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The venue needs two different answers at the same moment: which seats are still free right now, and how is revenue trending. Those are different workloads. The first is a stream of tiny, exact updates. The second is a rolling aggregate over everything sold so far.\u003C/p\u003E\n","\u003Cp\u003EIn this guide you build a pipeline that serves both from a single event stream. A Python producer simulates a sellout of 320 seats and sends each purchase to Amazon Kinesis. From there, two Amazon Data Firehose delivery streams read the same Kinesis stream:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EFast path:\u003C/strong\u003E Firehose writes straight into Snowflake through its native Snowflake destination, which uses Snowpipe Streaming under the hood. A Stream and a Task then flip seats to \u003Ccode\u003ESOLD\u003C/code\u003E in a Hybrid Table.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBatch path:\u003C/strong\u003E Firehose writes JSON files to Amazon S3, S3 notifies Snowpipe through Amazon SNS, and two Dynamic Tables turn the raw rows into per-section revenue metrics.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EA local Streamlit dashboard reads both and shows a live seat map next to revenue, sell-through, and channel mix. There are no batch jobs to schedule, no manual refreshes, and no separate operational database.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn \u003Ca href=\"https://aws.amazon.com/free/\"\u003EAWS account\u003C/a\u003E in \u003Ccode\u003Eus-east-1\u003C/code\u003E with billing upgraded beyond the Free Plan, because Kinesis is not available on it\u003C/li\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E where you can use the \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E role, in a region where \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tutorials/getting-started-with-hybrid-tables-tutorial\"\u003EHybrid Tables\u003C/a\u003E are available\u003C/li\u003E\u003Cli\u003EPermission in AWS to create IAM roles and policies, Kinesis streams, Firehose delivery streams, S3 buckets, and SNS topics\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to fan one Kinesis stream out to two independent Firehose delivery streams\u003C/li\u003E\u003Cli\u003EHow to stream data into Snowflake with the Firehose native Snowflake destination and key-pair authentication\u003C/li\u003E\u003Cli\u003EHow to use a Stream and a Task to keep a Hybrid Table current with row-level \u003Ccode\u003EMERGE\u003C/code\u003E updates\u003C/li\u003E\u003Cli\u003EHow to auto-ingest files from S3 with Snowpipe, using SNS for notifications\u003C/li\u003E\u003Cli\u003EHow to build Silver and Gold layers declaratively with Dynamic Tables\u003C/li\u003E\u003Cli\u003EHow to suspend and resume the whole pipeline with stored procedures to control cost\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe \u003Ca href=\"https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html\"\u003EAWS CLI\u003C/a\u003E installed and configured with \u003Ccode\u003Eaws configure\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EPython 3.9 or later with \u003Ccode\u003Epip\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EOpenSSL, to generate a key pair. Git for Windows includes it\u003C/li\u003E\u003Cli\u003EA code editor, such as VS Code (optional)\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: Some AWS resources in this guide bill while they exist, including the Kinesis stream and the Firehose delivery streams. The Snowflake warehouse is X-Small and suspends after 60 seconds. Run the \u003Cstrong\u003EClean Up\u003C/strong\u003E step when you finish.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EYou'll build the full pipeline end to end:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake foundation:\u003C/strong\u003E a database, a schema, a warehouse, and two Bronze tables.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAWS streaming layer:\u003C/strong\u003E a Kinesis stream and an S3 bucket.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKey-pair authentication\u003C/strong\u003E for the Firehose-to-Snowflake connection and the dashboard.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFast path:\u003C/strong\u003E a Hybrid Table of 320 seats, a Stream, and a Task, fed by Firehose.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EBatch path:\u003C/strong\u003E a storage integration, a Snowpipe, and Silver and Gold Dynamic Tables, fed through S3 and SNS.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPipeline controls:\u003C/strong\u003E \u003Ccode\u003Ereset_demo()\u003C/code\u003E and \u003Ccode\u003Eresume_demo()\u003C/code\u003E procedures.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EA live dashboard\u003C/strong\u003E and a producer that sells out the venue in a few minutes.\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EHow It Works\u003C/h2\u003E\n","\u003Cp\u003EThe diagram shows both paths from the producer to the dashboard.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/architecture.png?v=34db7abb\" alt=\"Architecture of the pipeline. A Python producer sends JSON purchase events to a Kinesis data stream. The stream feeds two Firehose delivery streams. The batch path writes files to S3, notifies Snowpipe through SNS, loads the bronze_purchases table, and feeds the silver_purchases and gold_section_metrics Dynamic Tables. The fast path writes directly into the bronze_purchases_rt table, where a stream and a task merge changes into the seats Hybrid Table. A local Streamlit dashboard shows a seat map from the Hybrid Table and metrics from the Gold table. Reset and resume procedures suspend and wake the task and Dynamic Tables.\"\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003E \u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFast path\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBatch path\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EServes\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELive seat map\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERevenue, sell-through, and channel metrics\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAWS side\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFirehose, native Snowflake destination\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFirehose, S3, SNS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflake side\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBronze table, Stream, Task, Hybrid Table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowpipe, Bronze table, two Dynamic Tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ERefresh\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETask checks about every 10 seconds\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E60-second target lag, the minimum for Dynamic Tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EWhy this object\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EHybrid Tables handle fast, transactional point updates with row-level locking\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDynamic Tables replace hand-written ELT with declarative, incremental refresh\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EWhy two paths off the same stream? Dynamic Tables have a 60-second minimum target lag, so the file-based batch path costs nothing in freshness for analytics. A live seat map needs far fresher updates, so it gets its own direct path. Kinesis supports multiple consumers on one stream with no extra setup.\u003C/p\u003E\n","\u003Ch3\u003EThe Purchase Event\u003C/h3\u003E\n","\u003Cp\u003EEvery event the producer sends has this shape:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "purchase_id":  "9f1c2a3e-6b7d-4c8e-9a10-2b3c4d5e6f70",\n  "event_id":     "EVT001",\n  "seat_id":      "A-101",\n  "customer_id":  "CUST-8821",\n  "price_paid":   150.00,\n  "channel":      "web",\n  "promo_code":   "EARLY20",\n  "party_size":   4,\n  "purchased_at": "2026-07-23T01:06:07.038Z"\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EThe Venue\u003C/h3\u003E\n","\u003Cp\u003EThe venue has 320 seats: 16 sections (A to P), 2 rows per section, and 10 seats per row. The seat ID format is \u003Ccode\u003E{section}-{row}{seat:02d}\u003C/code\u003E, for example \u003Ccode\u003EA-101\u003C/code\u003E or \u003Ccode\u003EE-205\u003C/code\u003E.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETier\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESections\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPrice\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFLOOR\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA to D\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$150\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELOWER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EE to L\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$85\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUPPER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EM to P\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E$45\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPrepare Snowflake\u003C/h2\u003E\n","\u003Cp\u003EOpen a Snowsight worksheet and run everything in this step as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. The commands create the database, the schema, a small dedicated warehouse, and the two Bronze tables.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS ticketing_db;\nCREATE SCHEMA   IF NOT EXISTS ticketing_db.pipeline;\n\nCREATE WAREHOUSE IF NOT EXISTS ticketing_wh\n  WAREHOUSE_SIZE      = 'XSMALL'\n  AUTO_SUSPEND        = 60\n  AUTO_RESUME         = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nUSE WAREHOUSE ticketing_wh;\nUSE SCHEMA ticketing_db.pipeline;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Bronze Tables\u003C/h3\u003E\n","\u003Cp\u003EEach ingestion path gets its own Bronze table. They differ slightly because the two ingestion technologies have different constraints.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Batch path: loaded by Snowpipe from S3 files\nCREATE OR REPLACE TABLE ticketing_db.pipeline.bronze_purchases (\n  purchase_id    VARCHAR,\n  event_id       VARCHAR,\n  seat_id        VARCHAR,\n  customer_id    VARCHAR,\n  price_paid     NUMBER(10,2),\n  channel        VARCHAR,\n  promo_code     VARCHAR,\n  party_size     NUMBER,\n  purchased_at   TIMESTAMP_TZ,\n  _loaded_at     TIMESTAMP_TZ DEFAULT CURRENT_TIMESTAMP(),\n  _src_file      VARCHAR\n);\n\n-- Fast path: written directly by Firehose through Snowpipe Streaming\nCREATE OR REPLACE TABLE ticketing_db.pipeline.bronze_purchases_rt (\n  purchase_id    VARCHAR,\n  event_id       VARCHAR,\n  seat_id        VARCHAR,\n  customer_id    VARCHAR,\n  price_paid     NUMBER(10,2),\n  channel        VARCHAR,\n  promo_code     VARCHAR,\n  party_size     NUMBER,\n  purchased_at   TIMESTAMP_TZ\n);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT: Do not add a \u003Ccode\u003E_loaded_at\u003C/code\u003E column, or any column with a \u003Ccode\u003EDEFAULT\u003C/code\u003E, to \u003Ccode\u003Ebronze_purchases_rt\u003C/code\u003E. Snowpipe Streaming, which the Firehose Snowflake destination uses, rejects a table that has a column with \u003Ccode\u003EDEFAULT\u003C/code\u003E, \u003Ccode\u003EAUTOINCREMENT\u003C/code\u003E, \u003Ccode\u003EIDENTITY\u003C/code\u003E, \u003Ccode\u003EGEO\u003C/code\u003E, or a collation. It does not skip the column. It fails the entire delivery with a \u003Ccode\u003ESnowflake.InvalidColumns\u003C/code\u003E error.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate AWS Streaming Layer\u003C/h2\u003E\n","\u003Cp\u003EIn the AWS console, switch to the \u003Ccode\u003Eus-east-1\u003C/code\u003E region. You'll create the Kinesis stream and the S3 bucket now. The Firehose delivery streams come later, once Snowflake is ready to receive data.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Kinesis Data Stream\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen \u003Cstrong\u003EKinesis\u003C/strong\u003E, select \u003Cstrong\u003EData streams\u003C/strong\u003E, then \u003Cstrong\u003ECreate data stream\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EName the stream \u003Ccode\u003Eticketing-stream\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EChoose \u003Cstrong\u003EOn-demand\u003C/strong\u003E capacity, so there is no shard math.\u003C/li\u003E\u003Cli\u003ELeave data retention at the 1-day default and select \u003Cstrong\u003ECreate data stream\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EBoth Firehose delivery streams will read from this one stream at the same time. Kinesis supports multiple consumers with no extra configuration.