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The dataset is a randomly generated dataset one would find from ski resorts. It generates Resort Tickets, Lift Rides, and Season Passes in Python and enqueues the data in a SQLite database. The streamer component reads data from the database and sends it to Snowflake and cleans up the database from data sent.\u003C/p\u003E\n","\u003Cp\u003ETo have fast and efficient near real-time reporting, we will use Dynamic Tables to materialize reports which are then queried from a Streamlit application deployed in the account.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPrivileges necessary to create a service user, database, and warehouse in Snowflake\u003C/li\u003E\u003Cli\u003EAccess to run SQL in the Snowflake console or SnowSQL\u003C/li\u003E\u003Cli\u003EBasic experience using git, GitHub, and Codespaces\u003C/li\u003E\u003Cli\u003EIntermediate knowledge of Python and SQL\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to send data from Python to Snowflake Streaming v2 REST API\u003C/li\u003E\u003Cli\u003EHow to prepare data for reporting using Dynamic Tables\u003C/li\u003E\u003Cli\u003EHow to create a basic Streamlit application for reporting\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://snowflake.com\"\u003ESnowflake\u003C/a\u003E Account in an AWS commercial region\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/\"\u003EGitHub\u003C/a\u003E Account with credits for Codespaces\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EStreaming pipeline to do near real-time reporting\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ELaunch the Codespace in GitHub\u003C/h2\u003E\n","\u003Cp\u003ENavigate to the \u003Ca href=\"https://github.com/Snowflake-Labs/Summit2025-DE214\"\u003Ecode repository\u003C/a\u003E in GitHub.\u003C/p\u003E\n","\u003Cp\u003EClick on the green Code Button, go to the Codespaces tab, and click the green Create codespace on main. You must be logged into GitHub to see the Codespaces tab.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreating the Service User & Role\u003C/h2\u003E\n","\u003Cp\u003ETo send data to Snowflake, the client must have a Service User's credentials. We will use key-pair authentication in this guide to authenticate to Snowflake and create a custom role with minimal privileges.\u003C/p\u003E\n","\u003Cp\u003ETo generate the keypair run the following commands in the terminal in the codespace.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eopenssl 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","\u003Cp\u003ECOPY the contents of the public key in rsa_key.pub from codespaces to the clipboard.\u003C/p\u003E\n","\u003Cp\u003ELogin to Snowsight or use SnowSQL to execute the following commands replacing \u003Ccode\u003E===YOUR_PUBLIC_KEY_HERE===\u003C/code\u003E with the key copied previously:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nUSE ROLE ACCOUNTADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS STREAMING_INGEST;\nCREATE ROLE IF NOT EXISTS STREAMING_INGEST;\nCREATE USER STREAMING_INGEST LOGIN_NAME='STREAMING_INGEST' DEFAULT_WAREHOUSE='STREAMING_INGEST', DEFAULT_NAMESPACE='STREAMING_INGEST.STREAMING_INGEST', DEFAULT_ROLE='STREAMING_INGEST', TYPE=SERVICE, RSA_PUBLIC_KEY='===YOUR_PUBLIC_KEY_HERE===';\nGRANT ROLE STREAMING_INGEST TO USER STREAMING_INGEST;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE STREAMING_INGEST TO USER IDENTIFIER($USERNAME);\n\nGRANT USAGE ON WAREHOUSE STREAMING_INGEST TO ROLE STREAMING_INGEST;\nGRANT OPERATE ON WAREHOUSE STREAMING_INGEST TO ROLE STREAMING_INGEST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreating the Database and Schema for data\u003C/h2\u003E\n","\u003Cp\u003EThis step will create the database and the schema where all the data is landed. This database will also store the notebook which sets up the data pipeline, the streamlit which displays the reports, and all tasks and dynamic tables created for this guide.\u003C/p\u003E\n","\u003Cp\u003ELogin to Snowsight or use SnowSQL to execute the following commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS STREAMING_INGEST;\nUSE DATABASE STREAMING_INGEST;\nALTER DATABASE STREAMING_INGEST SET USER_TASK_MINIMUM_TRIGGER_INTERVAL_IN_SECONDS=10;\nCREATE SCHEMA IF NOT EXISTS STREAMING_INGEST;\nUSE SCHEMA STREAMING_INGEST;\nGRANT OWNERSHIP ON DATABASE STREAMING_INGEST TO ROLE STREAMING_INGEST;\nGRANT OWNERSHIP ON SCHEMA STREAMING_INGEST.STREAMING_INGEST TO ROLE STREAMING_INGEST;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE STREAMING_INGEST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ECreating the Tables and Pipes needed for data\u003C/h2\u003E\n","\u003Cp\u003EThis step creates the pipes which are needed to accept data from the clients and the tables which store the data from the pipes.