call takes an \u003Cstrong\u003Eoffset token\u003C/strong\u003E — a monotonically increasing string the SDK uses to track exactly which rows have been durably committed on the Snowflake side. If the streamer crashes and restarts, it can reopen the same channel and resume from \u003Ccode\u003Echannel.get_latest_committed_offset_token()\u003C/code\u003E, avoiding duplicate inserts.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# offset_token increments with every row:\nchannel.append_row(row, str(offset_token))\noffset_token += 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E6 — Latency monitor\u003C/h3\u003E\n","\u003Cp\u003EA background thread calls \u003Ccode\u003Eclient.get_channel_statuses()\u003C/code\u003E periodically to retrieve Snowflake's reported average processing latency — the time between \u003Ccode\u003Eappend_row\u003C/code\u003E being called and the row being durably committed and queryable in the Interactive Table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# arcade_streamer.py  (latency_monitor)\nstatuses = client.get_channel_statuses(names)\nfor name, st in statuses.items():\n    lag = st.server_avg_processing_latency   # datetime.timedelta | None\n    if lag is not None:\n        print(f"[latency] {name}: {lag.total_seconds() * 1000:.0f} ms avg")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is the \u003Ccode\u003E[latency]\u003C/code\u003E line you will see in the console once the streamer is running.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPreview the data without connecting to Snowflake\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EBefore starting the real streamer you can preview a few generated rows to confirm the generator is working:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd python\npython arcade_streamer.py --dry-run --rows 3\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EStart the Arcade Streamer\u003C/h2\u003E\n","\u003Cp\u003EWith the connection configured, start the Python streamer. It will immediately begin writing arcade score events to Snowflake at full speed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd python\npython arcade_streamer.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see output like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E============================================================\n Summit 2026 – Arcade Scores Snowpipe Streamer\n============================================================\n  Account   : YOUR_ORG-YOUR_ACCOUNT\n  Database  : ARCADE_DB.PUBLIC\n  Pipe      : ARCADE_SCORES-STREAMING\n  Channels  : 1\n  Target    : unlimited rows/sec\n============================================================\n\n  [14:22:05]  rows:      512  |  512.0 rows/sec  |  errors: 0  |  elapsed:    1s\n  [14:22:05]  [latency] ARCADE_CHANNEL_0_A3F2B1C4: 540 ms avg\n  [14:22:10]  rows:    1,024  |  512.0 rows/sec  |  errors: 0  |  elapsed:    6s\n  [14:22:10]  [latency] ARCADE_CHANNEL_0_A3F2B1C4: 512 ms avg\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe \u003Ccode\u003E[latency]\u003C/code\u003E line is the \u003Cstrong\u003ESnowflake-reported average processing latency\u003C/strong\u003E polled from the SDK's \u003Ccode\u003Eget_channel_statuses()\u003C/code\u003E API — typically under a second.\u003C/p\u003E\n","\u003Ch3\u003EWait for cache warm-up\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EAfter the streamer starts, wait \u003Cstrong\u003E2–3 minutes\u003C/strong\u003E before running lab queries. The \u003Ccode\u003ESUMMIT_INT_WH\u003C/code\u003E Interactive Warehouse needs this time to populate its local SSD cache. Queries run before warm-up will still succeed but will be slower.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExercise 1 — Pipeline Throughput\u003C/h2\u003E\n","\u003Cp\u003EOpen \u003Cstrong\u003E\u003Ccode\u003Esql/03_lab_queries.sql\u003C/code\u003E\u003C/strong\u003E in Snowsight and run the Exercise 1 queries while the streamer is running. Make sure \u003Ccode\u003ESUMMIT_INT_WH\u003C/code\u003E is selected as the active warehouse.\u003C/p\u003E\n","\u003Ch3\u003E1a — Total row count\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) AS TOTAL_SCORES FROM ARCADE_SCORES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERe-run this every few seconds. You should see the count climbing as new rows stream in.\u003C/p\u003E\n","\u003Ch3\u003E1b — Rows per second (last 60 seconds)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    COUNT(*)                   AS ROWS_LAST_60_SEC,\n    ROUND(COUNT(*) / 60.0, 1) AS ROWS_PER_SECOND,\n    COUNT(DISTINCT PLAYER_ID)  AS ACTIVE_PLAYERS\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('second', -60,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Ccode\u003ECONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\u003C/code\u003E\u003C/strong\u003E normalises the session-local \u003Ccode\u003ECURRENT_TIMESTAMP()\u003C/code\u003E to UTC so it compares correctly against the UTC \u003Ccode\u003ETIMESTAMP_NTZ\u003C/code\u003E values in \u003Ccode\u003EGAME_ENDED_AT\u003C/code\u003E. You'll see this pattern in every time-filtered query throughout the lab.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003E1c — Throughput by 10-second bucket (last 3 minutes)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    DATEADD('second',\n        FLOOR(DATEDIFF('second', '2000-01-01'::TIMESTAMP_NTZ, GAME_ENDED_AT) / 10) * 10,\n        '2000-01-01'::TIMESTAMP_NTZ)   AS TIME_BUCKET,\n    COUNT(*)                           AS SCORES\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('minute', -3,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY TIME_BUCKET\nORDER BY TIME_BUCKET DESC\nLIMIT 18;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis histogram shows you the exact shape of the ingest stream — each bucket represents 10 seconds of data arriving from the Python generator. Notice how rows are already in the Interactive Table by the time the query runs: there is no batch window, no \u003Ccode\u003ECOPY INTO\u003C/code\u003E, and no manual refresh.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExercise 2 — Data Freshness\u003C/h2\u003E\n","\u003Cp\u003EHow stale is the most recent row in the table right now?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    MAX(GAME_ENDED_AT)                                                    AS LATEST_GAME_ENDED,\n    DATEDIFF(\n        'millisecond',\n        MAX(GAME_ENDED_AT),\n        CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\n    ) / 1000.0                                                            AS FRESHNESS_SEC\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('minute', -5,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EGAME_ENDED_AT\u003C/code\u003E is the UTC timestamp when the Python generator emitted the row. \u003Ccode\u003EFRESHNESS_SEC\u003C/code\u003E is how many seconds ago that was — the end-to-end pipeline latency from the Python process to a queryable row in Snowflake.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis query uses the same \u003Ccode\u003ECONVERT_TIMEZONE('UTC', ...)\u003C/code\u003E pattern introduced in Exercise 1.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EWith a healthy streamer you should see freshness values in the range of \u003Cstrong\u003E0.3 – 2 seconds\u003C/strong\u003E, depending on Snowflake region and network latency.\u003C/p\u003E\n","\u003Cp\u003EThe streamer console also logs the SDK-reported average processing latency per channel (the \u003Ccode\u003E[latency]\u003C/code\u003E lines) — compare that to the SQL freshness value.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExercise 3 — Global Leaderboard\u003C/h2\u003E\n","\u003Cp\u003EWho has the highest scores across all games in the last 24 hours?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    RANK() OVER (ORDER BY SCORE DESC)  AS RANK,\n    PLAYER_NAME,\n    PLAYER_COUNTRY,\n    PLAYER_CITY,\n    GAME_NAME,\n    TO_CHAR(SCORE, '999,999,999')      AS SCORE,\n    LEVEL_REACHED,\n    PLATFORM,\n    COALESCE(ACHIEVEMENT, '—')         AS ACHIEVEMENT,\n    GAME_ENDED_AT\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -24,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nORDER BY SCORE DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENotice the query execution time in Snowsight's query history — it should be \u003Cstrong\u003Esub-second\u003C/strong\u003E even with hundreds of thousands of rows in the table.