\u003C/p\u003E\n","\u003Ch3\u003ECreate the S3 Bucket\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen \u003Cstrong\u003ES3\u003C/strong\u003E and select \u003Cstrong\u003ECreate bucket\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EChoose a globally unique name, for example \u003Ccode\u003Eticketing-store-<your-initials>-<random-digits>\u003C/code\u003E. S3 bucket names are shared across all AWS accounts. This guide refers to your name as \u003Ccode\u003E<your-bucket-name>\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EChoose \u003Ccode\u003Eus-east-1\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EKeep the defaults: ACLs disabled, all four \u003Cstrong\u003EBlock Public Access\u003C/strong\u003E settings on, versioning disabled, and SSE-S3 encryption.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECreate bucket\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure Key-Pair Auth\u003C/h2\u003E\n","\u003Cp\u003EThe Firehose delivery stream and the dashboard both connect to Snowflake with an RSA key pair instead of a password. This keeps credentials out of config files, and it works if your user has MFA enforced, because an MFA code rotates and cannot be stored for an unattended process.\u003C/p\u003E\n","\u003Cp\u003ECreate a project folder and generate the key pair inside it. The commands produce a 2048-bit private key in PKCS8 format with no passphrase, and the matching public key.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Emkdir ticketing-pipeline\ncd ticketing-pipeline\nmkdir firehose-snowflake-auth\ncd firehose-snowflake-auth\n\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT: \u003Ccode\u003Ersa_key.p8\u003C/code\u003E is a private key. Never commit it to Git or share it. If you put this folder under version control, add \u003Ccode\u003Ersa_key.p8\u003C/code\u003E to \u003Ccode\u003E.gitignore\u003C/code\u003E first.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERegister the Public Key\u003C/h3\u003E\n","\u003Cp\u003EOpen \u003Ccode\u003Ersa_key.pub\u003C/code\u003E. Copy the base64 text between the \u003Ccode\u003E-----BEGIN PUBLIC KEY-----\u003C/code\u003E and \u003Ccode\u003E-----END PUBLIC KEY-----\u003C/code\u003E lines, joined into a single line with no line breaks. Then run this in Snowflake, replacing the placeholders:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER USER <your_user>\n  SET RSA_PUBLIC_KEY = '<paste base64 body here, no header, no footer, no line breaks>';\n\n-- Verify: RSA_PUBLIC_KEY_FP and RSA_PUBLIC_KEY_LAST_SET_TIME should be populated\nDESC USER <your_user>;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EFind Your Account Identifier\u003C/h3\u003E\n","\u003Cp\u003EYou need your Snowflake account identifier for Firehose and for the dashboard. In Snowsight, open the account menu, select \u003Cstrong\u003EView account details\u003C/strong\u003E, and copy the identifier in \u003Ccode\u003E<orgname>-<account_name>\u003C/code\u003E format. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier\"\u003EAccount identifiers\u003C/a\u003E for the other formats. Your account URL is \u003Ccode\u003Ehttps://<orgname>-<account_name>.snowflakecomputing.com\u003C/code\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Fast Path\u003C/h2\u003E\n","\u003Cp\u003EThe fast path has a Snowflake half and an AWS half. You'll build the Snowflake objects first, then connect Firehose to them.\u003C/p\u003E\n","\u003Ch3\u003ECreate and Seed the Seats Table\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Eseats\u003C/code\u003E Hybrid Table holds the live state of every seat. It requires a primary key, which gives it row-level locking and indexed point lookups. The static columns are loaded once, and only \u003Ccode\u003Estatus\u003C/code\u003E and \u003Ccode\u003Esold_at\u003C/code\u003E change afterward.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE HYBRID TABLE ticketing_db.pipeline.seats (\n  seat_id      VARCHAR PRIMARY KEY,\n  event_id     VARCHAR,\n  section      VARCHAR,\n  row_number   NUMBER,\n  seat_number  NUMBER,\n  price_tier   VARCHAR,\n  price        NUMBER(10,2),\n  status       VARCHAR DEFAULT 'AVAILABLE',\n  sold_at      TIMESTAMP_TZ\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESeed all 320 seats:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO ticketing_db.pipeline.seats\n  (seat_id, event_id, section, row_number, seat_number, price_tier, price, status, sold_at)\nSELECT\n  section || '-' || (row_num::STRING) || LPAD(seat_num::STRING, 2, '0') AS seat_id,\n  'EVT001', section, row_num, seat_num, price_tier, price, 'AVAILABLE', NULL\nFROM (\n  SELECT\n    CHR(65 + FLOOR(idx / 20))         AS section,\n    FLOOR(MOD(idx, 20) / 10) + 1      AS row_num,\n    MOD(idx, 10) + 1                  AS seat_num,\n    CASE\n      WHEN FLOOR(idx / 20) <= 3  THEN 'FLOOR'   -- sections A-D\n      WHEN FLOOR(idx / 20) <= 11 THEN 'LOWER'   -- sections E-L\n      ELSE                            'UPPER'   -- sections M-P\n    END AS price_tier,\n    CASE\n      WHEN FLOOR(idx / 20) <= 3  THEN 150.00\n      WHEN FLOOR(idx / 20) <= 11 THEN  85.00\n      ELSE                             45.00\n    END AS price\n  FROM (SELECT SEQ4() AS idx FROM TABLE(GENERATOR(ROWCOUNT => 320)))\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the seed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM ticketing_db.pipeline.seats;                          -- expect 320\nSELECT section, price_tier, price, COUNT(*) FROM ticketing_db.pipeline.seats\n  GROUP BY section, price_tier, price ORDER BY section;                    -- 20 per section\nSELECT status, COUNT(*) FROM ticketing_db.pipeline.seats GROUP BY status;  -- 320 AVAILABLE\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck that sections A to D are \u003Ccode\u003EFLOOR\u003C/code\u003E, E to L are \u003Ccode\u003ELOWER\u003C/code\u003E, and M to P are \u003Ccode\u003EUPPER\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Stream and the Task\u003C/h3\u003E\n","\u003Cp\u003EA Stream is an offset pointer into a table's change log. It is not a queue that drains itself. The offset only advances when a statement reads the stream and commits, so a failed Task run leaves the backlog in place to be retried.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STREAM ticketing_db.pipeline.bronze_purchases_rt_stream\n  ON TABLE ticketing_db.pipeline.bronze_purchases_rt;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe Task runs only when the Stream has data, reads the new inserts, and merges them into \u003Ccode\u003Eseats\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TASK ticketing_db.pipeline.seat_status_update_task\n    WAREHOUSE = ticketing_wh\n    USER_TASK_MINIMUM_TRIGGER_INTERVAL_IN_SECONDS = 10\n    WHEN SYSTEM$STREAM_HAS_DATA('ticketing_db.pipeline.bronze_purchases_rt_stream')\nAS\nMERGE INTO ticketing_db.pipeline.seats AS tgt\nUSING (\n    SELECT seat_id, purchased_at\n    FROM (\n        SELECT seat_id, purchased_at,\n               ROW_NUMBER() OVER (PARTITION BY seat_id ORDER BY purchased_at DESC) AS rn\n        FROM ticketing_db.pipeline.bronze_purchases_rt_stream\n        WHERE METADATA$ACTION = 'INSERT'   -- ignore DELETE records created by TRUNCATE\n    )\n    WHERE rn = 1                            -- keep only the newest record per seat\n) AS src\nON tgt.seat_id = src.seat_id\nWHEN MATCHED THEN UPDATE SET\n    tgt.status  = 'SOLD',\n    tgt.sold_at = src.purchased_at;\n\n-- Tasks are created suspended. Without this line the Task never runs.\nALTER TASK ticketing_db.pipeline.seat_status_update_task RESUME;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The Task needs both safeguards in the \u003Ccode\u003EMERGE\u003C/code\u003E. \u003Ccode\u003ETRUNCATE TABLE\u003C/code\u003E writes DELETE records into the Stream, and without the \u003Ccode\u003EMETADATA$ACTION = 'INSERT'\u003C/code\u003E filter a DELETE and an INSERT for the same seat collide on the primary key. A backlog from failed runs can also hold the same \u003Ccode\u003Eseat_id\u003C/code\u003E more than once, which the \u003Ccode\u003EROW_NUMBER()\u003C/code\u003E filter resolves.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: 10 seconds is the minimum value for \u003Ccode\u003EUSER_TASK_MINIMUM_TRIGGER_INTERVAL_IN_SECONDS\u003C/code\u003E. The \u003Ccode\u003EWHEN\u003C/code\u003E clause accepts only \u003Ccode\u003ESYSTEM$STREAM_HAS_DATA\u003C/code\u003E and \u003Ccode\u003ESYSTEM$GET_PREDECESSOR_RETURN_VALUE\u003C/code\u003E. Checking the Stream does not start the warehouse. The warehouse runs only when the Task actually fires.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Fast-Path Firehose Stream\u003C/h3\u003E\n","\u003Cp\u003ENow connect AWS to the Snowflake table. In the AWS console, open \u003Cstrong\u003EAmazon Data Firehose\u003C/strong\u003E and select \u003Cstrong\u003ECreate Firehose stream\u003C/strong\u003E.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESetting\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESource\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAmazon Kinesis Data Streams, \u003Ccode\u003Eticketing-stream\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDestination\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFirehose stream name\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eticketing-firehose-snow\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake account URL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ehttps://<orgname>-<account_name>.snowflakecomputing.com\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAuthentication\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPrivate key. Paste the key body of \u003Ccode\u003Ersa_key.p8\u003C/code\u003E (the base64 text between the \u003Ccode\u003EBEGIN\u003C/code\u003E and \u003Ccode\u003EEND\u003C/code\u003E lines, without those two lines) and enter your Snowflake user\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse the default Snowflake role of that user\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake database\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETICKETING_DB\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake schema\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EPIPELINE\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESnowflake table\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EBRONZE_PURCHASES_RT\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EData loading option\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUse JSON keys as table column names (\u003Ccode\u003EJSON_MAPPING\u003C/code\u003E)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERetry duration\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E60 seconds (default)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBuffer size and interval\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1 MiB and 0 seconds\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESource record backup\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFailed data only\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ES3 backup bucket\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<your-bucket-name>\u003C/code\u003E (the bucket name only, not an \u003Ccode\u003Es3://\u003C/code\u003E URI)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EError output prefix\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ert-failed/\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/firehose_snowflake_destination.png?v=34db7abb\" alt=\"Firehose Snowflake destination settings showing the TICKETING_DB database, PIPELINE schema, BRONZE_PURCHASES_RT table, JSON keys as column names, 60 second retry and a 1 MiB, 0 second buffer\"\u003E\u003C/p\u003E\n","\u003Cp\u003ELeave the service role on \u003Cstrong\u003ECreate or update IAM role\u003C/strong\u003E, so the console creates it for you. Leave the VPCE ID blank. Firehose reaches Snowflake over public HTTPS, even when your Snowflake account is in a different cloud region than your AWS resources.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Ccode\u003EJSON_MAPPING\u003C/code\u003E option maps each JSON key to a column of the same name, which is why the Bronze column names match the producer's JSON keys exactly.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: S3 here is only a dead-letter location. The active data path is Firehose to Snowflake with no files in between. If deliveries fail, check the backup location. Each failed record includes the exact error message, for example \u003Ccode\u003ESnowflake.InvalidColumns\u003C/code\u003E, and the original payload. Enter only the bucket name in the backup field. If you enter a full \u003Ccode\u003Es3://\u003C/code\u003E URI, Firehose nests it literally into the object key.