\u003C/p\u003E\n","\u003Cp\u003ELogin to Snowsight or use SnowSQL to execute the following commands:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE STREAMING_INGEST;\nUSE DATABASE STREAMING_INGEST;\nUSE SCHEMA STREAMING_INGEST;\n\nCREATE OR REPLACE TABLE RESORT_TICKET(TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, PRICE_USD DECIMAL(7,2), EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE PIPE RESORT_TICKET_PIPE AS\nCOPY INTO RESORT_TICKET\nFROM TABLE (\n      DATA_SOURCE (\n      TYPE => 'STREAMING'\n  )\n)\nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n\nCREATE OR REPLACE TABLE SEASON_PASS(TXID varchar(255), RFID varchar(255), PURCHASE_TIME datetime, PRICE_USD DECIMAL(7,2), EXPIRATION_TIME date, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE PIPE SEASON_PASS_PIPE AS\nCOPY INTO SEASON_PASS\nFROM TABLE (\n      DATA_SOURCE (\n      TYPE => 'STREAMING'\n  )\n)\nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n\nCREATE OR REPLACE TABLE LIFT_RIDE(TXID varchar(255), RFID varchar(255), RESORT varchar(255), LIFT varchar(255), RIDE_TIME datetime, ACTIVATION_DAY_COUNT integer);\n\nCREATE OR REPLACE PIPE LIFT_RIDE_PIPE AS\nCOPY INTO LIFT_RIDE\nFROM TABLE (\n      DATA_SOURCE (\n      TYPE => 'STREAMING'\n  )\n)\nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EWrite Streaming Application\u003C/h2\u003E\n","\u003Cp\u003ETo authenticate to Snowflake, you will need to setup the environment with credentials to your account.\u003C/p\u003E\n","\u003Cp\u003EThis will all be done in the codespace created previously.\u003C/p\u003E\n","\u003Cp\u003EMake a copy of the env file to edit by running this command in the codespace terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecp .env.example .env\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEdit the account name in the .env file (SNOWFLAKE_ACCOUNT) to match your Snowflake account name.\u003C/p\u003E\n","\u003Cp\u003EIf you do not know your account name, you can run this sql in Snowsight or via snowsql.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect current_account();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPaste in your private key from the rsa_key.p8 file into the .env file (PRIVATE_KEY).\u003C/p\u003E\n","\u003Cp\u003ESet the appropriate value for the host where your Snowflake endpoint resides (SNOWFLAKE_HOST). You can get the Account/Server URL from the Account Details in Snowsight.\u003C/p\u003E\n","\u003Ch3\u003EStream the Data to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EThis repository will generate sample data and supplies the framework and dependencies needed for you to stream data to Snowflake. You need to write the code to stream data to Snowflake.\u003C/p\u003E\n","\u003Cp\u003EYou will write the main body of the function stream_data in streamer.py. The pipe_name and 2 data access functions are passed to this function. All configuration parameters needed are in the streamer.py under \u003Ccode\u003E# parameters\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003Efn_get_data takes 2 parameters, the first parameter is the offset to read data from ex: (SELECT * where > offset) and the second parameter is the maximum number of records to read. It will return a list of json strings which can be sent to Snowflake.\u003C/p\u003E\n","\u003Cp\u003EExample usage:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erows = fn_get_data(latest_committed_offset_token, BATCH_SIZE)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Efn_delete_data takes 1 parameter: the offset to delete data up to and including ex: (DELETE * where offset <= offset).\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efn_delete_data(current_committed_offset_token)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe first thing the stream_data fn should do is to create a SnowflakeStreamingIngestClient. This client will allow you to operate on Channels which are needed to send data to Snowflake.\u003C/p\u003E\n","\u003Cp\u003ETo create a SnowflakeStreamingIngestClient, you will need to pass it the channel name and kwargs: account, user, database, schema, private_key, ROWSET_DEV_VM_TEST_MODE.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eclient = SnowflakeStreamingIngestClient(client_name, account=account_name, host=host_name, user=user_name, database=database_name, schema=schema_name, private_key=private_key, ROWSET_DEV_VM_TEST_MODE="false")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis client can be used to open a channel you will need to send data. client.open_channel function takes the channel_name, database_name, schema_name, and the pipe_name as arguments.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Echannel = client.open_channel(channel_name, database_name, schema_name, pipe_name)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo know where this process left off on last run (or if this is the first run) you can pull the current committed offset. This is available by calling channel.get_latest_committed_offset_token()\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Elatest_committed_offset_token = channel.get_latest_committed_offset_token()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf this returns None, there has been no data sent to Snowflake, otherwise it will be the latest offset sent.\u003C/p\u003E\n","\u003Cp\u003EThe channel should be long lived, so there should be an event loop grabbing data. Data can be pulled using fn_get_data from the that offset, or 0 if this is the first data.\u003C/p\u003E\n","\u003Cp\u003EData is returned from the fn_get_data in records as: (int id, string data), but the append_row function expects a single object. This can easily be converted using json.loads from the data in each row returned from fn_get_data. You can use the id as the last offset in the append_row function to set the correct offset token.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efor row in rows:\n    channel.append_row(json.loads(row[1]), str(row[0]))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn order to cleanup you will also want to occasionally delete the local data from the committed offset (retrieved from Snowflake). You can use the fn_delete_data function to do so. This should also be done in the event loop.