\u003C/p\u003E\n","\u003Cp\u003EThis is the \u003Ccode\u003ECLUSTER BY (GAME_ENDED_AT)\u003C/code\u003E effect: the Interactive Warehouse knows exactly which micro-partitions contain rows from the last 24 hours and skips everything else. No full table scan occurs.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry it without the \u003Ccode\u003EWHERE\u003C/code\u003E clause.\u003C/strong\u003E Remove the \u003Ccode\u003EGAME_ENDED_AT\u003C/code\u003E filter and re-run. Snowsight will show the query hitting the 5-second Interactive Warehouse timeout — which is the expected, by-design behaviour that enforces efficient query patterns.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExercise 4 — Per-Game Top 5\u003C/h2\u003E\n","\u003Cp\u003EUse \u003Ccode\u003EQUALIFY ROW_NUMBER()\u003C/code\u003E to rank the top 5 players per game in the last hour without a subquery:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    GAME_NAME,\n    ROW_NUMBER() OVER (\n        PARTITION BY GAME_NAME ORDER BY SCORE DESC\n    )                                  AS POSITION,\n    PLAYER_NAME,\n    PLAYER_COUNTRY,\n    TO_CHAR(SCORE, '999,999,999')      AS SCORE,\n    LEVEL_REACHED,\n    GAME_MODE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nQUALIFY ROW_NUMBER() OVER (\n    PARTITION BY GAME_NAME ORDER BY SCORE DESC\n) <= 5\nORDER BY GAME_NAME, POSITION;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003EQUALIFY\u003C/code\u003E is Snowflake's inline filter for window functions — no outer \u003Ccode\u003ESELECT\u003C/code\u003E wrapper needed. The Interactive Warehouse handles 20 game partitions × 5 positions in a single pass.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExercises 5–9 — Deeper Analysis\u003C/h2\u003E\n","\u003Cp\u003EThese exercises explore the data from different angles. Open \u003Cstrong\u003E\u003Ccode\u003Esql/03_lab_queries.sql\u003C/code\u003E\u003C/strong\u003E and run the queries for each exercise.\u003C/p\u003E\n","\u003Ch3\u003EExercise 5 — Country Heat Map\u003C/h3\u003E\n","\u003Cp\u003EWhich countries are generating the most play sessions right now?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    PLAYER_COUNTRY,\n    COUNT(*)                   AS GAMES_PLAYED,\n    ROUND(AVG(SCORE))          AS AVG_SCORE,\n    MAX(SCORE)                 AS HIGH_SCORE,\n    COUNT(DISTINCT PLAYER_ID)  AS UNIQUE_PLAYERS\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_COUNTRY\nORDER BY GAMES_PLAYED DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EJapan and South Korea lead — the data generator weights cities by real-world gaming culture.\u003C/p\u003E\n","\u003Ch3\u003EExercise 6 — Game Popularity\u003C/h3\u003E\n","\u003Cp\u003EWhich games have the most sessions? How do average scores compare across titles?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    GAME_NAME,\n    COUNT(*)                                        AS SESSIONS,\n    COUNT(DISTINCT PLAYER_ID)                       AS UNIQUE_PLAYERS,\n    TO_CHAR(ROUND(AVG(SCORE)), '999,999,999')       AS AVG_SCORE,\n    TO_CHAR(MAX(SCORE),        '999,999,999')       AS HIGH_SCORE,\n    ROUND(AVG(LEVEL_REACHED), 1)                    AS AVG_LEVEL,\n    ROUND(AVG(DURATION_SECONDS) / 60.0, 1)          AS AVG_GAME_MIN\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY GAME_NAME\nORDER BY SESSIONS DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPac-Man and Tetris should lead with roughly 5× more sessions than rarer titles like Joust and Tron.\u003C/p\u003E\n","\u003Ch3\u003EExercise 7 — Platform Breakdown\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    PLATFORM,\n    COUNT(*)                   AS SESSIONS,\n    ROUND(AVG(SCORE))          AS AVG_SCORE,\n    ROUND(AVG(ACCURACY_PCT), 1) AS AVG_ACCURACY_PCT,\n    COUNT(DISTINCT PLAYER_ID)  AS UNIQUE_PLAYERS\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLATFORM\nORDER BY SESSIONS DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClassic arcade cabinets dominate for titles like Pac-Man; Tetris skews mobile.\u003C/p\u003E\n","\u003Ch3\u003EExercise 8 — Live Score Feed (re-run repeatedly!)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    PLAYER_NAME,\n    PLAYER_CITY,\n    PLAYER_COUNTRY,\n    GAME_NAME,\n    TO_CHAR(SCORE, '999,999,999')  AS SCORE,\n    LEVEL_REACHED,\n    PLATFORM,\n    COALESCE(ACHIEVEMENT, '—')     AS ACHIEVEMENT,\n    GAME_ENDED_AT\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('minute', -5,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nORDER BY GAME_ENDED_AT DESC\nLIMIT 30;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERe-run every few seconds. You'll see new rows appearing at the top — these are rows that streamed in after your last query execution. No refresh, no polling interval: the table is live.\u003C/p\u003E\n","\u003Ch3\u003EExercise 9 — Achievement Rarity\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    ACHIEVEMENT,\n    COUNT(*)                   AS TIMES_EARNED,\n    COUNT(DISTINCT PLAYER_ID)  AS UNIQUE_EARNERS,\n    ROUND(AVG(SCORE))          AS AVG_SCORE_WHEN_EARNED,\n    ROUND(AVG(LEVEL_REACHED), 1) AS AVG_LEVEL\nFROM ARCADE_SCORES\nWHERE ACHIEVEMENT IS NOT NULL\n  AND GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY ACHIEVEMENT\nORDER BY TIMES_EARNED ASC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAchievements like "Pacifist" and "Triple Threat" require specific skill tier + game mode + score conditions and appear in fewer than 1 in 200 rows.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExercise 10 — Interactive vs Traditional Warehouse\u003C/h2\u003E\n","\u003Cp\u003ERun the same aggregation query on both warehouses back-to-back and compare execution times in Query History.\u003C/p\u003E\n","\u003Ch3\u003EStep A — Interactive Warehouse\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE SUMMIT_INT_WH;\n\nSELECT\n    PLAYER_COUNTRY,\n    COUNT(*)    AS SESSIONS,\n    MAX(SCORE)  AS HIGH_SCORE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_COUNTRY\nORDER BY SESSIONS DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep B — Standard Warehouse (identical query)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE SUMMIT_TRAD_WH;\n\nSELECT\n    PLAYER_COUNTRY,\n    COUNT(*)    AS SESSIONS,\n    MAX(SCORE)  AS HIGH_SCORE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_COUNTRY\nORDER BY SESSIONS DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECompare in Query History\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Cstrong\u003EActivity → Query History\u003C/strong\u003E in Snowsight and compare the two query durations side by side.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWarehouse\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETypical latency\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhy\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESUMMIT_INT_WH\u003C/code\u003E (Interactive)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Esub-second\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPre-computed indexes + local SSD cache; skips most micro-partitions via clustering\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESUMMIT_TRAD_WH\u003C/code\u003E (Standard)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Eseveral seconds\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMicro-partition scan on first run\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe Interactive Warehouse achieves significantly higher throughput and lower latency at high concurrency because it uses a dedicated shared SSD cache and pre-computed index metadata that survives between queries.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESwitch back to the Interactive Warehouse\u003C/strong\u003E before continuing:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE SUMMIT_INT_WH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBonus Exercises\u003C/h2\u003E\n","\u003Ch3\u003EBonus A — Time Travel on an Interactive Table\u003C/h3\u003E\n","\u003Cp\u003EInteractive Tables support Time Travel even with continuous streaming writes.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Row count 5 minutes ago\nSELECT COUNT(*) AS ROWS_5_MIN_AGO\nFROM ARCADE_SCORES AT(OFFSET => -300);\n\n-- How many rows were generated in the last 5 minutes?