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The failed-record backup shares a bucket with the batch path's JSON files. The batch path's S3 event notification covers the whole bucket, so a file written under \u003Ccode\u003Ert-failed/\u003C/code\u003E after a failed delivery can also reach Snowpipe. In a healthy run nothing is written there. If you see unexpected rows in \u003Ccode\u003Ebronze_purchases\u003C/code\u003E, check the files under \u003Ccode\u003Ert-failed/\u003C/code\u003E first. For a longer-lived setup, use a separate bucket for the backup.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBuild the Batch Path\u003C/h2\u003E\n","\u003Cp\u003EThe batch path loads JSON files from S3 into \u003Ccode\u003Ebronze_purchases\u003C/code\u003E with Snowpipe, then refines the data with Dynamic Tables. The order matters here. Snowflake has to generate its AWS identity before you can write the AWS policies that trust it.\u003C/p\u003E\n","\u003Ch3\u003ECreate the Storage Integration\u003C/h3\u003E\n","\u003Cp\u003EA storage integration lets Snowflake read from your bucket without handling AWS keys. Snowflake generates its own AWS identity only after the integration exists, so you create it with a placeholder role first and fix it afterward.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STORAGE INTEGRATION ticketing_s3_int\n  TYPE                      = EXTERNAL_STAGE\n  STORAGE_PROVIDER          = 'S3'\n  ENABLED                   = TRUE\n  STORAGE_AWS_ROLE_ARN      = 'arn:aws:iam::<aws-account-id>:role/PLACEHOLDER_WILL_UPDATE'\n  STORAGE_ALLOWED_LOCATIONS = ('s3://<your-bucket-name>/');\n\nDESC STORAGE INTEGRATION ticketing_s3_int;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFrom the output, copy two values:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ESTORAGE_AWS_IAM_USER_ARN\u003C/code\u003E, which is the identity Snowflake will use. This guide calls it \u003Ccode\u003E<snowflake-iam-user-arn>\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ESTORAGE_AWS_EXTERNAL_ID\u003C/code\u003E, which proves the request comes from your account. This guide calls it \u003Ccode\u003E<storage-aws-external-id>\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECreate the IAM Role\u003C/h3\u003E\n","\u003Cp\u003EIn the AWS console, open \u003Cstrong\u003EIAM\u003C/strong\u003E and create the permissions policy first. Choose \u003Cstrong\u003EPolicies\u003C/strong\u003E, then \u003Cstrong\u003ECreate policy\u003C/strong\u003E, and paste this JSON. Name it \u003Ccode\u003Esnowflake-ticketing-s3-policy\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    { "Effect": "Allow", "Action": ["s3:GetObject"],  "Resource": "arn:aws:s3:::<your-bucket-name>/*" },\n    { "Effect": "Allow", "Action": ["s3:ListBucket"], "Resource": "arn:aws:s3:::<your-bucket-name>" }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen choose \u003Cstrong\u003ERoles\u003C/strong\u003E, then \u003Cstrong\u003ECreate role\u003C/strong\u003E, select \u003Cstrong\u003ECustom trust policy\u003C/strong\u003E, and paste this JSON. Attach \u003Ccode\u003Esnowflake-ticketing-s3-policy\u003C/code\u003E and name the role \u003Ccode\u003Esnowflake-ticketing-s3-role\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    {\n      "Effect": "Allow",\n      "Principal": { "AWS": "<snowflake-iam-user-arn>" },\n      "Action": "sts:AssumeRole",\n      "Condition": { "StringEquals": { "sts:ExternalId": "<storage-aws-external-id>" } }\n    }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPoint the integration at the real role:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER STORAGE INTEGRATION ticketing_s3_int\n  SET STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::<aws-account-id>:role/snowflake-ticketing-s3-role';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The ARN must be a single-quoted string. An unquoted ARN is parsed as an identifier and causes a syntax error.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the External Stage\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE ticketing_db.pipeline.ticketing_s3_stage\n  URL                 = 's3://<your-bucket-name>/'\n  STORAGE_INTEGRATION = ticketing_s3_int\n  FILE_FORMAT         = (TYPE = 'JSON');\n\nLIST @ticketing_db.pipeline.ticketing_s3_stage;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf \u003Ccode\u003ELIST\u003C/code\u003E returns without an access error, the trust chain from Snowflake to the integration, the IAM role, and the bucket is working. The result is empty until the first files arrive.\u003C/p\u003E\n","\u003Ch3\u003ECreate the SNS Topic\u003C/h3\u003E\n","\u003Cp\u003ESnowpipe learns about new files through an SNS topic. In the AWS console, open \u003Cstrong\u003ESNS\u003C/strong\u003E, select \u003Cstrong\u003ETopics\u003C/strong\u003E, then \u003Cstrong\u003ECreate topic\u003C/strong\u003E. Choose \u003Cstrong\u003EStandard\u003C/strong\u003E, name it \u003Ccode\u003Eticketing-sns-topic\u003C/code\u003E, and create it.\u003C/p\u003E\n","\u003Cp\u003EThen edit the topic's \u003Cstrong\u003EAccess policy\u003C/strong\u003E. Select all of the existing text, delete it, and paste this policy. It has two statements, and both are required: one lets S3 publish to the topic, the other lets Snowflake subscribe to it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "Version": "2012-10-17",\n  "Statement": [\n    {\n      "Sid": "AllowS3Publish",\n      "Effect": "Allow",\n      "Principal": { "Service": "s3.amazonaws.com" },\n      "Action": "SNS:Publish",\n      "Resource": "arn:aws:sns:us-east-1:<aws-account-id>:ticketing-sns-topic",\n      "Condition": { "ArnLike": { "aws:SourceArn": "arn:aws:s3:::<your-bucket-name>" } }\n    },\n    {\n      "Sid": "AllowSnowflakeSubscribe",\n      "Effect": "Allow",\n      "Principal": { "AWS": "<snowflake-iam-user-arn>" },\n      "Action": ["SNS:Subscribe", "SNS:Receive"],\n      "Resource": "arn:aws:sns:us-east-1:<aws-account-id>:ticketing-sns-topic"\n    }\n  ]\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: Always select all, delete, and paste when you replace a policy in the console. Partial edits cause brace mismatches that are hard to spot.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESend S3 Events to SNS\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EOpen your S3 bucket, select \u003Cstrong\u003EProperties\u003C/strong\u003E, and scroll to \u003Cstrong\u003EEvent notifications\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECreate event notification\u003C/strong\u003E and name it \u003Ccode\u003Eticketing-new-files\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EUnder event types, select \u003Cstrong\u003EAll object create events\u003C/strong\u003E (\u003Ccode\u003Es3:ObjectCreated:*\u003C/code\u003E).\u003C/li\u003E\u003Cli\u003EUnder destination, choose \u003Cstrong\u003ESNS topic\u003C/strong\u003E and select \u003Ccode\u003Eticketing-sns-topic\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ESave the notification.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECreate the Batch Firehose Stream\u003C/h3\u003E\n","\u003Cp\u003ECreate a second Firehose delivery stream, this one writing files to S3. In \u003Cstrong\u003EAmazon Data Firehose\u003C/strong\u003E, select \u003Cstrong\u003ECreate Firehose stream\u003C/strong\u003E.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESetting\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EValue\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESource\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAmazon Kinesis Data Streams, \u003Ccode\u003Eticketing-stream\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDestination\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAmazon S3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFirehose stream name\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Eticketing-firehose\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ES3 bucket\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003E<your-bucket-name>\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENew line delimiter\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEnabled\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBuffer size and interval\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E1 MiB and 0 seconds\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompression\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENone\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/firehose_s3_destination.png?v=34db7abb\" alt=\"Firehose S3 destination settings with the new line delimiter enabled\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/firehose_s3_buffer.png?v=34db7abb\" alt=\"Firehose S3 buffer hints showing a 1 MiB buffer size and a 0 second buffer interval\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT: Keep the new line delimiter enabled. Without it, Firehose joins all the JSON records in a delivery into one line, and Snowpipe cannot parse them. Snowpipe expects one JSON object per line.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The 0-second buffer cuts transit time sharply. It also writes many small files, roughly 60 to 100 for a full run, which is fine at this scale and is not advisable at production volume.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Snowpipe\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003EAWS_SNS_TOPIC\u003C/code\u003E takes the topic's ARN as a plain string. It is not a Snowflake integration object. Snowflake creates and manages its own SQS queue and subscribes it to your topic.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PIPE ticketing_db.pipeline.ticketing_snowpipe\n  AUTO_INGEST   = TRUE\n  AWS_SNS_TOPIC = 'arn:aws:sns:us-east-1:<aws-account-id>:ticketing-sns-topic'\nAS\nCOPY INTO ticketing_db.pipeline.bronze_purchases (\n  purchase_id, event_id, seat_id, customer_id, price_paid,\n  channel, promo_code, party_size, purchased_at, _src_file\n)\nFROM (\n  SELECT\n    $1:purchase_id::VARCHAR,\n    $1:event_id::VARCHAR,\n    $1:seat_id::VARCHAR,\n    $1:customer_id::VARCHAR,\n    $1:price_paid::NUMBER(10,2),\n    $1:channel::VARCHAR,\n    $1:promo_code::VARCHAR,\n    $1:party_size::NUMBER,\n    $1:purchased_at::TIMESTAMP_TZ,\n    METADATA$FILENAME\n  FROM @ticketing_db.pipeline.ticketing_s3_stage\n)\nFILE_FORMAT = (TYPE = 'JSON');\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: A staged JSON file has no declared columns, so Snowflake loads each record as one \u003Ccode\u003EVARIANT\u003C/code\u003E value in column position 1, written \u003Ccode\u003E$1\u003C/code\u003E. \u003Ccode\u003E$1:field_name\u003C/code\u003E extracts a key, and the \u003Ccode\u003E::TYPE\u003C/code\u003E suffix casts it.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: If Snowpipe does not pick up files, the SNS subscription may be stuck. \u003Ccode\u003ECREATE OR REPLACE PIPE\u003C/code\u003E does not force a new subscription when one already exists, even a broken one. To reset, drop the pipe, delete the SNS topic and recreate it with the same name, reapply the access policy, check that the S3 event notification still points at the topic, then create the pipe again.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECreate the Dynamic Tables\u003C/h3\u003E\n","\u003Cp\u003ESilver cleans each row and calculates the price after any promo discount. The section comes straight from the seat ID, so no join is needed. Every column depends on the current row only, with no subqueries, which keeps the table eligible for incremental refresh.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DYNAMIC TABLE ticketing_db.pipeline.silver_purchases\n    TARGET_LAG = '60 seconds'\n    WAREHOUSE  = ticketing_wh\nAS\nSELECT\n    purchase_id,\n    seat_id,\n    LEFT(seat_id, 1) AS section,\n    customer_id,\n    channel,\n    promo_code,\n    party_size,\n    price_paid,\n    price_paid * CASE promo_code\n        WHEN 'EARLY20' THEN 0.80\n        WHEN 'VIP10'   THEN 0.90\n        WHEN 'GROUP15' THEN 0.85\n        ELSE 1.0\n    END AS effective_price,\n    purchased_at,\n    _loaded_at\nFROM ticketing_db.pipeline.bronze_purchases;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EGold aggregates Silver into one row per section and channel. This feeds the dashboard's metrics.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics\n    TARGET_LAG = '60 seconds'\n    WAREHOUSE  = ticketing_wh\nAS\nSELECT\n    section,\n    channel,\n    COUNT(*)                         AS tickets_sold,\n    ROUND(SUM(effective_price), 2)   AS total_revenue,\n    ROUND(AVG(effective_price), 2)   AS avg_effective_price,\n    ROUND(COUNT(*) / 20.0 * 100, 1)  AS sell_through_pct,   -- 20 seats per section\n    MAX(purchased_at)                AS last_sale_at\nFROM ticketing_db.pipeline.silver_purchases\nGROUP BY section, channel;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: 60 seconds is the minimum target lag for a Dynamic Table. Gold is a plain \u003Ccode\u003EGROUP BY\u003C/code\u003E, so a section appears only after its first sale. Right after a reset the table is empty, and the dashboard's metrics stay blank until the first purchases land.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAdd Pipeline Controls\u003C/h2\u003E\n","\u003Cp\u003ETwo stored procedures turn the pipeline off and on. \u003Ccode\u003Ereset_demo()\u003C/code\u003E clears all data and suspends the Task and both Dynamic Tables, so nothing wakes the warehouse while you are idle. \u003Ccode\u003Eresume_demo()\u003C/code\u003E wakes everything back up and forces a refresh.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE PROCEDURE ticketing_db.pipeline.reset_demo()\nRETURNS VARCHAR\nLANGUAGE SQL\nEXECUTE AS OWNER\nAS\n$$\nBEGIN\n    -- Reset data\n    TRUNCATE TABLE ticketing_db.pipeline.bronze_purchases;\n    TRUNCATE TABLE ticketing_db.pipeline.bronze_purchases_rt;\n\n    UPDATE ticketing_db.pipeline.seats\n    SET status = 'AVAILABLE', sold_at = NULL;\n\n    -- Refresh Silver and Gold before suspending, so the dashboard shows\n    -- an empty state right away instead of stale numbers.\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases REFRESH;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics REFRESH;\n\n    -- Suspend the pipeline to stop warehouse wake-ups\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases SUSPEND;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics SUSPEND;\n    ALTER TASK ticketing_db.pipeline.seat_status_update_task SUSPEND;\n\n    RETURN 'Demo reset complete and pipeline suspended. Call resume_demo() to restart.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE ticketing_db.pipeline.resume_demo()\nRETURNS VARCHAR\nLANGUAGE SQL\nEXECUTE AS OWNER\nAS\n$$\nBEGIN\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases RESUME;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics RESUME;\n\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases REFRESH;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics REFRESH;\n\n    ALTER TASK ticketing_db.pipeline.seat_status_update_task RESUME;\n\n    RETURN 'Pipeline resumed: dynamic tables refreshed and active, task running.';\nEND;\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe operating pattern is simple. Between runs, call \u003Ccode\u003Ereset_demo()\u003C/code\u003E. Right before each producer run, call \u003Ccode\u003Eresume_demo()\u003C/code\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL ticketing_db.pipeline.reset_demo();    -- clears data, suspends everything\nCALL ticketing_db.pipeline.resume_demo();   -- wakes everything up\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT: \u003Ccode\u003Ereset_demo()\u003C/code\u003E leaves the pipeline suspended on purpose. It will not wake itself up, so always call \u003Ccode\u003Eresume_demo()\u003C/code\u003E before the next run.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EIf the Seat Map Stops Updating\u003C/h3\u003E\n","\u003Cp\u003EThe most common cause is a suspended Task. Check it, and look at recent runs for an error:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW TASKS LIKE 'SEAT_STATUS_UPDATE_TASK' IN SCHEMA ticketing_db.pipeline;   -- state should be "started"\n\nSELECT name, state, error_code, error_message, scheduled_time\nFROM TABLE(ticketing_db.INFORMATION_SCHEMA.TASK_HISTORY(\n    TASK_NAME => 'SEAT_STATUS_UPDATE_TASK',\n    SCHEDULED_TIME_RANGE_START => DATEADD('hour', -1, CURRENT_TIMESTAMP())))\nORDER BY scheduled_time DESC\nLIMIT 20;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf the state is \u003Ccode\u003Esuspended\u003C/code\u003E, run \u003Ccode\u003ECALL ticketing_db.pipeline.resume_demo();\u003C/code\u003E. Snowflake also suspends a Task by itself after repeated failures in a row. The default is 10, set by \u003Ccode\u003ESUSPEND_TASK_AFTER_NUM_FAILURES\u003C/code\u003E, so a persistent error in \u003Ccode\u003Eerror_message\u003C/code\u003E is worth fixing before you resume.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELaunch the Dashboard\u003C/h2\u003E\n","\u003Cp\u003EThe dashboard runs on your computer and queries Snowflake directly. It shows a seat map from the \u003Ccode\u003Eseats\u003C/code\u003E Hybrid Table and revenue metrics from the Gold table, each refreshing on its own clock.\u003C/p\u003E\n","\u003Ch3\u003EDownload the Files\u003C/h3\u003E\n","\u003Cp\u003EThe producer and the dashboard are in this guide's \u003Ccode\u003Eassets/\u003C/code\u003E folder on GitHub:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFile\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it does\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/real-time-ticketing-pipeline-aws-snowflake/assets/dashboard.py\"\u003E\u003Ccode\u003Edashboard.py\u003C/code\u003E\u003C/a\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe Streamlit dashboard\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/real-time-ticketing-pipeline-aws-snowflake/assets/producer.py\"\u003E\u003Ccode\u003Eproducer.py\u003C/code\u003E\u003C/a\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe ticket purchase simulator, used in the next step\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EOn each file's GitHub page, select \u003Cstrong\u003EDownload raw file\u003C/strong\u003E. Arrange your project folder like this:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003Eticketing-pipeline/\n  firehose-snowflake-auth/\n    rsa_key.p8\n    rsa_key.pub\n  presentation-layer/\n    dashboard.py\n    .streamlit/\n      secrets.toml\n  ticket-generator/\n    producer.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EInstall the Packages\u003C/h3\u003E\n","\u003Cp\u003EFrom the \u003Ccode\u003Eticketing-pipeline\u003C/code\u003E folder, create a virtual environment and install the dependencies for both scripts.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython -m venv venv\n\n# macOS and Linux\nsource venv/bin/activate\n# Windows\nvenv\\Scripts\\activate\n\npip install boto3 streamlit snowflake-snowpark-python cryptography plotly\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAdd Your Credentials\u003C/h3\u003E\n","\u003Cp\u003ECreate \u003Ccode\u003Epresentation-layer/.streamlit/secrets.toml\u003C/code\u003E with your own values. The dashboard reuses the private key you already registered.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-toml\"\u003E[snowflake]\naccount          = "<orgname>-<account_name>"\nuser             = "<your_user>"\nprivate_key_path = "../firehose-snowflake-auth/rsa_key.p8"\nwarehouse        = "ticketing_wh"\ndatabase         = "ticketing_db"\nschema           = "pipeline"\nrole             = "ACCOUNTADMIN"\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANT: Never commit \u003Ccode\u003Esecrets.toml\u003C/code\u003E. It names your account and the path to your private key.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStart the Dashboard\u003C/h3\u003E\n","\u003Cp\u003ELaunch from inside the \u003Ccode\u003Epresentation-layer\u003C/code\u003E folder, so Streamlit finds \u003Ccode\u003E.streamlit/secrets.toml\u003C/code\u003E and the relative key path resolves.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd presentation-layer\nstreamlit run dashboard.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYour browser opens at \u003Ccode\u003Elocalhost:8501\u003C/code\u003E. You'll see 320 available seats and an empty metrics panel. Leave it open and the page refreshes itself. The seat map re-queries every 10 seconds, and the metrics every 30 seconds. The refresh interval controls how often the page asks, not how fresh the data is. The metrics can only change when the Gold table refreshes. The dashboard shows timestamps in US Central time.\u003C/p\u003E\n<!-- VIDEO: add the YouTube link of the live demo here once published, using the embed syntax the Snowflake guides use. -->\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The video above is a recording of a complete live run. The architecture diagram shown in it is an earlier version. Where it differs from this guide, such as the phase names or the refresh rates, the guide is current.\u003C/p\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERun the Producer\u003C/h2\u003E\n","\u003Cp\u003EThe producer shuffles all 320 seats like a deck of cards, then sells them in bursts. It sends each purchase to Kinesis with the seat ID as the partition key. It reads nothing from Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EWake the Pipeline\u003C/h3\u003E\n","\u003Cp\u003EIn Snowflake, run \u003Ccode\u003Eresume_demo()\u003C/code\u003E so the Task and Dynamic Tables are active:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECALL ticketing_db.pipeline.resume_demo();\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENOTE: The first file Snowpipe sees after a quiet period can take 25 to 30 seconds to load. If you want a smooth first run, send a small throwaway batch a minute ahead, then run \u003Ccode\u003Ereset_demo()\u003C/code\u003E and \u003Ccode\u003Eresume_demo()\u003C/code\u003E again.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStart Selling\u003C/h3\u003E\n","\u003Cp\u003EOpen a second terminal, activate the same virtual environment, and run the producer from the \u003Ccode\u003Eticket-generator\u003C/code\u003E folder. It uses the AWS credentials from \u003Ccode\u003Eaws configure\u003C/code\u003E, which need permission to put records to \u003Ccode\u003Eticketing-stream\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecd ticket-generator\npython producer.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe producer announces each phase as it enters it. It simulates three waves of demand:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPhase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETickets sold\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EBurst size\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPause between bursts\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EINITIAL RUSH\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Efirst 25%\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E18 to 24 seats\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E4 to 6 seconds\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESLOWDOWN PHASE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E25% to 75%\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E6 to 12 seats\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E10 to 14 seconds\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EFINAL RUSH\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003Elast 25%\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E18 to 24 seats\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E3 to 5 seconds\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EA short random delay of 0.05 to 0.15 seconds separates the individual sales inside a burst. A full run takes a few minutes. Watch the seat map turn from green to gray as the Task merges each batch into \u003Ccode\u003Eseats\u003C/code\u003E. The KPI cards and charts fill in about a minute or two behind.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_mid_run.png?v=34db7abb\" alt=\"Dashboard seat map partway through a run, with 81 of 320 seats sold and the rest still gray\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_mid_run_metrics.png?v=34db7abb\" alt=\"Dashboard metrics partway through a run, showing total revenue, sell-out percentage, revenue by section and sales by channel\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_sold_out.png?v=34db7abb\" alt=\"Dashboard seat map at 320 of 320 seats sold, with every seat gray\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_sold_out_metrics.png?v=34db7abb\" alt=\"Dashboard metrics at sell-out, showing $27,043.75 total revenue, 100 percent sell-out and the final channel split\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVerify the Pipeline\u003C/h2\u003E\n","\u003Cp\u003EIf something looks off, check each component separately. Each block isolates one part of the pipeline.\u003C/p\u003E\n","\u003Ch3\u003ECheck the Fast Path\u003C/h3\u003E\n","\u003Cp\u003ERows land in \u003Ccode\u003Ebronze_purchases_rt\u003C/code\u003E first. A few seconds later the Task flips the matching seats.