\nSELECT COUNT(*) AS NEW_ROWS_LAST_5_MIN\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('minute', -5,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBonus B — Rolling 1-Minute City Hotspot\u003C/h3\u003E\n","\u003Cp\u003EWhich cities are most active right now?\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    PLAYER_CITY,\n    PLAYER_COUNTRY,\n    COUNT(*) AS GAMES_LAST_MINUTE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT >= DATEADD('minute', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_CITY, PLAYER_COUNTRY\nORDER BY GAMES_LAST_MINUTE DESC\nLIMIT 15;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EBonus C — Inspect Interactive Table Metadata\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE SUMMIT_TRAD_WH;\nUSE ROLE ACCOUNTADMIN;\n\nSHOW INTERACTIVE TABLES IN SCHEMA ARCADE_DB.PUBLIC;\n\n-- JSON clustering stats for GAME_ENDED_AT (overlap depth, overlaps, partition counts, histogram)\nSELECT SYSTEM$CLUSTERING_INFORMATION('ARCADE_DB.PUBLIC.ARCADE_SCORES', '(GAME_ENDED_AT)');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/system_clustering_information\"\u003E\u003Ccode\u003ESYSTEM$CLUSTERING_INFORMATION\u003C/code\u003E\u003C/a\u003E returns a \u003Cstrong\u003EJSON string\u003C/strong\u003E. The object includes \u003Cstrong\u003E\u003Ccode\u003Ecluster_by_keys\u003C/code\u003E\u003C/strong\u003E, \u003Cstrong\u003E\u003Ccode\u003Eaverage_depth\u003C/code\u003E\u003C/strong\u003E and \u003Cstrong\u003E\u003Ccode\u003Eaverage_overlaps\u003C/code\u003E\u003C/strong\u003E (\u003Cstrong\u003Ehigher\u003C/strong\u003E values indicate the table is \u003Cstrong\u003Enot\u003C/strong\u003E well clustered for the given columns). These fields help relate micro-partition overlap to the pruning behavior behind the sub-second query times in earlier exercises.\u003C/p\u003E\n","\u003Ch3\u003EBonus D — Find the Ghost Sessions\u003C/h3\u003E\n","\u003Cp\u003EThe streamer occasionally inserts \u003Cstrong\u003Eperfect-game\u003C/strong\u003E rows from a single synthetic player; those rows carry an intentionally rare achievement (\u003Ccode\u003ESummit 2026\u003C/code\u003E). Aggregating by player therefore collapses to \u003Cstrong\u003Eone\u003C/strong\u003E row—the discovery is more interesting if you look at \u003Cstrong\u003Ewhich badges are rarest\u003C/strong\u003E, then list \u003Cstrong\u003Eeach ghost session\u003C/strong\u003E (many rows, same player across titles).\u003C/p\u003E\n","\u003Cp\u003EKeep a \u003Cstrong\u003E\u003Ccode\u003EGAME_ENDED_AT\u003C/code\u003E\u003C/strong\u003E window on these queries so the Interactive Warehouse stays within its timeout; widen the window if needed. Ghost rows are injected roughly \u003Cstrong\u003Eonce per 100,000\u003C/strong\u003E generated sessions—leave the Python streamer running if badge counts are still zero.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE WAREHOUSE SUMMIT_INT_WH;\nUSE DATABASE  ARCADE_DB;\nUSE SCHEMA    PUBLIC;\n\n-- D1 — Rarest achievements in the window (explore the tail before naming the badge)\nSELECT\n    ACHIEVEMENT,\n    COUNT(*) AS SESSIONS_WITH_BADGE\nFROM ARCADE_SCORES\nWHERE ACHIEVEMENT IS NOT NULL\n  AND GAME_ENDED_AT >= DATEADD('hour', -24,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY ACHIEVEMENT\nORDER BY SESSIONS_WITH_BADGE ASC, ACHIEVEMENT\nLIMIT 25;\n\n-- D2 — Every ghost session in the window (same player, different games / times)\nSELECT\n    GAME_ENDED_AT,\n    PLAYER_NAME,\n    PLAYER_ID,\n    PLAYER_CITY,\n    GAME_NAME,\n    SCORE,\n    LEVEL_REACHED,\n    ACCURACY_PCT,\n    GAME_MODE,\n    ACHIEVEMENT\nFROM ARCADE_SCORES\nWHERE ACHIEVEMENT = 'Summit 2026'\n  AND GAME_ENDED_AT >= DATEADD('hour', -24,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nORDER BY GAME_ENDED_AT DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe same statements are in \u003Cstrong\u003E\u003Ccode\u003Esql/03_lab_queries.sql\u003C/code\u003E\u003C/strong\u003E under Bonus D.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOptional: Streamlit Dashboard\u003C/h2\u003E\n","\u003Cp\u003EDeploy a live multi-page dashboard against \u003Ccode\u003EARCADE_SCORES\u003C/code\u003E using \u003Cstrong\u003ESnowflake Cortex CLI\u003C/strong\u003E — an AI agent that reads your project files, writes the Streamlit app, and deploys it to Snowflake in one shot.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe compute pool \u003Ccode\u003EARCADE_REPORTING_POOL\u003C/code\u003E (CPU_X64_XS) was provisioned by \u003Ccode\u003Esql/01_setup.sql\u003C/code\u003E and is ready to use. No additional Snowflake setup is required.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1 — Install the Snowflake CLI\u003C/h3\u003E\n","\u003Cp\u003EIn the Codespace terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epip install snowflake-cli\nsnow --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 — Install the Cortex CLI\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\ncortex --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 3 — Generate a PAT for the Cortex CLI\u003C/h3\u003E\n","\u003Cp\u003EThe Cortex CLI uses the \u003Cstrong\u003E\u003Ccode\u003Esnow\u003C/code\u003E CLI\u003C/strong\u003E under the hood to deploy the Streamlit app to Snowflake. \u003Ccode\u003Esnow\u003C/code\u003E requires a \u003Cstrong\u003EProgrammatic Access Token (PAT)\u003C/strong\u003E registered as a named connection — this is completely separate from the RSA key pair used by the Python streamer, which is not involved here at all.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen \u003Cstrong\u003E\u003Ccode\u003Esql/04_generate_pat.sql\u003C/code\u003E\u003C/strong\u003E in Snowsight and run it as the user who will deploy the dashboard\u003C/li\u003E\u003Cli\u003ECopy the \u003Ccode\u003ESETUP_COMMAND\u003C/code\u003E result that is printed, and run it in the Codespace terminal — it registers the \u003Ccode\u003Esnow\u003C/code\u003E connection in a single step\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EVerify the \u003Ccode\u003Esnow\u003C/code\u003E connection works:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Esnow connection test\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 4 — Run Cortex\u003C/h3\u003E\n","\u003Cp\u003EFrom the project root in the Codespace terminal, start Cortex with all tool calls enabled so it can read project files, write the Streamlit app, and deploy it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex --dangerously-allow-all-tool-calls\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 5 — Send the deployment prompt\u003C/h3\u003E\n","\u003Cp\u003EPaste the following prompt into the Cortex session:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUse skill `developing-with-streamlit` to create a real-time arcade scores dashboard with the following requirements:\n\n**Data Source:**\n- Table: `ARCADE_DB.PUBLIC.ARCADE_SCORES`\n- Warehouse: `SUMMIT_INT_WH` (Interactive Warehouse)\n- Connection: Use default connection with programmatic token, connection should be made with st.connection("snowflake")\n- Columns: All columns and tables are uppercase in Snowflake\n\n**Deployment:**\n- Deploy as Streamlit in Snowflake named `ARCADE_SCORES_DASHBOARD` in `ARCADE_DB.PUBLIC`\n- Use compute pool `ARCADE_REPORTING_POOL` (already provisioned)\n- Add external access integration and network rule for PyPI access\n- Add a pyproject.toml file to manage packages and add packages: "streamlit[snowflake]==1.50.0", "pandas>=2.0.0", "snowflake-snowpark-python"\n- Use SUMMIT_INT_WH by setting that in Streamlit application settings\n- Do not perform any "USE Warehouse" queries in python\n\n**Dashboard Features:**\n1. Global Leaderboard - Highest scores in last 24 hours\n2. Top Players by Game - Top 5 players per game in last hour\n3. Geographic Heatmap - Country-level plays over last hour\n4. Game Popularity - Session and player counts by game (last hour)\n5. Platform Breakdown - Session and player counts by platform (last hour)\n6. Recent Activity - Last 30 scores\n7. Achievements - All earned achievements (where ACHIEVEMENT IS NOT NULL) with counts\n8. Active Cities - Most active cities in last minute\n9. Pipeline Health - Operational metrics page with:\n   - Total games played (all time)\n   - Current data freshness in decimal seconds: DATEDIFF('millisecond', MAX(GAME_ENDED_AT), CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ) / 1000.0\n   - Freshness over last hour as a time-series line chart bucketed by DATE_TRUNC('minute', GAME_ENDED_AT)\n   - Rows/sec over last hour: count per minute bucket divided by 60, as a time-series line chart\n\n**Additional Guidance:**\n- Validate all data types, units and calculations, in both SQL and Python layers\n- Ensure that all data presented is intuitive and informative\n- Ensure that the layout and design of the dashboard is sleek and appealing\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECortex will write the Streamlit app file and deploy it to Snowflake. Once complete, open the dashboard from \u003Cstrong\u003ESnowsight → Streamlit → ARCADE_SCORES_DASHBOARD\u003C/strong\u003E.