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM ticketing_db.pipeline.bronze_purchases_rt;                    -- grows during the run\n\nSELECT status, COUNT(*) FROM ticketing_db.pipeline.seats GROUP BY status;          -- SOLD count rises\n\nSELECT * FROM ticketing_db.pipeline.seats\nWHERE status = 'SOLD' ORDER BY sold_at DESC LIMIT 10;                             -- recent sold_at values\n\nSELECT COUNT(*) FROM ticketing_db.pipeline.bronze_purchases_rt_stream;            -- near zero when the Task is caught up\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen a run finishes, the number of rows in \u003Ccode\u003Ebronze_purchases_rt\u003C/code\u003E should equal the number of \u003Ccode\u003ESOLD\u003C/code\u003E seats. A mismatch means the Task fell behind or dropped rows.\u003C/p\u003E\n","\u003Ch3\u003ECheck the Batch Path\u003C/h3\u003E\n","\u003Cp\u003ERows land in \u003Ccode\u003Ebronze_purchases\u003C/code\u003E once Snowpipe has loaded the files. Silver and Gold follow within their 60-second target lag.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*) FROM ticketing_db.pipeline.bronze_purchases;\nSELECT COUNT(*) FROM ticketing_db.pipeline.silver_purchases;\nSELECT * FROM ticketing_db.pipeline.gold_section_metrics ORDER BY section, channel LIMIT 10;\n\nSELECT * FROM TABLE(INFORMATION_SCHEMA.COPY_HISTORY(\n  TABLE_NAME => 'BRONZE_PURCHASES',\n  START_TIME => DATEADD(HOUR, -1, CURRENT_TIMESTAMP())));\n\nSHOW DYNAMIC TABLES IN SCHEMA ticketing_db.pipeline;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf Gold looks empty or stale, check \u003Ccode\u003ESHOW DYNAMIC TABLES\u003C/code\u003E to confirm neither table is suspended, then wait out the 60-second lag. You can also force a refresh with \u003Ccode\u003EALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics REFRESH;\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003ETroubleshooting\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESymptom\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ELikely cause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EFix\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebronze_purchases_rt\u003C/code\u003E stays empty\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe table has a column with a \u003Ccode\u003EDEFAULT\u003C/code\u003E, or the key or account settings are wrong\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECheck the backup location in S3 (\u003Ccode\u003Ert-failed/\u003C/code\u003E) for the exact error message\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Ebronze_purchases\u003C/code\u003E stays empty\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EStuck SNS subscription, missing newline delimiter, or a bucket policy problem\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESee the notes in \u003Cstrong\u003EBuild the Batch Path\u003C/strong\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETask shows \u003Ccode\u003Esuspended\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA reset was run without resuming, or repeated failures\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun \u003Ccode\u003Eresume_demo()\u003C/code\u003E and check \u003Ccode\u003ETASK_HISTORY\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESeats stay \u003Ccode\u003EAVAILABLE\u003C/code\u003E but the Task shows \u003Ccode\u003ESUCCEEDED\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe \u003Ccode\u003Eseat_id\u003C/code\u003E format differs between the producer and the \u003Ccode\u003Eseats\u003C/code\u003E table, so the \u003Ccode\u003EMERGE\u003C/code\u003E matches nothing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECompare \u003Ccode\u003ESELECT DISTINCT seat_id\u003C/code\u003E in both tables\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDashboard cannot connect\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe account value in \u003Ccode\u003Esecrets.toml\u003C/code\u003E is wrong or the key is not registered\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERe-check the account identifier and run \u003Ccode\u003EDESC USER\u003C/code\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003EWhen you finish, remove the resources so nothing keeps billing.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EStop the producer and the dashboard with \u003Ccode\u003ECtrl+C\u003C/code\u003E in each terminal.\u003C/li\u003E\u003Cli\u003EIn Snowflake, drop everything this guide created. Dropping the database also removes the pipe, the Task, the Stream, the Dynamic Tables, and the procedures.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nALTER TASK IF EXISTS ticketing_db.pipeline.seat_status_update_task SUSPEND;\nDROP DATABASE IF EXISTS ticketing_db;\nDROP STORAGE INTEGRATION IF EXISTS ticketing_s3_int;\nDROP WAREHOUSE IF EXISTS ticketing_wh;\nALTER USER <your_user> UNSET RSA_PUBLIC_KEY;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EIn the AWS console, delete these resources:\n\u003Cul\u003E\u003Cli\u003EBoth Firehose delivery streams, \u003Ccode\u003Eticketing-firehose\u003C/code\u003E and \u003Ccode\u003Eticketing-firehose-snow\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe Kinesis stream \u003Ccode\u003Eticketing-stream\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe SNS topic \u003Ccode\u003Eticketing-sns-topic\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe S3 bucket. Empty it first, then delete it\u003C/li\u003E\u003Cli\u003EThe IAM role \u003Ccode\u003Esnowflake-ticketing-s3-role\u003C/code\u003E and the policy \u003Ccode\u003Esnowflake-ticketing-s3-policy\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EThe IAM roles the console created for the two Firehose streams\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EDelete the local \u003Ccode\u003Eticketing-pipeline\u003C/code\u003E folder, including \u003Ccode\u003Efirehose-snowflake-auth/rsa_key.p8\u003C/code\u003E.\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EYou built a pipeline where a single Kinesis stream serves two very different workloads. Firehose's native Snowflake destination fed a Hybrid Table that a Stream and a Task kept current, so a seat map could flip within seconds of a purchase. In parallel, S3, SNS, and Snowpipe fed Dynamic Tables that kept revenue metrics current with no orchestration. Two stored procedures let you suspend the whole pipeline between runs to save cost.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EOne Kinesis stream can feed several Firehose delivery streams at once, each with a different destination\u003C/li\u003E\u003Cli\u003EThe Firehose native Snowflake destination writes through Snowpipe Streaming, and it rejects any table with a \u003Ccode\u003EDEFAULT\u003C/code\u003E column\u003C/li\u003E\u003Cli\u003EA Stream, a Task, and a \u003Ccode\u003EMERGE\u003C/code\u003E keep a Hybrid Table current with row-level updates\u003C/li\u003E\u003Cli\u003ESnowpipe with an SNS topic auto-ingests files from S3, and Snowflake's AWS identity comes from a storage integration\u003C/li\u003E\u003Cli\u003EDynamic Tables build Silver and Gold layers declaratively, with a 60-second minimum target lag\u003C/li\u003E\u003Cli\u003EStored procedures can suspend and resume a Task and Dynamic Tables to control cost\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/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview\"\u003ESnowpipe Streaming overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-auto-s3\"\u003EAuto-ingest Snowpipe from Amazon S3\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/tutorials/getting-started-with-hybrid-tables-tutorial\"\u003EGetting started with Hybrid Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables/best-practices\"\u003EDynamic Tables best practices\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/key-pair-auth\"\u003EKey-pair authentication\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.aws.amazon.com/firehose/\"\u003EAmazon Data Firehose documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/automate-data-stream-ingestion-into-a-snowflake-database-by-using-snowflake-snowpipe-amazon-s3-amazon-sns-and-amazon-data-firehose.html\"\u003EAWS prescriptive guidance: automate data stream ingestion into Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Real-Time Ticketing Pipeline with AWS and Snowflake",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nA concert goes on sale and thousands of buyers arrive at once. The venue needs two different answers at the same moment: which seats are still free right now, and how is revenue trending. Those are different workloads. The first is a stream of tiny, exact updates. The second is a rolling aggregate over everything sold so far.\n\nIn this guide you build a pipeline that serves both from a single event stream. A Python producer simulates a sellout of 320 seats and sends each purchase to Amazon Kinesis. From there, two Amazon Data Firehose delivery streams read the same Kinesis stream:\n\n- **Fast path:** Firehose writes straight into Snowflake through its native Snowflake destination, which uses Snowpipe Streaming under the hood. A Stream and a Task then flip seats to `SOLD` in a Hybrid Table.\n- **Batch path:** Firehose writes JSON files to Amazon S3, S3 notifies Snowpipe through Amazon SNS, and two Dynamic Tables turn the raw rows into per-section revenue metrics.\n\nA local Streamlit dashboard reads both and shows a live seat map next to revenue, sell-through, and channel mix. There are no batch jobs to schedule, no manual refreshes, and no separate operational database.\n\n### Prerequisites\n- An [AWS account](https://aws.amazon.com/free/) in `us-east-1` with billing upgraded beyond the Free Plan, because Kinesis is not available on it\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) where you can use the `ACCOUNTADMIN` role, in a region where [Hybrid Tables](https://docs.snowflake.com/en/user-guide/tutorials/getting-started-with-hybrid-tables-tutorial) are available\n- Permission in AWS to create IAM roles and policies, Kinesis streams, Firehose delivery streams, S3 buckets, and SNS topics\n\n### What You'll Learn\n- How to fan one Kinesis stream out to two independent Firehose delivery streams\n- How to stream data into Snowflake with the Firehose native Snowflake destination and key-pair authentication\n- How to use a Stream and a Task to keep a Hybrid Table current with row-level `MERGE` updates\n- How to auto-ingest files from S3 with Snowpipe, using SNS for notifications\n- How to build Silver and Gold layers declaratively with Dynamic Tables\n- How to suspend and resume the whole pipeline with stored procedures to control cost\n\n### What You'll Need\n- The [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) installed and configured with `aws configure`\n- Python 3.9 or later with `pip`\n- OpenSSL, to generate a key pair. Git for Windows includes it\n- A code editor, such as VS Code (optional)\n\n\u003E NOTE: Some AWS resources in this guide bill while they exist, including the Kinesis stream and the Firehose delivery streams. The Snowflake warehouse is X-Small and suspends after 60 seconds. Run the **Clean Up** step when you finish.\n\n### What You'll Build\nYou'll build the full pipeline end to end:\n\n1. **Snowflake foundation:** a database, a schema, a warehouse, and two Bronze tables.\n2. **AWS streaming layer:** a Kinesis stream and an S3 bucket.\n3. **Key-pair authentication** for the Firehose-to-Snowflake connection and the dashboard.\n4. **Fast path:** a Hybrid Table of 320 seats, a Stream, and a Task, fed by Firehose.\n5. **Batch path:** a storage integration, a Snowpipe, and Silver and Gold Dynamic Tables, fed through S3 and SNS.\n6. **Pipeline controls:** `reset_demo()` and `resume_demo()` procedures.\n7. **A live dashboard** and a producer that sells out the venue in a few minutes.\n\n\u003C!-- ------------------------ --\u003E\n## How It Works\n\nThe diagram shows both paths from the producer to the dashboard.\n\n![Architecture of the pipeline. A Python producer sends JSON purchase events to a Kinesis data stream. The stream feeds two Firehose delivery streams. The batch path writes files to S3, notifies Snowpipe through SNS, loads the bronze_purchases table, and feeds the silver_purchases and gold_section_metrics Dynamic Tables. The fast path writes directly into the bronze_purchases_rt table, where a stream and a task merge changes into the seats Hybrid Table. A local Streamlit dashboard shows a seat map from the Hybrid Table and metrics from the Gold table. Reset and resume procedures suspend and wake the task and Dynamic Tables.](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/architecture.png?v=34db7abb)\n\n| | Fast path | Batch path |\n|---|---|---|\n| **Serves** | Live seat map | Revenue, sell-through, and channel metrics |\n| **AWS side** | Firehose, native Snowflake destination | Firehose, S3, SNS |\n| **Snowflake side** | Bronze table, Stream, Task, Hybrid Table | Snowpipe, Bronze table, two Dynamic Tables |\n| **Refresh** | Task checks about every 10 seconds | 60-second target lag, the minimum for Dynamic Tables |\n| **Why this object** | Hybrid Tables handle fast, transactional point updates with row-level locking | Dynamic Tables replace hand-written ELT with declarative, incremental refresh |\n\nWhy two paths off the same stream? Dynamic Tables have a 60-second minimum target lag, so the file-based batch path costs nothing in freshness for analytics. A live seat map needs far fresher updates, so it gets its own direct path. Kinesis supports multiple consumers on one stream with no extra setup.