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe dashboard queries \u003Ccode\u003EARCADE_SCORES\u003C/code\u003E through \u003Ccode\u003ESUMMIT_INT_WH\u003C/code\u003E — the same Interactive Warehouse used in the lab exercises. All nine pages will refresh with live data as long as the Python streamer is running.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EWhen you are done with the lab, run the teardown script to remove all Snowflake objects and stop billing.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EOpen \u003Cstrong\u003E\u003Ccode\u003Esql/05_cleanup.sql\u003C/code\u003E\u003C/strong\u003E in Snowsight\u003C/li\u003E\u003Cli\u003ERun the entire script as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis drops (in order): the Streamlit dashboard, compute pool, both warehouses, the \u003Ccode\u003EARCADE_DB\u003C/code\u003E database (which cascades to the Interactive Table and all pipes), the service user, and both roles.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe Interactive Warehouse (\u003Ccode\u003ESUMMIT_INT_WH\u003C/code\u003E) bills a \u003Cstrong\u003Eminimum of 1 hour\u003C/strong\u003E from the time it is created, then per-second thereafter. Run cleanup as soon as you are finished with the lab to avoid unnecessary charges.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYou can also stop the Python streamer at any time with \u003Cstrong\u003ECtrl-C\u003C/strong\u003E in the Codespace terminal.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EIn this lab you built a fully operational real-time data pipeline on Snowflake:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStreamed thousands of rows per second directly into an \u003Cstrong\u003EInteractive Table\u003C/strong\u003E using the \u003Cstrong\u003ESnowpipe Streaming Python SDK\u003C/strong\u003E — no files, no \u003Ccode\u003ECOPY INTO\u003C/code\u003E, no batch windows\u003C/li\u003E\u003Cli\u003EQueried live data with \u003Cstrong\u003Esub-second latency\u003C/strong\u003E using an \u003Cstrong\u003EInteractive Warehouse\u003C/strong\u003E backed by pre-computed indexes and a warm SSD cache\u003C/li\u003E\u003Cli\u003EExperienced how \u003Ccode\u003ECLUSTER BY (GAME_ENDED_AT)\u003C/code\u003E eliminates micro-partition scans on time-range predicates — the key to Interactive Warehouse performance at scale\u003C/li\u003E\u003Cli\u003EMeasured end-to-end pipeline freshness: from Python generator to queryable Snowflake row in under 2 seconds\u003C/li\u003E\u003Cli\u003ECompared the Interactive and Standard warehouse head-to-head under the same query workload\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow Snowpipe Streaming's \u003Cstrong\u003Echannel API\u003C/strong\u003E enables direct, low-latency row-level ingestion without staging\u003C/li\u003E\u003Cli\u003EThe architectural difference between \u003Cstrong\u003EInteractive Tables\u003C/strong\u003E and standard Snowflake tables\u003C/li\u003E\u003Cli\u003EHow an \u003Cstrong\u003EInteractive Warehouse\u003C/strong\u003E uses persistent SSD caching and pre-computed indexes to deliver sub-second queries\u003C/li\u003E\u003Cli\u003EWhy \u003Ccode\u003ECLUSTER BY\u003C/code\u003E on a time column is essential for time-series workloads on Interactive Tables\u003C/li\u003E\u003Cli\u003EHow to measure \u003Cstrong\u003Edata freshness\u003C/strong\u003E and \u003Cstrong\u003ESDK processing latency\u003C/strong\u003E from within Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/Summit26-InteractiveLab\"\u003ELab GitHub Repository\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming-sdk-python/reference/latest/api/snowflake/ingest/streaming/index\"\u003ESnowpipe Streaming Python SDK Reference\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/interactive\"\u003EInteractive Tables Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/warehouses-interactive\"\u003EInteractive Warehouses Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quickstarts.snowflake.com\"\u003ESnowflake Quickstarts\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Real-Time Streaming with Snowpipe Streaming and Interactive Tables",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nIn this lab you will stream thousands of synthetic arcade game scores from a Python process directly into Snowflake using the **Snowpipe Streaming Python SDK**, store them in an **Interactive Table**, and experience true sub-second query latency at scale using an **Interactive Warehouse**.\n\nEverything runs inside a **GitHub Codespace** — no local installs required beyond a browser and a Snowflake account.\n\n![Architecture diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/interactive-tables-snowpipe-streaming-arcade-lab/architecture.png?v=6101be7f)\n\n```\n Python Generator\n ─────────────────\n  Arcade Score Events          Snowpipe Streaming SDK\n  (unlimited rows/sec) ──────► StreamingIngestClient\n  20 games · 45 cities          └─ Channel 0      ──► ARCADE_SCORES\n  500 players                                          (Interactive Table)\n                                                       CLUSTER BY (GAME_ENDED_AT)\n                                                            │\n                                                            ▼\n                                                     SUMMIT_INT_WH\n                                                  (Interactive Warehouse, XS)\n                                                  Always-on · sub-second latency queries\n```\n\nSnowpipe Streaming uses the **channel API** — rows are written directly into the Interactive Table without any intermediate landing table, staging area, or `COPY INTO` command.\n\n### Prerequisites\n\n- A [Snowflake account](https://signup.snowflake.com/) in a [supported Interactive Tables region](https://docs.snowflake.com/en/user-guide/interactive#region-availability) with `ACCOUNTADMIN` access\n\n### What You'll Learn\n\n- How to stream rows into Snowflake using the **Snowpipe Streaming Python SDK** channel API\n- How **Interactive Tables** differ from standard Snowflake tables and why they enable sub-second queries\n- How an **Interactive Warehouse** uses pre-computed indexes and a warm local SSD cache to answer queries quickly\n- How `CLUSTER BY` on a timestamp column eliminates irrelevant micro-partition scans on time-range queries\n- How to measure real-time data **freshness** from within Snowsight\n- How the Interactive Warehouse handles **50 concurrent users** without query queuing\n\n### What You'll Need\n\n- A [GitHub account](https://github.com/) (free tier is sufficient for Codespaces)\n- A web browser — no local installs required; Python, the Snowpipe Streaming SDK, and all other dependencies are installed automatically in the Codespace\n\n### What You'll Build\n\n- A live arcade score streaming pipeline (Python → Snowpipe Streaming → Interactive Table)\n- 11 analytical exercises covering freshness, leaderboards, geo heat maps, and concurrency benchmarking\n- A side-by-side speed comparison between the Interactive Warehouse and a standard warehouse\n\n\u003C!-- ------------------------ --\u003E\n## Open the Lab in a GitHub Codespace\n\nAll lab code lives in a public GitHub repository. GitHub Codespaces gives you a fully configured cloud development environment — Python, the Snowpipe Streaming SDK, and all dependencies are installed automatically.