\n\n### The Purchase Event\n\nEvery event the producer sends has this shape:\n\n```json\n{\n  \"purchase_id\":  \"9f1c2a3e-6b7d-4c8e-9a10-2b3c4d5e6f70\",\n  \"event_id\":     \"EVT001\",\n  \"seat_id\":      \"A-101\",\n  \"customer_id\":  \"CUST-8821\",\n  \"price_paid\":   150.00,\n  \"channel\":      \"web\",\n  \"promo_code\":   \"EARLY20\",\n  \"party_size\":   4,\n  \"purchased_at\": \"2026-07-23T01:06:07.038Z\"\n}\n```\n\n### The Venue\n\nThe venue has 320 seats: 16 sections (A to P), 2 rows per section, and 10 seats per row. The seat ID format is `{section}-{row}{seat:02d}`, for example `A-101` or `E-205`.\n\n| Tier | Sections | Price |\n|---|---|---|\n| FLOOR | A to D | $150 |\n| LOWER | E to L | $85 |\n| UPPER | M to P | $45 |\n\n\u003C!-- ------------------------ --\u003E\n## Prepare Snowflake\n\nOpen a Snowsight worksheet and run everything in this step as `ACCOUNTADMIN`. The commands create the database, the schema, a small dedicated warehouse, and the two Bronze tables.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS ticketing_db;\nCREATE SCHEMA   IF NOT EXISTS ticketing_db.pipeline;\n\nCREATE WAREHOUSE IF NOT EXISTS ticketing_wh\n  WAREHOUSE_SIZE      = 'XSMALL'\n  AUTO_SUSPEND        = 60\n  AUTO_RESUME         = TRUE\n  INITIALLY_SUSPENDED = TRUE;\n\nUSE WAREHOUSE ticketing_wh;\nUSE SCHEMA ticketing_db.pipeline;\n```\n\n### Create the Bronze Tables\n\nEach ingestion path gets its own Bronze table. They differ slightly because the two ingestion technologies have different constraints.\n\n```sql\n-- Batch path: loaded by Snowpipe from S3 files\nCREATE OR REPLACE TABLE ticketing_db.pipeline.bronze_purchases (\n  purchase_id    VARCHAR,\n  event_id       VARCHAR,\n  seat_id        VARCHAR,\n  customer_id    VARCHAR,\n  price_paid     NUMBER(10,2),\n  channel        VARCHAR,\n  promo_code     VARCHAR,\n  party_size     NUMBER,\n  purchased_at   TIMESTAMP_TZ,\n  _loaded_at     TIMESTAMP_TZ DEFAULT CURRENT_TIMESTAMP(),\n  _src_file      VARCHAR\n);\n\n-- Fast path: written directly by Firehose through Snowpipe Streaming\nCREATE OR REPLACE TABLE ticketing_db.pipeline.bronze_purchases_rt (\n  purchase_id    VARCHAR,\n  event_id       VARCHAR,\n  seat_id        VARCHAR,\n  customer_id    VARCHAR,\n  price_paid     NUMBER(10,2),\n  channel        VARCHAR,\n  promo_code     VARCHAR,\n  party_size     NUMBER,\n  purchased_at   TIMESTAMP_TZ\n);\n```\n\n\u003E IMPORTANT: Do not add a `_loaded_at` column, or any column with a `DEFAULT`, to `bronze_purchases_rt`. Snowpipe Streaming, which the Firehose Snowflake destination uses, rejects a table that has a column with `DEFAULT`, `AUTOINCREMENT`, `IDENTITY`, `GEO`, or a collation. It does not skip the column. It fails the entire delivery with a `Snowflake.InvalidColumns` error.\n\n\u003C!-- ------------------------ --\u003E\n## Create AWS Streaming Layer\n\nIn the AWS console, switch to the `us-east-1` region. You'll create the Kinesis stream and the S3 bucket now. The Firehose delivery streams come later, once Snowflake is ready to receive data.\n\n### Create the Kinesis Data Stream\n\n1. Open **Kinesis**, select **Data streams**, then **Create data stream**.\n2. Name the stream `ticketing-stream`.\n3. Choose **On-demand** capacity, so there is no shard math.\n4. Leave data retention at the 1-day default and select **Create data stream**.\n\nBoth Firehose delivery streams will read from this one stream at the same time. Kinesis supports multiple consumers with no extra configuration.\n\n### Create the S3 Bucket\n\n1. Open **S3** and select **Create bucket**.\n2. Choose a globally unique name, for example `ticketing-store-\u003Cyour-initials\u003E-\u003Crandom-digits\u003E`. S3 bucket names are shared across all AWS accounts. This guide refers to your name as `\u003Cyour-bucket-name\u003E`.\n3. Choose `us-east-1`.\n4. Keep the defaults: ACLs disabled, all four **Block Public Access** settings on, versioning disabled, and SSE-S3 encryption.\n5. Select **Create bucket**.\n\n\u003C!-- ------------------------ --\u003E\n## Configure Key-Pair Auth\n\nThe Firehose delivery stream and the dashboard both connect to Snowflake with an RSA key pair instead of a password. This keeps credentials out of config files, and it works if your user has MFA enforced, because an MFA code rotates and cannot be stored for an unattended process.\n\nCreate a project folder and generate the key pair inside it. The commands produce a 2048-bit private key in PKCS8 format with no passphrase, and the matching public key.\n\n```bash\nmkdir ticketing-pipeline\ncd ticketing-pipeline\nmkdir firehose-snowflake-auth\ncd firehose-snowflake-auth\n\nopenssl genrsa 2048 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\n```\n\n\u003E IMPORTANT: `rsa_key.p8` is a private key. Never commit it to Git or share it. If you put this folder under version control, add `rsa_key.p8` to `.gitignore` first.\n\n### Register the Public Key\n\nOpen `rsa_key.pub`. Copy the base64 text between the `-----BEGIN PUBLIC KEY-----` and `-----END PUBLIC KEY-----` lines, joined into a single line with no line breaks. Then run this in Snowflake, replacing the placeholders:\n\n```sql\nALTER USER \u003Cyour_user\u003E\n  SET RSA_PUBLIC_KEY = '\u003Cpaste base64 body here, no header, no footer, no line breaks\u003E';\n\n-- Verify: RSA_PUBLIC_KEY_FP and RSA_PUBLIC_KEY_LAST_SET_TIME should be populated\nDESC USER \u003Cyour_user\u003E;\n```\n\n### Find Your Account Identifier\n\nYou need your Snowflake account identifier for Firehose and for the dashboard. In Snowsight, open the account menu, select **View account details**, and copy the identifier in `\u003Corgname\u003E-\u003Caccount_name\u003E` format. See [Account identifiers](https://docs.snowflake.com/en/user-guide/admin-account-identifier) for the other formats. Your account URL is `https://\u003Corgname\u003E-\u003Caccount_name\u003E.snowflakecomputing.com`.\n\n\u003C!-- ------------------------ --\u003E\n## Build the Fast Path\n\nThe fast path has a Snowflake half and an AWS half. You'll build the Snowflake objects first, then connect Firehose to them.\n\n### Create and Seed the Seats Table\n\nThe `seats` Hybrid Table holds the live state of every seat. It requires a primary key, which gives it row-level locking and indexed point lookups. The static columns are loaded once, and only `status` and `sold_at` change afterward.\n\n```sql\nCREATE OR REPLACE HYBRID TABLE ticketing_db.pipeline.seats (\n  seat_id      VARCHAR PRIMARY KEY,\n  event_id     VARCHAR,\n  section      VARCHAR,\n  row_number   NUMBER,\n  seat_number  NUMBER,\n  price_tier   VARCHAR,\n  price        NUMBER(10,2),\n  status       VARCHAR DEFAULT 'AVAILABLE',\n  sold_at      TIMESTAMP_TZ\n);\n```\n\nSeed all 320 seats:\n\n```sql\nINSERT INTO ticketing_db.pipeline.seats\n  (seat_id, event_id, section, row_number, seat_number, price_tier, price, status, sold_at)\nSELECT\n  section || '-' || (row_num::STRING) || LPAD(seat_num::STRING, 2, '0') AS seat_id,\n  'EVT001', section, row_num, seat_num, price_tier, price, 'AVAILABLE', NULL\nFROM (\n  SELECT\n    CHR(65 + FLOOR(idx / 20))         AS section,\n    FLOOR(MOD(idx, 20) / 10) + 1      AS row_num,\n    MOD(idx, 10) + 1                  AS seat_num,\n    CASE\n      WHEN FLOOR(idx / 20) \u003C= 3  THEN 'FLOOR'   -- sections A-D\n      WHEN FLOOR(idx / 20) \u003C= 11 THEN 'LOWER'   -- sections E-L\n      ELSE                            'UPPER'   -- sections M-P\n    END AS price_tier,\n    CASE\n      WHEN FLOOR(idx / 20) \u003C= 3  THEN 150.00\n      WHEN FLOOR(idx / 20) \u003C= 11 THEN  85.00\n      ELSE                             45.00\n    END AS price\n  FROM (SELECT SEQ4() AS idx FROM TABLE(GENERATOR(ROWCOUNT =\u003E 320)))\n);\n```\n\nVerify the seed:\n\n```sql\nSELECT COUNT(*) FROM ticketing_db.pipeline.seats;                          -- expect 320\nSELECT section, price_tier, price, COUNT(*) FROM ticketing_db.pipeline.seats\n  GROUP BY section, price_tier, price ORDER BY section;                    -- 20 per section\nSELECT status, COUNT(*) FROM ticketing_db.pipeline.seats GROUP BY status;  -- 320 AVAILABLE\n```\n\nCheck that sections A to D are `FLOOR`, E to L are `LOWER`, and M to P are `UPPER`.\n\n### Create the Stream and the Task\n\nA Stream is an offset pointer into a table's change log. It is not a queue that drains itself. The offset only advances when a statement reads the stream and commits, so a failed Task run leaves the backlog in place to be retried.\n\n```sql\nCREATE OR REPLACE STREAM ticketing_db.pipeline.bronze_purchases_rt_stream\n  ON TABLE ticketing_db.pipeline.bronze_purchases_rt;\n```\n\nThe Task runs only when the Stream has data, reads the new inserts, and merges them into `seats`.\n\n```sql\nCREATE OR REPLACE TASK ticketing_db.pipeline.seat_status_update_task\n    WAREHOUSE = ticketing_wh\n    USER_TASK_MINIMUM_TRIGGER_INTERVAL_IN_SECONDS = 10\n    WHEN SYSTEM$STREAM_HAS_DATA('ticketing_db.pipeline.bronze_purchases_rt_stream')\nAS\nMERGE INTO ticketing_db.pipeline.seats AS tgt\nUSING (\n    SELECT seat_id, purchased_at\n    FROM (\n        SELECT seat_id, purchased_at,\n               ROW_NUMBER() OVER (PARTITION BY seat_id ORDER BY purchased_at DESC) AS rn\n        FROM ticketing_db.pipeline.bronze_purchases_rt_stream\n        WHERE METADATA$ACTION = 'INSERT'   -- ignore DELETE records created by TRUNCATE\n    )\n    WHERE rn = 1                            -- keep only the newest record per seat\n) AS src\nON tgt.seat_id = src.seat_id\nWHEN MATCHED THEN UPDATE SET\n    tgt.status  = 'SOLD',\n    tgt.sold_at = src.purchased_at;\n\n-- Tasks are created suspended. Without this line the Task never runs.\nALTER TASK ticketing_db.pipeline.seat_status_update_task RESUME;\n```\n\n\u003E NOTE: The Task needs both safeguards in the `MERGE`. `TRUNCATE TABLE` writes DELETE records into the Stream, and without the `METADATA$ACTION = 'INSERT'` filter a DELETE and an INSERT for the same seat collide on the primary key. A backlog from failed runs can also hold the same `seat_id` more than once, which the `ROW_NUMBER()` filter resolves.\n\n\u003E NOTE: 10 seconds is the minimum value for `USER_TASK_MINIMUM_TRIGGER_INTERVAL_IN_SECONDS`. The `WHEN` clause accepts only `SYSTEM$STREAM_HAS_DATA` and `SYSTEM$GET_PREDECESSOR_RETURN_VALUE`. Checking the Stream does not start the warehouse. The warehouse runs only when the Task actually fires.\n\n### Create the Fast-Path Firehose Stream\n\nNow connect AWS to the Snowflake table. In the AWS console, open **Amazon Data Firehose** and select **Create Firehose stream**.\n\n| Setting | Value |\n|---|---|\n| Source | Amazon Kinesis Data Streams, `ticketing-stream` |\n| Destination | Snowflake |\n| Firehose stream name | `ticketing-firehose-snow` |\n| Snowflake account URL | `https://\u003Corgname\u003E-\u003Caccount_name\u003E.snowflakecomputing.com` |\n| Authentication | Private key. Paste the key body of `rsa_key.p8` (the base64 text between the `BEGIN` and `END` lines, without those two lines) and enter your Snowflake user |\n| Role | Use the default Snowflake role of that user |\n| Snowflake database | `TICKETING_DB` |\n| Snowflake schema | `PIPELINE` |\n| Snowflake table | `BRONZE_PURCHASES_RT` |\n| Data loading option | Use JSON keys as table column names (`JSON_MAPPING`) |\n| Retry duration | 60 seconds (default) |\n| Buffer size and interval | 1 MiB and 0 seconds |\n| Source record backup | Failed data only |\n| S3 backup bucket | `\u003Cyour-bucket-name\u003E` (the bucket name only, not an `s3://` URI) |\n| Error output prefix | `rt-failed/` |\n\n![Firehose Snowflake destination settings showing the TICKETING_DB database, PIPELINE schema, BRONZE_PURCHASES_RT table, JSON keys as column names, 60 second retry and a 1 MiB, 0 second buffer](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/firehose_snowflake_destination.png?v=34db7abb)\n\nLeave the service role on **Create or update IAM role**, so the console creates it for you. Leave the VPCE ID blank. Firehose reaches Snowflake over public HTTPS, even when your Snowflake account is in a different cloud region than your AWS resources.\n\nThe `JSON_MAPPING` option maps each JSON key to a column of the same name, which is why the Bronze column names match the producer's JSON keys exactly.