\n\n### Step 1 — Open the Codespace\n\n1. Navigate to [https://github.com/Snowflake-Labs/Summit26-InteractiveLab](https://github.com/Snowflake-Labs/Summit26-InteractiveLab)\n2. Click the green **Code** button → **Codespaces** tab → **Create codespace on main**\n\nGitHub will build the container and open VS Code in your browser. This takes about 1–2 minutes on first launch.\n\n\u003E The Codespace automatically installs the `snowpipe-streaming` Python package and all other dependencies from `requirements.txt` — no manual `pip install` needed.\n\n### Step 2 — Explore the repository structure\n\nOnce the Codespace is open, you will see:\n\n```\nSummit26-InteractiveLab/\n├── sql/\n│   ├── 01_setup.sql          Snowflake object provisioning\n│   ├── 02_service_auth.sh    RSA key generation + ALTER USER SQL\n│   ├── 03_lab_queries.sql    All 11 exercises + bonus queries\n│   ├── 04_generate_pat.sql   Optional: PAT for Cortex CLI dashboard deployment\n│   └── 05_cleanup.sql        Teardown script\n├── python/\n│   ├── arcade_streamer.py    Snowpipe Streaming SDK ingest\n│   ├── generator.py          Synthetic arcade data generator\n│   └── config.py             All tuneable parameters\n├── jmeter/                   Concurrency test plan (optional)\n├── profile.json.example      Snowflake connection template\n└── requirements.txt\n```\n\n\u003C!-- ------------------------ --\u003E\n## Provision Snowflake Objects\n\nAll Snowflake objects are created by a single SQL script. Run it in Snowsight using a **standard warehouse** session.\n\n### What gets provisioned\n\n| Object | Type | Purpose |\n|---|---|---|\n| `ARCADE_STREAMING_ROLE` | Role | Minimum-privilege role for the streaming user |\n| `ARCADE_STREAMING_USER` | User | Service account used by the Python SDK |\n| `ARCADE_KEYPAIR_POLICY` | Auth Policy | Restricts the service user to key-pair auth only |\n| `ARCADE_DB` | Database | Holds all lab objects |\n| `SUMMIT_TRAD_WH` | Standard Warehouse XS | Setup tasks; comparison benchmarks |\n| `ARCADE_SCORES` | **Interactive Table** | Direct streaming target, `CLUSTER BY (GAME_ENDED_AT)` |\n| `SUMMIT_INT_WH` | **Interactive Warehouse XS** | All lab queries — always-on, sub-second latency |\n| `ARCADE_REPORTING_POOL` | Compute Pool | For the optional Streamlit dashboard |\n| `ARCADE_LAB_READER` | Role | Read-only role for lab attendees |\n\n### Step 1 — Open Snowsight\n\nLog in to your Snowflake account at [app.snowflake.com](https://app.snowflake.com).\n\n### Step 2 — Run the setup script\n\n1. In the Codespace, open **`sql/01_setup.sql`**\n2. Copy the entire file contents\n3. In Snowsight, open a new SQL worksheet, paste, and click **Run All**\n\n\u003E **Important:** Run the script with a **Standard** warehouse session (e.g. `SUMMIT_TRAD_WH` once created, or any existing standard warehouse). `CREATE INTERACTIVE TABLE` is not supported from an Interactive Warehouse session.\n\nWhen the script completes you will see:\n\n```\nSetup complete – start the Python streamer, then wait ~2 min for cache warm-up.\n```\n\n### What makes an Interactive Table different?\n\nInteractive Tables are optimised for low-latency streaming ingestion and sub-second lookups. Key properties:\n\n- **Direct streaming writes** — Snowpipe Streaming SDK writes rows without staging files\n- **Always-queryable** — rows are visible immediately after commit, no `REFRESH` needed\n- **Pre-computed indexes** — the Interactive Warehouse maintains in-memory index metadata\n- **Time Travel** — fully supported even with continuous streaming writes\n- **`CLUSTER BY`** — aligns micro-partitions with query predicates; the warehouse skips irrelevant partitions automatically\n\n\u003C!-- ------------------------ --\u003E\n## Register the RSA Key Pair\n\nThe Python streamer authenticates as `ARCADE_STREAMING_USER` using RSA key-pair authentication. A shell script generates the key pair and prints the exact SQL needed to register the public key.\n\n### Step 1 — Run the keygen script\n\nIn the Codespace terminal:\n\n```bash\nbash sql/02_service_auth.sh\n```\n\nExecute the SQL which is output from the script in your Snowflake account.\n\nThis script:\n1. Generates `rsa_key.p8` (private key) and `rsa_key.pub` (public key) if they don't already exist\n2. Creates a network policy to allow the codespace to communicate with the service account\n - Sets public key for the service account\n3. Prints a ready-to-use `profile.json` block with the absolute path to `rsa_key.p8`\n\nSave the json from the output of this script for the next step\n\n\u003C!-- ------------------------ --\u003E\n## Configure the Connection Profile\n\nThe streamer reads connection details from `profile.json`.\n\n### Step 1 — Create profile.json\n\n```bash\ncp profile.json.example profile.json\n```\n\nOpen the profile.json in the editor and paste in the json configuration generated by the sql script in the previous step.\n\n\u003C!-- ------------------------ --\u003E\n## How the Snowpipe Streaming SDK Works\n\nBefore running the streamer, let's walk through the key parts of `python/arcade_streamer.py` to understand exactly how rows get from Python into Snowflake — no files, no `COPY INTO`, no batch window.\n\n### 1 — Create the client\n\n`StreamingIngestClient` is the top-level SDK object. One client maps to exactly one account / database / schema / pipe combination. It handles authentication, channel lifecycle, and the underlying HTTPS transport to Snowflake's ingest endpoint.\n\n```python\n# arcade_streamer.py  (main)\nfrom snowflake.ingest.streaming import StreamingIngestClient\n\nwith StreamingIngestClient(\n    client_name  = f\"ARCADE_CLIENT_{uuid.uuid4().hex[:8].upper()}\",\n    db_name      = config.SNOWFLAKE_DATABASE,   # \"ARCADE_DB\"\n    schema_name  = config.SNOWFLAKE_SCHEMA,     # \"PUBLIC\"\n    pipe_name    = config.SNOWFLAKE_PIPE,        # \"ARCADE_SCORES-STREAMING\"\n    profile_json = args.profile,                 # path to profile.json\n) as client:\n    ...\n```\n\n`profile_json` points at the `profile.json` you just created — the client reads the RSA private key path from there and signs its own JWT tokens for every request.\n\n### 2 — Open a channel\n\nA **channel** is the logical write path within a client. Each channel maintains its own internal write buffer and commit position. Opening a channel is idempotent — if a channel with the same name already exists (e.g. after a restart), the SDK resumes from the last committed offset token.\n\n```python\n# arcade_streamer.py  (channel_worker)\nchannel_name = f\"ARCADE_CHANNEL_{channel_id}_{uuid.uuid4().hex[:8].upper()}\"\n\nwith client.open_channel(channel_name)[0] as channel:\n    print(f\"  [channel-{channel_id}] opened: {channel.channel_name}\")\n    ...\n```\n\n`open_channel()` returns `(StreamingIngestChannel, ChannelStatus)` — the `[0]` unwraps the channel. The context manager automatically closes and flushes the channel when the `with` block exits.\n\n### 3 — Append rows\n\n`append_row()` is the core ingestion call. It accepts a plain Python `dict` where **keys are column names** (case-insensitive) and **values are native Python types**. No serialisation to JSON or Parquet needed — the SDK handles that internally.\n\n```python\n# arcade_streamer.py  (channel_worker inner loop)\nbatch = generate_batch(rows_per_batch)   # list of row dicts from generator.py\n\nfor row in batch:\n    channel.append_row(row, str(offset_token))\n    offset_token += 1\n```\n\nEach call to `append_row` is non-blocking. The SDK buffers rows in memory and flushes them to Snowflake in the background, automatically respecting Snowflake's micro-batch commit cadence.\n\n### 4 — What a row looks like\n\n`generator.py` produces rows as plain Python dicts. The dict keys map directly to `ARCADE_SCORES` column names:\n\n```python\n# generator.py  (generate_score)\nreturn {\n    \"score_id\":         str(uuid.uuid4()),   # VARCHAR(36)\n    \"player_id\":        player[\"player_id\"], # VARCHAR(36)\n    \"player_name\":      player[\"player_name\"],\n    \"player_country\":   player[\"player_country\"],\n    \"player_city\":      player[\"player_city\"],\n    \"latitude\":         player[\"latitude\"],  # FLOAT\n    \"longitude\":        player[\"longitude\"],\n    \"game_name\":        game_name,\n    \"game_mode\":        game_mode,\n    \"platform\":         platform,\n    \"score\":            score,               # NUMBER(12,0)\n    \"level_reached\":    level,\n    \"duration_seconds\": duration_sec,\n    \"lives_remaining\":  lives,\n    \"accuracy_pct\":     accuracy,            # nullable FLOAT\n    \"achievement\":      achievement,         # nullable VARCHAR(64)\n    \"game_ended_at\":    game_ended_at,       # TIMESTAMP_NTZ (UTC datetime)\n}\n```\n\nThe SDK maps Python `int` → `NUMBER`, `float` → `FLOAT`, `str` → `VARCHAR`, `datetime` → `TIMESTAMP_NTZ`, and `None` → `NULL` automatically.\n\n### 5 — Offset tokens\n\nEvery `append_row` call takes an **offset token** — a monotonically increasing string the SDK uses to track exactly which rows have been durably committed on the Snowflake side. If the streamer crashes and restarts, it can reopen the same channel and resume from `channel.get_latest_committed_offset_token()`, avoiding duplicate inserts.\n\n```python\n# offset_token increments with every row:\nchannel.append_row(row, str(offset_token))\noffset_token += 1\n```\n\n### 6 — Latency monitor\n\nA background thread calls `client.get_channel_statuses()` periodically to retrieve Snowflake's reported average processing latency — the time between `append_row` being called and the row being durably committed and queryable in the Interactive Table.\n\n```python\n# arcade_streamer.py  (latency_monitor)\nstatuses = client.get_channel_statuses(names)\nfor name, st in statuses.items():\n    lag = st.server_avg_processing_latency   # datetime.timedelta | None\n    if lag is not None:\n        print(f\"[latency] {name}: {lag.total_seconds() * 1000:.0f} ms avg\")\n```\n\nThis is the `[latency]` line you will see in the console once the streamer is running.\n\n\u003E **Preview the data without connecting to Snowflake**\n\u003E\n\u003E Before starting the real streamer you can preview a few generated rows to confirm the generator is working:\n\u003E ```bash\n\u003E cd python\n\u003E python arcade_streamer.py --dry-run --rows 3\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Start the Arcade Streamer\n\nWith the connection configured, start the Python streamer. It will immediately begin writing arcade score events to Snowflake at full speed.\n\n```bash\ncd python\npython arcade_streamer.py\n```\n\nYou should see output like:\n\n```\n============================================================\n Summit 2026 – Arcade Scores Snowpipe Streamer\n============================================================\n  Account   : YOUR_ORG-YOUR_ACCOUNT\n  Database  : ARCADE_DB.PUBLIC\n  Pipe      : ARCADE_SCORES-STREAMING\n  Channels  : 1\n  Target    : unlimited rows/sec\n============================================================\n\n  [14:22:05]  rows:      512  |  512.0 rows/sec  |  errors: 0  |  elapsed:    1s\n  [14:22:05]  [latency] ARCADE_CHANNEL_0_A3F2B1C4: 540 ms avg\n  [14:22:10]  rows:    1,024  |  512.0 rows/sec  |  errors: 0  |  elapsed:    6s\n  [14:22:10]  [latency] ARCADE_CHANNEL_0_A3F2B1C4: 512 ms avg\n```\n\nThe `[latency]` line is the **Snowflake-reported average processing latency** polled from the SDK's `get_channel_statuses()` API — typically under a second.\n\n### Wait for cache warm-up\n\n\u003E After the streamer starts, wait **2–3 minutes** before running lab queries. The `SUMMIT_INT_WH` Interactive Warehouse needs this time to populate its local SSD cache. Queries run before warm-up will still succeed but will be slower.\n\n\u003C!-- ------------------------ --\u003E\n## Exercise 1 — Pipeline Throughput\n\nOpen **`sql/03_lab_queries.sql`** in Snowsight and run the Exercise 1 queries while the streamer is running. Make sure `SUMMIT_INT_WH` is selected as the active warehouse.\n\n### 1a — Total row count\n\n```sql\nSELECT COUNT(*) AS TOTAL_SCORES FROM ARCADE_SCORES;\n```\n\nRe-run this every few seconds. You should see the count climbing as new rows stream in.\n\n### 1b — Rows per second (last 60 seconds)\n\n```sql\nSELECT\n    COUNT(*)                   AS ROWS_LAST_60_SEC,\n    ROUND(COUNT(*) / 60.0, 1) AS ROWS_PER_SECOND,\n    COUNT(DISTINCT PLAYER_ID)  AS ACTIVE_PLAYERS\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('second', -60,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ);\n```\n\n\u003E **`CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ`** normalises the session-local `CURRENT_TIMESTAMP()` to UTC so it compares correctly against the UTC `TIMESTAMP_NTZ` values in `GAME_ENDED_AT`. You'll see this pattern in every time-filtered query throughout the lab.\n\n### 1c — Throughput by 10-second bucket (last 3 minutes)\n\n```sql\nSELECT\n    DATEADD('second',\n        FLOOR(DATEDIFF('second', '2000-01-01'::TIMESTAMP_NTZ, GAME_ENDED_AT) / 10) * 10,\n        '2000-01-01'::TIMESTAMP_NTZ)   AS TIME_BUCKET,\n    COUNT(*)                           AS SCORES\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('minute', -3,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY TIME_BUCKET\nORDER BY TIME_BUCKET DESC\nLIMIT 18;\n```\n\nThis histogram shows you the exact shape of the ingest stream — each bucket represents 10 seconds of data arriving from the Python generator. Notice how rows are already in the Interactive Table by the time the query runs: there is no batch window, no `COPY INTO`, and no manual refresh.\n\n\u003C!-- ------------------------ --\u003E\n## Exercise 2 — Data Freshness\n\nHow stale is the most recent row in the table right now?\n\n```sql\nSELECT\n    MAX(GAME_ENDED_AT)                                                    AS LATEST_GAME_ENDED,\n    DATEDIFF(\n        'millisecond',\n        MAX(GAME_ENDED_AT),\n        CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ\n    ) / 1000.0                                                            AS FRESHNESS_SEC\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('minute', -5,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ);\n```\n\n`GAME_ENDED_AT` is the UTC timestamp when the Python generator emitted the row. `FRESHNESS_SEC` is how many seconds ago that was — the end-to-end pipeline latency from the Python process to a queryable row in Snowflake.\n\n\u003E This query uses the same `CONVERT_TIMEZONE('UTC', ...)` pattern introduced in Exercise 1.\n\nWith a healthy streamer you should see freshness values in the range of **0.3 – 2 seconds**, depending on Snowflake region and network latency.\n\nThe streamer console also logs the SDK-reported average processing latency per channel (the `[latency]` lines) — compare that to the SQL freshness value.\n\n\u003C!-- ------------------------ --\u003E\n## Exercise 3 — Global Leaderboard\n\nWho has the highest scores across all games in the last 24 hours?\n\n```sql\nSELECT\n    RANK() OVER (ORDER BY SCORE DESC)  AS RANK,\n    PLAYER_NAME,\n    PLAYER_COUNTRY,\n    PLAYER_CITY,\n    GAME_NAME,\n    TO_CHAR(SCORE, '999,999,999')      AS SCORE,\n    LEVEL_REACHED,\n    PLATFORM,\n    COALESCE(ACHIEVEMENT, '—')         AS ACHIEVEMENT,\n    GAME_ENDED_AT\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -24,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nORDER BY SCORE DESC\nLIMIT 20;\n```\n\nNotice the query execution time in Snowsight's query history — it should be **sub-second** even with hundreds of thousands of rows in the table. \n\nThis is the `CLUSTER BY (GAME_ENDED_AT)` effect: the Interactive Warehouse knows exactly which micro-partitions contain rows from the last 24 hours and skips everything else. No full table scan occurs.\n\n\u003E **Try it without the `WHERE` clause.** Remove the `GAME_ENDED_AT` filter and re-run. Snowsight will show the query hitting the 5-second Interactive Warehouse timeout — which is the expected, by-design behaviour that enforces efficient query patterns.\n\n\u003C!-- ------------------------ --\u003E\n## Exercise 4 — Per-Game Top 5\n\nUse `QUALIFY ROW_NUMBER()` to rank the top 5 players per game in the last hour without a subquery:\n\n```sql\nSELECT\n    GAME_NAME,\n    ROW_NUMBER() OVER (\n        PARTITION BY GAME_NAME ORDER BY SCORE DESC\n    )                                  AS POSITION,\n    PLAYER_NAME,\n    PLAYER_COUNTRY,\n    TO_CHAR(SCORE, '999,999,999')      AS SCORE,\n    LEVEL_REACHED,\n    GAME_MODE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nQUALIFY ROW_NUMBER() OVER (\n    PARTITION BY GAME_NAME ORDER BY SCORE DESC\n) \u003C= 5\nORDER BY GAME_NAME, POSITION;\n```\n\n`QUALIFY` is Snowflake's inline filter for window functions — no outer `SELECT` wrapper needed. The Interactive Warehouse handles 20 game partitions × 5 positions in a single pass.