\n\n\u003E NOTE: S3 here is only a dead-letter location. The active data path is Firehose to Snowflake with no files in between. If deliveries fail, check the backup location. Each failed record includes the exact error message, for example `Snowflake.InvalidColumns`, and the original payload. Enter only the bucket name in the backup field. If you enter a full `s3://` URI, Firehose nests it literally into the object key.\n\n\u003E NOTE: The failed-record backup shares a bucket with the batch path's JSON files. The batch path's S3 event notification covers the whole bucket, so a file written under `rt-failed/` after a failed delivery can also reach Snowpipe. In a healthy run nothing is written there. If you see unexpected rows in `bronze_purchases`, check the files under `rt-failed/` first. For a longer-lived setup, use a separate bucket for the backup.\n\n\u003C!-- ------------------------ --\u003E\n## Build the Batch Path\n\nThe batch path loads JSON files from S3 into `bronze_purchases` with Snowpipe, then refines the data with Dynamic Tables. The order matters here. Snowflake has to generate its AWS identity before you can write the AWS policies that trust it.\n\n### Create the Storage Integration\n\nA storage integration lets Snowflake read from your bucket without handling AWS keys. Snowflake generates its own AWS identity only after the integration exists, so you create it with a placeholder role first and fix it afterward.\n\n```sql\nCREATE OR REPLACE STORAGE INTEGRATION ticketing_s3_int\n  TYPE                      = EXTERNAL_STAGE\n  STORAGE_PROVIDER          = 'S3'\n  ENABLED                   = TRUE\n  STORAGE_AWS_ROLE_ARN      = 'arn:aws:iam::\u003Caws-account-id\u003E:role/PLACEHOLDER_WILL_UPDATE'\n  STORAGE_ALLOWED_LOCATIONS = ('s3://\u003Cyour-bucket-name\u003E/');\n\nDESC STORAGE INTEGRATION ticketing_s3_int;\n```\n\nFrom the output, copy two values:\n\n- `STORAGE_AWS_IAM_USER_ARN`, which is the identity Snowflake will use. This guide calls it `\u003Csnowflake-iam-user-arn\u003E`.\n- `STORAGE_AWS_EXTERNAL_ID`, which proves the request comes from your account. This guide calls it `\u003Cstorage-aws-external-id\u003E`.\n\n### Create the IAM Role\n\nIn the AWS console, open **IAM** and create the permissions policy first. Choose **Policies**, then **Create policy**, and paste this JSON. Name it `snowflake-ticketing-s3-policy`.\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    { \"Effect\": \"Allow\", \"Action\": [\"s3:GetObject\"],  \"Resource\": \"arn:aws:s3:::\u003Cyour-bucket-name\u003E/*\" },\n    { \"Effect\": \"Allow\", \"Action\": [\"s3:ListBucket\"], \"Resource\": \"arn:aws:s3:::\u003Cyour-bucket-name\u003E\" }\n  ]\n}\n```\n\nThen choose **Roles**, then **Create role**, select **Custom trust policy**, and paste this JSON. Attach `snowflake-ticketing-s3-policy` and name the role `snowflake-ticketing-s3-role`.\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Effect\": \"Allow\",\n      \"Principal\": { \"AWS\": \"\u003Csnowflake-iam-user-arn\u003E\" },\n      \"Action\": \"sts:AssumeRole\",\n      \"Condition\": { \"StringEquals\": { \"sts:ExternalId\": \"\u003Cstorage-aws-external-id\u003E\" } }\n    }\n  ]\n}\n```\n\nPoint the integration at the real role:\n\n```sql\nALTER STORAGE INTEGRATION ticketing_s3_int\n  SET STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::\u003Caws-account-id\u003E:role/snowflake-ticketing-s3-role';\n```\n\n\u003E NOTE: The ARN must be a single-quoted string. An unquoted ARN is parsed as an identifier and causes a syntax error.\n\n### Create the External Stage\n\n```sql\nCREATE OR REPLACE STAGE ticketing_db.pipeline.ticketing_s3_stage\n  URL                 = 's3://\u003Cyour-bucket-name\u003E/'\n  STORAGE_INTEGRATION = ticketing_s3_int\n  FILE_FORMAT         = (TYPE = 'JSON');\n\nLIST @ticketing_db.pipeline.ticketing_s3_stage;\n```\n\nIf `LIST` returns without an access error, the trust chain from Snowflake to the integration, the IAM role, and the bucket is working. The result is empty until the first files arrive.\n\n### Create the SNS Topic\n\nSnowpipe learns about new files through an SNS topic. In the AWS console, open **SNS**, select **Topics**, then **Create topic**. Choose **Standard**, name it `ticketing-sns-topic`, and create it.\n\nThen edit the topic's **Access policy**. Select all of the existing text, delete it, and paste this policy. It has two statements, and both are required: one lets S3 publish to the topic, the other lets Snowflake subscribe to it.\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Sid\": \"AllowS3Publish\",\n      \"Effect\": \"Allow\",\n      \"Principal\": { \"Service\": \"s3.amazonaws.com\" },\n      \"Action\": \"SNS:Publish\",\n      \"Resource\": \"arn:aws:sns:us-east-1:\u003Caws-account-id\u003E:ticketing-sns-topic\",\n      \"Condition\": { \"ArnLike\": { \"aws:SourceArn\": \"arn:aws:s3:::\u003Cyour-bucket-name\u003E\" } }\n    },\n    {\n      \"Sid\": \"AllowSnowflakeSubscribe\",\n      \"Effect\": \"Allow\",\n      \"Principal\": { \"AWS\": \"\u003Csnowflake-iam-user-arn\u003E\" },\n      \"Action\": [\"SNS:Subscribe\", \"SNS:Receive\"],\n      \"Resource\": \"arn:aws:sns:us-east-1:\u003Caws-account-id\u003E:ticketing-sns-topic\"\n    }\n  ]\n}\n```\n\n\u003E NOTE: Always select all, delete, and paste when you replace a policy in the console. Partial edits cause brace mismatches that are hard to spot.\n\n### Send S3 Events to SNS\n\n1. Open your S3 bucket, select **Properties**, and scroll to **Event notifications**.\n2. Select **Create event notification** and name it `ticketing-new-files`.\n3. Under event types, select **All object create events** (`s3:ObjectCreated:*`).\n4. Under destination, choose **SNS topic** and select `ticketing-sns-topic`.\n5. Save the notification.\n\n### Create the Batch Firehose Stream\n\nCreate a second Firehose delivery stream, this one writing files to S3. In **Amazon Data Firehose**, select **Create Firehose stream**.\n\n| Setting | Value |\n|---|---|\n| Source | Amazon Kinesis Data Streams, `ticketing-stream` |\n| Destination | Amazon S3 |\n| Firehose stream name | `ticketing-firehose` |\n| S3 bucket | `\u003Cyour-bucket-name\u003E` |\n| New line delimiter | Enabled |\n| Buffer size and interval | 1 MiB and 0 seconds |\n| Compression | None |\n\n![Firehose S3 destination settings with the new line delimiter enabled](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/firehose_s3_destination.png?v=34db7abb)\n\n![Firehose S3 buffer hints showing a 1 MiB buffer size and a 0 second buffer interval](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/firehose_s3_buffer.png?v=34db7abb)\n\n\u003E IMPORTANT: Keep the new line delimiter enabled. Without it, Firehose joins all the JSON records in a delivery into one line, and Snowpipe cannot parse them. Snowpipe expects one JSON object per line.\n\n\u003E NOTE: The 0-second buffer cuts transit time sharply. It also writes many small files, roughly 60 to 100 for a full run, which is fine at this scale and is not advisable at production volume.\n\n### Create the Snowpipe\n\n`AWS_SNS_TOPIC` takes the topic's ARN as a plain string. It is not a Snowflake integration object. Snowflake creates and manages its own SQS queue and subscribes it to your topic.\n\n```sql\nCREATE OR REPLACE PIPE ticketing_db.pipeline.ticketing_snowpipe\n  AUTO_INGEST   = TRUE\n  AWS_SNS_TOPIC = 'arn:aws:sns:us-east-1:\u003Caws-account-id\u003E:ticketing-sns-topic'\nAS\nCOPY INTO ticketing_db.pipeline.bronze_purchases (\n  purchase_id, event_id, seat_id, customer_id, price_paid,\n  channel, promo_code, party_size, purchased_at, _src_file\n)\nFROM (\n  SELECT\n    $1:purchase_id::VARCHAR,\n    $1:event_id::VARCHAR,\n    $1:seat_id::VARCHAR,\n    $1:customer_id::VARCHAR,\n    $1:price_paid::NUMBER(10,2),\n    $1:channel::VARCHAR,\n    $1:promo_code::VARCHAR,\n    $1:party_size::NUMBER,\n    $1:purchased_at::TIMESTAMP_TZ,\n    METADATA$FILENAME\n  FROM @ticketing_db.pipeline.ticketing_s3_stage\n)\nFILE_FORMAT = (TYPE = 'JSON');\n```\n\n\u003E NOTE: A staged JSON file has no declared columns, so Snowflake loads each record as one `VARIANT` value in column position 1, written `$1`. `$1:field_name` extracts a key, and the `::TYPE` suffix casts it.\n\n\u003E NOTE: If Snowpipe does not pick up files, the SNS subscription may be stuck. `CREATE OR REPLACE PIPE` does not force a new subscription when one already exists, even a broken one. To reset, drop the pipe, delete the SNS topic and recreate it with the same name, reapply the access policy, check that the S3 event notification still points at the topic, then create the pipe again.\n\n### Create the Dynamic Tables\n\nSilver cleans each row and calculates the price after any promo discount. The section comes straight from the seat ID, so no join is needed. Every column depends on the current row only, with no subqueries, which keeps the table eligible for incremental refresh.\n\n```sql\nCREATE OR REPLACE DYNAMIC TABLE ticketing_db.pipeline.silver_purchases\n    TARGET_LAG = '60 seconds'\n    WAREHOUSE  = ticketing_wh\nAS\nSELECT\n    purchase_id,\n    seat_id,\n    LEFT(seat_id, 1) AS section,\n    customer_id,\n    channel,\n    promo_code,\n    party_size,\n    price_paid,\n    price_paid * CASE promo_code\n        WHEN 'EARLY20' THEN 0.80\n        WHEN 'VIP10'   THEN 0.90\n        WHEN 'GROUP15' THEN 0.85\n        ELSE 1.0\n    END AS effective_price,\n    purchased_at,\n    _loaded_at\nFROM ticketing_db.pipeline.bronze_purchases;\n```\n\nGold aggregates Silver into one row per section and channel. This feeds the dashboard's metrics.\n\n```sql\nCREATE OR REPLACE DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics\n    TARGET_LAG = '60 seconds'\n    WAREHOUSE  = ticketing_wh\nAS\nSELECT\n    section,\n    channel,\n    COUNT(*)                         AS tickets_sold,\n    ROUND(SUM(effective_price), 2)   AS total_revenue,\n    ROUND(AVG(effective_price), 2)   AS avg_effective_price,\n    ROUND(COUNT(*) / 20.0 * 100, 1)  AS sell_through_pct,   -- 20 seats per section\n    MAX(purchased_at)                AS last_sale_at\nFROM ticketing_db.pipeline.silver_purchases\nGROUP BY section, channel;\n```\n\n\u003E NOTE: 60 seconds is the minimum target lag for a Dynamic Table. Gold is a plain `GROUP BY`, so a section appears only after its first sale. Right after a reset the table is empty, and the dashboard's metrics stay blank until the first purchases land.\n\n\u003C!-- ------------------------ --\u003E\n## Add Pipeline Controls\n\nTwo stored procedures turn the pipeline off and on. `reset_demo()` clears all data and suspends the Task and both Dynamic Tables, so nothing wakes the warehouse while you are idle. `resume_demo()` wakes everything back up and forces a refresh.\n\n```sql\nCREATE OR REPLACE PROCEDURE ticketing_db.pipeline.reset_demo()\nRETURNS VARCHAR\nLANGUAGE SQL\nEXECUTE AS OWNER\nAS\n$$\nBEGIN\n    -- Reset data\n    TRUNCATE TABLE ticketing_db.pipeline.bronze_purchases;\n    TRUNCATE TABLE ticketing_db.pipeline.bronze_purchases_rt;\n\n    UPDATE ticketing_db.pipeline.seats\n    SET status = 'AVAILABLE', sold_at = NULL;\n\n    -- Refresh Silver and Gold before suspending, so the dashboard shows\n    -- an empty state right away instead of stale numbers.\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases REFRESH;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics REFRESH;\n\n    -- Suspend the pipeline to stop warehouse wake-ups\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases SUSPEND;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics SUSPEND;\n    ALTER TASK ticketing_db.pipeline.seat_status_update_task SUSPEND;\n\n    RETURN 'Demo reset complete and pipeline suspended. Call resume_demo() to restart.';\nEND;\n$$;\n\nCREATE OR REPLACE PROCEDURE ticketing_db.pipeline.resume_demo()\nRETURNS VARCHAR\nLANGUAGE SQL\nEXECUTE AS OWNER\nAS\n$$\nBEGIN\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases RESUME;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics RESUME;\n\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.silver_purchases REFRESH;\n    ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics REFRESH;\n\n    ALTER TASK ticketing_db.pipeline.seat_status_update_task RESUME;\n\n    RETURN 'Pipeline resumed: dynamic tables refreshed and active, task running.';\nEND;\n$$;\n```\n\nThe operating pattern is simple. Between runs, call `reset_demo()`. Right before each producer run, call `resume_demo()`:\n\n```sql\nCALL ticketing_db.pipeline.reset_demo();    -- clears data, suspends everything\nCALL ticketing_db.pipeline.resume_demo();   -- wakes everything up\n```\n\n\u003E IMPORTANT: `reset_demo()` leaves the pipeline suspended on purpose. It will not wake itself up, so always call `resume_demo()` before the next run.