\n\n\u003C!-- ------------------------ --\u003E\n## Exercises 5–9 — Deeper Analysis\n\nThese exercises explore the data from different angles. Open **`sql/03_lab_queries.sql`** and run the queries for each exercise.\n\n### Exercise 5 — Country Heat Map\n\nWhich countries are generating the most play sessions right now?\n\n```sql\nSELECT\n    PLAYER_COUNTRY,\n    COUNT(*)                   AS GAMES_PLAYED,\n    ROUND(AVG(SCORE))          AS AVG_SCORE,\n    MAX(SCORE)                 AS HIGH_SCORE,\n    COUNT(DISTINCT PLAYER_ID)  AS UNIQUE_PLAYERS\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_COUNTRY\nORDER BY GAMES_PLAYED DESC\nLIMIT 20;\n```\n\nJapan and South Korea lead — the data generator weights cities by real-world gaming culture.\n\n### Exercise 6 — Game Popularity\n\nWhich games have the most sessions? How do average scores compare across titles?\n\n```sql\nSELECT\n    GAME_NAME,\n    COUNT(*)                                        AS SESSIONS,\n    COUNT(DISTINCT PLAYER_ID)                       AS UNIQUE_PLAYERS,\n    TO_CHAR(ROUND(AVG(SCORE)), '999,999,999')       AS AVG_SCORE,\n    TO_CHAR(MAX(SCORE),        '999,999,999')       AS HIGH_SCORE,\n    ROUND(AVG(LEVEL_REACHED), 1)                    AS AVG_LEVEL,\n    ROUND(AVG(DURATION_SECONDS) / 60.0, 1)          AS AVG_GAME_MIN\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY GAME_NAME\nORDER BY SESSIONS DESC;\n```\n\nPac-Man and Tetris should lead with roughly 5× more sessions than rarer titles like Joust and Tron.\n\n### Exercise 7 — Platform Breakdown\n\n```sql\nSELECT\n    PLATFORM,\n    COUNT(*)                   AS SESSIONS,\n    ROUND(AVG(SCORE))          AS AVG_SCORE,\n    ROUND(AVG(ACCURACY_PCT), 1) AS AVG_ACCURACY_PCT,\n    COUNT(DISTINCT PLAYER_ID)  AS UNIQUE_PLAYERS\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLATFORM\nORDER BY SESSIONS DESC;\n```\n\nClassic arcade cabinets dominate for titles like Pac-Man; Tetris skews mobile.\n\n### Exercise 8 — Live Score Feed (re-run repeatedly!)\n\n```sql\nSELECT\n    PLAYER_NAME,\n    PLAYER_CITY,\n    PLAYER_COUNTRY,\n    GAME_NAME,\n    TO_CHAR(SCORE, '999,999,999')  AS SCORE,\n    LEVEL_REACHED,\n    PLATFORM,\n    COALESCE(ACHIEVEMENT, '—')     AS ACHIEVEMENT,\n    GAME_ENDED_AT\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('minute', -5,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nORDER BY GAME_ENDED_AT DESC\nLIMIT 30;\n```\n\nRe-run every few seconds. You'll see new rows appearing at the top — these are rows that streamed in after your last query execution. No refresh, no polling interval: the table is live.\n\n### Exercise 9 — Achievement Rarity\n\n```sql\nSELECT\n    ACHIEVEMENT,\n    COUNT(*)                   AS TIMES_EARNED,\n    COUNT(DISTINCT PLAYER_ID)  AS UNIQUE_EARNERS,\n    ROUND(AVG(SCORE))          AS AVG_SCORE_WHEN_EARNED,\n    ROUND(AVG(LEVEL_REACHED), 1) AS AVG_LEVEL\nFROM ARCADE_SCORES\nWHERE ACHIEVEMENT IS NOT NULL\n  AND GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY ACHIEVEMENT\nORDER BY TIMES_EARNED ASC;\n```\n\nAchievements like \"Pacifist\" and \"Triple Threat\" require specific skill tier + game mode + score conditions and appear in fewer than 1 in 200 rows.\n\n\u003C!-- ------------------------ --\u003E\n## Exercise 10 — Interactive vs Traditional Warehouse\n\nRun the same aggregation query on both warehouses back-to-back and compare execution times in Query History.\n\n### Step A — Interactive Warehouse\n\n```sql\nUSE WAREHOUSE SUMMIT_INT_WH;\n\nSELECT\n    PLAYER_COUNTRY,\n    COUNT(*)    AS SESSIONS,\n    MAX(SCORE)  AS HIGH_SCORE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_COUNTRY\nORDER BY SESSIONS DESC;\n```\n\n### Step B — Standard Warehouse (identical query)\n\n```sql\nUSE WAREHOUSE SUMMIT_TRAD_WH;\n\nSELECT\n    PLAYER_COUNTRY,\n    COUNT(*)    AS SESSIONS,\n    MAX(SCORE)  AS HIGH_SCORE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('hour', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_COUNTRY\nORDER BY SESSIONS DESC;\n```\n\n### Compare in Query History\n\nOpen **Activity → Query History** in Snowsight and compare the two query durations side by side.\n\n| Warehouse | Typical latency | Why |\n|---|---|---|\n| `SUMMIT_INT_WH` (Interactive) | sub-second | Pre-computed indexes + local SSD cache; skips most micro-partitions via clustering |\n| `SUMMIT_TRAD_WH` (Standard) | several seconds | Micro-partition scan on first run |\n\nThe Interactive Warehouse achieves significantly higher throughput and lower latency at high concurrency because it uses a dedicated shared SSD cache and pre-computed index metadata that survives between queries.\n\n\u003E **Switch back to the Interactive Warehouse** before continuing:\n\u003E ```sql\n\u003E USE WAREHOUSE SUMMIT_INT_WH;\n\u003E ```\n\n\u003C!-- ------------------------ --\u003E\n## Bonus Exercises\n\n### Bonus A — Time Travel on an Interactive Table\n\nInteractive Tables support Time Travel even with continuous streaming writes.\n\n```sql\n-- Row count 5 minutes ago\nSELECT COUNT(*) AS ROWS_5_MIN_AGO\nFROM ARCADE_SCORES AT(OFFSET =\u003E -300);\n\n-- How many rows were generated in the last 5 minutes?\nSELECT COUNT(*) AS NEW_ROWS_LAST_5_MIN\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('minute', -5,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ);\n```\n\n### Bonus B — Rolling 1-Minute City Hotspot\n\nWhich cities are most active right now?\n\n```sql\nSELECT\n    PLAYER_CITY,\n    PLAYER_COUNTRY,\n    COUNT(*) AS GAMES_LAST_MINUTE\nFROM ARCADE_SCORES\nWHERE GAME_ENDED_AT \u003E= DATEADD('minute', -1,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY PLAYER_CITY, PLAYER_COUNTRY\nORDER BY GAMES_LAST_MINUTE DESC\nLIMIT 15;\n```\n\n### Bonus C — Inspect Interactive Table Metadata\n\n```sql\nUSE WAREHOUSE SUMMIT_TRAD_WH;\nUSE ROLE ACCOUNTADMIN;\n\nSHOW INTERACTIVE TABLES IN SCHEMA ARCADE_DB.PUBLIC;\n\n-- JSON clustering stats for GAME_ENDED_AT (overlap depth, overlaps, partition counts, histogram)\nSELECT SYSTEM$CLUSTERING_INFORMATION('ARCADE_DB.PUBLIC.ARCADE_SCORES', '(GAME_ENDED_AT)');\n```\n\n[`SYSTEM$CLUSTERING_INFORMATION`](https://docs.snowflake.com/en/sql-reference/functions/system_clustering_information) returns a **JSON string**. The object includes **`cluster_by_keys`**, **`average_depth`** and **`average_overlaps`** (**higher** values indicate the table is **not** well clustered for the given columns). These fields help relate micro-partition overlap to the pruning behavior behind the sub-second query times in earlier exercises.\n\n### Bonus D — Find the Ghost Sessions\n\nThe streamer occasionally inserts **perfect-game** rows from a single synthetic player; those rows carry an intentionally rare achievement (`Summit 2026`). Aggregating by player therefore collapses to **one** row—the discovery is more interesting if you look at **which badges are rarest**, then list **each ghost session** (many rows, same player across titles).\n\nKeep a **`GAME_ENDED_AT`** window on these queries so the Interactive Warehouse stays within its timeout; widen the window if needed. Ghost rows are injected roughly **once per 100,000** generated sessions—leave the Python streamer running if badge counts are still zero.\n\n```sql\nUSE WAREHOUSE SUMMIT_INT_WH;\nUSE DATABASE  ARCADE_DB;\nUSE SCHEMA    PUBLIC;\n\n-- D1 — Rarest achievements in the window (explore the tail before naming the badge)\nSELECT\n    ACHIEVEMENT,\n    COUNT(*) AS SESSIONS_WITH_BADGE\nFROM ARCADE_SCORES\nWHERE ACHIEVEMENT IS NOT NULL\n  AND GAME_ENDED_AT \u003E= DATEADD('hour', -24,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nGROUP BY ACHIEVEMENT\nORDER BY SESSIONS_WITH_BADGE ASC, ACHIEVEMENT\nLIMIT 25;\n\n-- D2 — Every ghost session in the window (same player, different games / times)\nSELECT\n    GAME_ENDED_AT,\n    PLAYER_NAME,\n    PLAYER_ID,\n    PLAYER_CITY,\n    GAME_NAME,\n    SCORE,\n    LEVEL_REACHED,\n    ACCURACY_PCT,\n    GAME_MODE,\n    ACHIEVEMENT\nFROM ARCADE_SCORES\nWHERE ACHIEVEMENT = 'Summit 2026'\n  AND GAME_ENDED_AT \u003E= DATEADD('hour', -24,\n          CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ)\nORDER BY GAME_ENDED_AT DESC;\n```\n\nThe same statements are in **`sql/03_lab_queries.sql`** under Bonus D.\n\n\u003C!-- ------------------------ --\u003E\n## Optional: Streamlit Dashboard\n\nDeploy a live multi-page dashboard against `ARCADE_SCORES` using **Snowflake Cortex CLI** — an AI agent that reads your project files, writes the Streamlit app, and deploys it to Snowflake in one shot.\n\n\u003E The compute pool `ARCADE_REPORTING_POOL` (CPU_X64_XS) was provisioned by `sql/01_setup.sql` and is ready to use. No additional Snowflake setup is required.\n\n### Step 1 — Install the Snowflake CLI\n\nIn the Codespace terminal:\n\n```bash\npip install snowflake-cli\nsnow --version\n```\n\n### Step 2 — Install the Cortex CLI\n\n```bash\ncurl -LsS https://ai.snowflake.com/static/cc-scripts/install.sh | sh\ncortex --version\n```\n\n### Step 3 — Generate a PAT for the Cortex CLI\n\nThe Cortex CLI uses the **`snow` CLI** under the hood to deploy the Streamlit app to Snowflake. `snow` requires a **Programmatic Access Token (PAT)** registered as a named connection — this is completely separate from the RSA key pair used by the Python streamer, which is not involved here at all.\n\n1. Open **`sql/04_generate_pat.sql`** in Snowsight and run it as the user who will deploy the dashboard\n2. Copy the `SETUP_COMMAND` result that is printed, and run it in the Codespace terminal — it registers the `snow` connection in a single step\n\nVerify the `snow` connection works:\n\n```bash\nsnow connection test\n```\n\n### Step 4 — Run Cortex\n\nFrom the project root in the Codespace terminal, start Cortex with all tool calls enabled so it can read project files, write the Streamlit app, and deploy it:\n\n```bash\ncortex --dangerously-allow-all-tool-calls\n```\n\n### Step 5 — Send the deployment prompt\n\nPaste the following prompt into the Cortex session:\n\n```\nUse skill `developing-with-streamlit` to create a real-time arcade scores dashboard with the following requirements:\n\n**Data Source:**\n- Table: `ARCADE_DB.PUBLIC.ARCADE_SCORES`\n- Warehouse: `SUMMIT_INT_WH` (Interactive Warehouse)\n- Connection: Use default connection with programmatic token, connection should be made with st.connection(\"snowflake\")\n- Columns: All columns and tables are uppercase in Snowflake\n\n**Deployment:**\n- Deploy as Streamlit in Snowflake named `ARCADE_SCORES_DASHBOARD` in `ARCADE_DB.PUBLIC`\n- Use compute pool `ARCADE_REPORTING_POOL` (already provisioned)\n- Add external access integration and network rule for PyPI access\n- Add a pyproject.toml file to manage packages and add packages: \"streamlit[snowflake]==1.50.0\", \"pandas\u003E=2.0.0\", \"snowflake-snowpark-python\"\n- Use SUMMIT_INT_WH by setting that in Streamlit application settings\n- Do not perform any \"USE Warehouse\" queries in python\n\n**Dashboard Features:**\n1. Global Leaderboard - Highest scores in last 24 hours\n2. Top Players by Game - Top 5 players per game in last hour\n3. Geographic Heatmap - Country-level plays over last hour\n4. Game Popularity - Session and player counts by game (last hour)\n5. Platform Breakdown - Session and player counts by platform (last hour)\n6. Recent Activity - Last 30 scores\n7. Achievements - All earned achievements (where ACHIEVEMENT IS NOT NULL) with counts\n8. Active Cities - Most active cities in last minute\n9. Pipeline Health - Operational metrics page with:\n   - Total games played (all time)\n   - Current data freshness in decimal seconds: DATEDIFF('millisecond', MAX(GAME_ENDED_AT), CONVERT_TIMEZONE('UTC', CURRENT_TIMESTAMP())::TIMESTAMP_NTZ) / 1000.0\n   - Freshness over last hour as a time-series line chart bucketed by DATE_TRUNC('minute', GAME_ENDED_AT)\n   - Rows/sec over last hour: count per minute bucket divided by 60, as a time-series line chart\n\n**Additional Guidance:**\n- Validate all data types, units and calculations, in both SQL and Python layers\n- Ensure that all data presented is intuitive and informative\n- Ensure that the layout and design of the dashboard is sleek and appealing\n```\n\nCortex will write the Streamlit app file and deploy it to Snowflake. Once complete, open the dashboard from **Snowsight → Streamlit → ARCADE_SCORES_DASHBOARD**.\n\n\u003E The dashboard queries `ARCADE_SCORES` through `SUMMIT_INT_WH` — the same Interactive Warehouse used in the lab exercises. All nine pages will refresh with live data as long as the Python streamer is running.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nWhen you are done with the lab, run the teardown script to remove all Snowflake objects and stop billing.\n\n1. Open **`sql/05_cleanup.sql`** in Snowsight\n2. Run the entire script as `ACCOUNTADMIN`\n\nThis drops (in order): the Streamlit dashboard, compute pool, both warehouses, the `ARCADE_DB` database (which cascades to the Interactive Table and all pipes), the service user, and both roles.\n\n\u003E The Interactive Warehouse (`SUMMIT_INT_WH`) bills a **minimum of 1 hour** from the time it is created, then per-second thereafter. Run cleanup as soon as you are finished with the lab to avoid unnecessary charges.\n\nYou can also stop the Python streamer at any time with **Ctrl-C** in the Codespace terminal.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nIn this lab you built a fully operational real-time data pipeline on Snowflake:\n\n- Streamed thousands of rows per second directly into an **Interactive Table** using the **Snowpipe Streaming Python SDK** — no files, no `COPY INTO`, no batch windows\n- Queried live data with **sub-second latency** using an **Interactive Warehouse** backed by pre-computed indexes and a warm SSD cache\n- Experienced how `CLUSTER BY (GAME_ENDED_AT)` eliminates micro-partition scans on time-range predicates — the key to Interactive Warehouse performance at scale\n- Measured end-to-end pipeline freshness: from Python generator to queryable Snowflake row in under 2 seconds\n- Compared the Interactive and Standard warehouse head-to-head under the same query workload\n\n### What You Learned\n\n- How Snowpipe Streaming's **channel API** enables direct, low-latency row-level ingestion without staging\n- The architectural difference between **Interactive Tables** and standard Snowflake tables\n- How an **Interactive Warehouse** uses persistent SSD caching and pre-computed indexes to deliver sub-second queries\n- Why `CLUSTER BY` on a time column is essential for time-series workloads on Interactive Tables\n- How to measure **data freshness** and **SDK processing latency** from within Snowflake\n\n### Related Resources\n\n- [Lab GitHub Repository](https://github.com/Snowflake-Labs/Summit26-InteractiveLab)\n- [Snowpipe Streaming Documentation](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-overview)\n- [Snowpipe Streaming Python SDK Reference](https://docs.snowflake.com/en/user-guide/snowpipe-streaming-sdk-python/reference/latest/api/snowflake/ingest/streaming/index)\n- [Interactive Tables Documentation](https://docs.snowflake.com/en/user-guide/interactive)\n- [Interactive Warehouses Documentation](https://docs.snowflake.com/en/user-guide/warehouses-interactive)\n- [Snowflake Quickstarts](https://quickstarts.snowflake.com)\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,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-f62d817b41","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-60bd53d602",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-7dbfe4fce4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-06-01",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-d7b63d19bc","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake 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the Lab in a GitHub Codespace\u003C/h2\u003E","\u003Ch2\u003EProvision Snowflake Objects\u003C/h2\u003E","\u003Ch2\u003ERegister the RSA Key Pair\u003C/h2\u003E","\u003Ch2\u003EConfigure the Connection Profile\u003C/h2\u003E","\u003Ch2\u003EHow the Snowpipe Streaming SDK Works\u003C/h2\u003E","\u003Ch2\u003EStart the Arcade Streamer\u003C/h2\u003E","\u003Ch2\u003EExercise 1 — Pipeline Throughput\u003C/h2\u003E","\u003Ch2\u003EExercise 2 — Data Freshness\u003C/h2\u003E","\u003Ch2\u003EExercise 3 — Global Leaderboard\u003C/h2\u003E","\u003Ch2\u003EExercise 4 — Per-Game Top 5\u003C/h2\u003E","\u003Ch2\u003EExercises 5–9 — Deeper Analysis\u003C/h2\u003E","\u003Ch2\u003EExercise 10 — Interactive vs Traditional Warehouse\u003C/h2\u003E","\u003Ch2\u003EBonus Exercises\u003C/h2\u003E","\u003Ch2\u003EOptional: Streamlit Dashboard\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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