\n\n### If the Seat Map Stops Updating\n\nThe most common cause is a suspended Task. Check it, and look at recent runs for an error:\n\n```sql\nSHOW TASKS LIKE 'SEAT_STATUS_UPDATE_TASK' IN SCHEMA ticketing_db.pipeline;   -- state should be \"started\"\n\nSELECT name, state, error_code, error_message, scheduled_time\nFROM TABLE(ticketing_db.INFORMATION_SCHEMA.TASK_HISTORY(\n    TASK_NAME =\u003E 'SEAT_STATUS_UPDATE_TASK',\n    SCHEDULED_TIME_RANGE_START =\u003E DATEADD('hour', -1, CURRENT_TIMESTAMP())))\nORDER BY scheduled_time DESC\nLIMIT 20;\n```\n\nIf the state is `suspended`, run `CALL ticketing_db.pipeline.resume_demo();`. Snowflake also suspends a Task by itself after repeated failures in a row. The default is 10, set by `SUSPEND_TASK_AFTER_NUM_FAILURES`, so a persistent error in `error_message` is worth fixing before you resume.\n\n\u003C!-- ------------------------ --\u003E\n## Launch the Dashboard\n\nThe dashboard runs on your computer and queries Snowflake directly. It shows a seat map from the `seats` Hybrid Table and revenue metrics from the Gold table, each refreshing on its own clock.\n\n### Download the Files\n\nThe producer and the dashboard are in this guide's `assets/` folder on GitHub:\n\n| File | What it does |\n|---|---|\n| [`dashboard.py`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/real-time-ticketing-pipeline-aws-snowflake/assets/dashboard.py) | The Streamlit dashboard |\n| [`producer.py`](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/real-time-ticketing-pipeline-aws-snowflake/assets/producer.py) | The ticket purchase simulator, used in the next step |\n\nOn each file's GitHub page, select **Download raw file**. Arrange your project folder like this:\n\n```text\nticketing-pipeline/\n  firehose-snowflake-auth/\n    rsa_key.p8\n    rsa_key.pub\n  presentation-layer/\n    dashboard.py\n    .streamlit/\n      secrets.toml\n  ticket-generator/\n    producer.py\n```\n\n### Install the Packages\n\nFrom the `ticketing-pipeline` folder, create a virtual environment and install the dependencies for both scripts.\n\n```bash\npython -m venv venv\n\n# macOS and Linux\nsource venv/bin/activate\n# Windows\nvenv\\Scripts\\activate\n\npip install boto3 streamlit snowflake-snowpark-python cryptography plotly\n```\n\n### Add Your Credentials\n\nCreate `presentation-layer/.streamlit/secrets.toml` with your own values. The dashboard reuses the private key you already registered.\n\n```toml\n[snowflake]\naccount          = \"\u003Corgname\u003E-\u003Caccount_name\u003E\"\nuser             = \"\u003Cyour_user\u003E\"\nprivate_key_path = \"../firehose-snowflake-auth/rsa_key.p8\"\nwarehouse        = \"ticketing_wh\"\ndatabase         = \"ticketing_db\"\nschema           = \"pipeline\"\nrole             = \"ACCOUNTADMIN\"\n```\n\n\u003E IMPORTANT: Never commit `secrets.toml`. It names your account and the path to your private key.\n\n### Start the Dashboard\n\nLaunch from inside the `presentation-layer` folder, so Streamlit finds `.streamlit/secrets.toml` and the relative key path resolves.\n\n```bash\ncd presentation-layer\nstreamlit run dashboard.py\n```\n\nYour browser opens at `localhost:8501`. You'll see 320 available seats and an empty metrics panel. Leave it open and the page refreshes itself. The seat map re-queries every 10 seconds, and the metrics every 30 seconds. The refresh interval controls how often the page asks, not how fresh the data is. The metrics can only change when the Gold table refreshes. The dashboard shows timestamps in US Central time.\n\n\u003C!-- VIDEO: add the YouTube link of the live demo here once published, using the embed syntax the Snowflake guides use. --\u003E\n\n\u003E NOTE: The video above is a recording of a complete live run. The architecture diagram shown in it is an earlier version. Where it differs from this guide, such as the phase names or the refresh rates, the guide is current.\n\n\u003C!-- ------------------------ --\u003E\n## Run the Producer\n\nThe producer shuffles all 320 seats like a deck of cards, then sells them in bursts. It sends each purchase to Kinesis with the seat ID as the partition key. It reads nothing from Snowflake.\n\n### Wake the Pipeline\n\nIn Snowflake, run `resume_demo()` so the Task and Dynamic Tables are active:\n\n```sql\nCALL ticketing_db.pipeline.resume_demo();\n```\n\n\u003E NOTE: The first file Snowpipe sees after a quiet period can take 25 to 30 seconds to load. If you want a smooth first run, send a small throwaway batch a minute ahead, then run `reset_demo()` and `resume_demo()` again.\n\n### Start Selling\n\nOpen a second terminal, activate the same virtual environment, and run the producer from the `ticket-generator` folder. It uses the AWS credentials from `aws configure`, which need permission to put records to `ticketing-stream`.\n\n```bash\ncd ticket-generator\npython producer.py\n```\n\nThe producer announces each phase as it enters it. It simulates three waves of demand:\n\n| Phase | Tickets sold | Burst size | Pause between bursts |\n|---|---|---|---|\n| INITIAL RUSH | first 25% | 18 to 24 seats | 4 to 6 seconds |\n| SLOWDOWN PHASE | 25% to 75% | 6 to 12 seats | 10 to 14 seconds |\n| FINAL RUSH | last 25% | 18 to 24 seats | 3 to 5 seconds |\n\nA short random delay of 0.05 to 0.15 seconds separates the individual sales inside a burst. A full run takes a few minutes. Watch the seat map turn from green to gray as the Task merges each batch into `seats`. The KPI cards and charts fill in about a minute or two behind.\n\n![Dashboard seat map partway through a run, with 81 of 320 seats sold and the rest still gray](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_mid_run.png?v=34db7abb)\n\n![Dashboard metrics partway through a run, showing total revenue, sell-out percentage, revenue by section and sales by channel](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_mid_run_metrics.png?v=34db7abb)\n\n![Dashboard seat map at 320 of 320 seats sold, with every seat gray](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_sold_out.png?v=34db7abb)\n\n![Dashboard metrics at sell-out, showing $27,043.75 total revenue, 100 percent sell-out and the final channel split](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/real-time-ticketing-pipeline-aws-snowflake/dashboard_sold_out_metrics.png?v=34db7abb)\n\n\u003C!-- ------------------------ --\u003E\n## Verify the Pipeline\n\nIf something looks off, check each component separately. Each block isolates one part of the pipeline.\n\n### Check the Fast Path\n\nRows land in `bronze_purchases_rt` first. A few seconds later the Task flips the matching seats.\n\n```sql\nSELECT COUNT(*) FROM ticketing_db.pipeline.bronze_purchases_rt;                    -- grows during the run\n\nSELECT status, COUNT(*) FROM ticketing_db.pipeline.seats GROUP BY status;          -- SOLD count rises\n\nSELECT * FROM ticketing_db.pipeline.seats\nWHERE status = 'SOLD' ORDER BY sold_at DESC LIMIT 10;                             -- recent sold_at values\n\nSELECT COUNT(*) FROM ticketing_db.pipeline.bronze_purchases_rt_stream;            -- near zero when the Task is caught up\n```\n\nWhen a run finishes, the number of rows in `bronze_purchases_rt` should equal the number of `SOLD` seats. A mismatch means the Task fell behind or dropped rows.\n\n### Check the Batch Path\n\nRows land in `bronze_purchases` once Snowpipe has loaded the files. Silver and Gold follow within their 60-second target lag.\n\n```sql\nSELECT COUNT(*) FROM ticketing_db.pipeline.bronze_purchases;\nSELECT COUNT(*) FROM ticketing_db.pipeline.silver_purchases;\nSELECT * FROM ticketing_db.pipeline.gold_section_metrics ORDER BY section, channel LIMIT 10;\n\nSELECT * FROM TABLE(INFORMATION_SCHEMA.COPY_HISTORY(\n  TABLE_NAME =\u003E 'BRONZE_PURCHASES',\n  START_TIME =\u003E DATEADD(HOUR, -1, CURRENT_TIMESTAMP())));\n\nSHOW DYNAMIC TABLES IN SCHEMA ticketing_db.pipeline;\n```\n\nIf Gold looks empty or stale, check `SHOW DYNAMIC TABLES` to confirm neither table is suspended, then wait out the 60-second lag. You can also force a refresh with `ALTER DYNAMIC TABLE ticketing_db.pipeline.gold_section_metrics REFRESH;`.\n\n### Troubleshooting\n\n| Symptom | Likely cause | Fix |\n|---|---|---|\n| `bronze_purchases_rt` stays empty | The table has a column with a `DEFAULT`, or the key or account settings are wrong | Check the backup location in S3 (`rt-failed/`) for the exact error message |\n| `bronze_purchases` stays empty | Stuck SNS subscription, missing newline delimiter, or a bucket policy problem | See the notes in **Build the Batch Path** |\n| Task shows `suspended` | A reset was run without resuming, or repeated failures | Run `resume_demo()` and check `TASK_HISTORY` |\n| Seats stay `AVAILABLE` but the Task shows `SUCCEEDED` | The `seat_id` format differs between the producer and the `seats` table, so the `MERGE` matches nothing | Compare `SELECT DISTINCT seat_id` in both tables |\n| Dashboard cannot connect | The account value in `secrets.toml` is wrong or the key is not registered | Re-check the account identifier and run `DESC USER` |\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nWhen you finish, remove the resources so nothing keeps billing.\n\n1. Stop the producer and the dashboard with `Ctrl+C` in each terminal.\n2. In Snowflake, drop everything this guide created. Dropping the database also removes the pipe, the Task, the Stream, the Dynamic Tables, and the procedures.\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nALTER TASK IF EXISTS ticketing_db.pipeline.seat_status_update_task SUSPEND;\nDROP DATABASE IF EXISTS ticketing_db;\nDROP STORAGE INTEGRATION IF EXISTS ticketing_s3_int;\nDROP WAREHOUSE IF EXISTS ticketing_wh;\nALTER USER \u003Cyour_user\u003E UNSET RSA_PUBLIC_KEY;\n```\n\n3. In the AWS console, delete these resources:\n   - Both Firehose delivery streams, `ticketing-firehose` and `ticketing-firehose-snow`\n   - The Kinesis stream `ticketing-stream`\n   - The SNS topic `ticketing-sns-topic`\n   - The S3 bucket. Empty it first, then delete it\n   - The IAM role `snowflake-ticketing-s3-role` and the policy `snowflake-ticketing-s3-policy`\n   - The IAM roles the console created for the two Firehose streams\n4. Delete the local `ticketing-pipeline` folder, including `firehose-snowflake-auth/rsa_key.p8`.\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nYou built a pipeline where a single Kinesis stream serves two very different workloads. Firehose's native Snowflake destination fed a Hybrid Table that a Stream and a Task kept current, so a seat map could flip within seconds of a purchase. In parallel, S3, SNS, and Snowpipe fed Dynamic Tables that kept revenue metrics current with no orchestration. Two stored procedures let you suspend the whole pipeline between runs to save cost.\n\n### What You Learned\n- One Kinesis stream can feed several Firehose delivery streams at once, each with a different destination\n- The Firehose native Snowflake destination writes through Snowpipe Streaming, and it rejects any table with a `DEFAULT` column\n- A Stream, a Task, and a `MERGE` keep a Hybrid Table current with row-level updates\n- Snowpipe with an SNS topic auto-ingests files from S3, and Snowflake's AWS identity comes from a storage integration\n- Dynamic Tables build Silver and Gold layers declaratively, with a 60-second minimum target lag\n- Stored procedures can suspend and resume a Task and Dynamic Tables to control cost\n\n### Related Resources\n- [Snowpipe Streaming overview](https://docs.snowflake.com/en/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview)\n- [Auto-ingest Snowpipe from Amazon S3](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-auto-s3)\n- [Getting started with Hybrid Tables](https://docs.snowflake.com/en/user-guide/tutorials/getting-started-with-hybrid-tables-tutorial)\n- [Dynamic Tables best practices](https://docs.snowflake.com/en/user-guide/dynamic-tables/best-practices)\n- [Key-pair authentication](https://docs.snowflake.com/en/user-guide/key-pair-auth)\n- [Amazon Data Firehose documentation](https://docs.aws.amazon.com/firehose/)\n- [AWS prescriptive guidance: automate data stream ingestion into Snowflake](https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/automate-data-stream-ingestion-into-a-snowflake-database-by-using-snowflake-snowpipe-amazon-s3-amazon-sns-and-amazon-data-firehose.html)\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-1f421c429a","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-8111570af2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-18290c5255","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-11-09",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-6b4e63accd","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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