\u003C/p\u003E\n","\u003Ch3\u003ETest the Streaming Application\u003C/h3\u003E\n","\u003Cp\u003EIn the codespace, build and start the docker container.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker compose build\ndocker compose up\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Data is Streaming\u003C/h3\u003E\n","\u003Cp\u003ERun the following sql to verify data is arriving in your account.\u003C/p\u003E\n","\u003Cp\u003EVerify Season Passes are being streamed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from SEASON_PASS limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify Resort Tickets are being streamed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from RESORT_TICKET limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify Lift Rides are being streamed:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * from LIFT_RIDE limit 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EImport the Notebook\u003C/h2\u003E\n","\u003Cp\u003EWe have created a notebook you can use to get started building the streaming data pipeline.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/Summit2025-DE214/raw/refs/heads/main/transformation_notebook.ipynb\"\u003EDownload\u003C/a\u003E the Notebook from Github.\u003C/p\u003E\n","\u003Cp\u003ELogin to Snowsight, click on the bottom left to get the Navigation Menu and Switch Role to STREAMING_INGEST.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/SwitchRole.png?v=7253236b\" alt=\"Switch Role\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the +, Notebook, and Import .ipynb File.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportNotebook.png?v=7253236b\" alt=\"Import .ipynb\"\u003E\u003C/p\u003E\n","\u003Cp\u003EName the notebook transformation_notebook, select the db STREAMING_INGEST and the schema STREAMING_INGEST.\u003C/p\u003E\n","\u003Cp\u003ESelect Run on warehouse and use the query warehouse STREAMING_INGEST and notebook warehouse STREAMING_INGEST.\u003C/p\u003E\n","\u003Cp\u003EThis will run everything on one warehouse to keep it as efficient as possible.\u003C/p\u003E\n","\u003Cp\u003EClick Create.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/CreateNotebook.png?v=7253236b\" alt=\"Create Notebook\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAdd the Snowflake.Core package which is required by this notebook.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportSnowflake.Core.png?v=7253236b\" alt=\"Create Notebook\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFollow the Notebook cells to build the data pipeline objects.\u003C/p\u003E\n","\u003Cp\u003EAfter complete, you will have a data pipeline built on the streaming data using: views, dynamic tables, and triggered tasks.\u003C/p\u003E\n","\u003Ch2\u003ECreate the Streamlit Application\u003C/h2\u003E\n","\u003Cp\u003EA Streamlit Application will be created to demonstrate how the data prepared previously could be leveraged in an analytic dashboard inside your organization.\u003C/p\u003E\n","\u003Cp\u003ETo create a new Streamlit Application Click on +, Streamlit App, and New Streamlit App.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/CreateStreamlit.png?v=7253236b\" alt=\"Create Streamlit 1\"\u003E\u003C/p\u003E\n","\u003Cp\u003EChoose the App title STREAMING_INGEST, App location in STREAMING_INGEST database and STREAMING_INGEST schema, and run on the warehouse STREAMING_INGEST.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/CreateStreamlit2.png?v=7253236b\" alt=\"Create Streamlit 2\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAdd the Package plotly and pandas.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportPlotly.png?v=7253236b\" alt=\"Import Plotly\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportPandas.png?v=7253236b\" alt=\"Import Pandas\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOverwrite all the contents of the streamlit_app.py file in the editor with the \u003Ca href=\"https://raw.githubusercontent.com/Snowflake-Labs/Summit2025-DE214/refs/heads/main/streamlit_app.py\"\u003Eapplication code\u003C/a\u003E available in the Github repository.\u003C/p\u003E\n","\u003Cp\u003ERun the Streamlit to see the visualizations from the data pipeline built in this guide.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003ETo fully remove everything you did today you only need to drop some objects in your Snowflake account. From the Snowflake console or SnowSQL, as \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-SQL\"\u003EUSE ROLE STREAMING_INGEST;\nDROP DATABASE IF EXISTS STREAMING_INGEST;\n\nUSE ROLE ACCOUNTADMIN;\nDROP WAREHOUSE IF EXISTS STREAMING_INGEST;\nDROP USER IF EXISTS STREAMING_INGEST;\nDROP ROLE IF EXISTS STREAMING_INGEST;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Ch3\u003EWhat we covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreating a table and pipe to receive streaming data\u003C/li\u003E\u003Cli\u003ESending data to the Snowpipe Streaming API from Python\u003C/li\u003E\u003Cli\u003EUsing a Notebook to create a near real-time Data Pipeline leveraging Dynamic Tables\u003C/li\u003E\u003Cli\u003EQuerying the streaming data from a Notebook and Streamlit\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext steps\u003C/h3\u003E\n","\u003Cp\u003ESnowflake documentation and quickstarts will provide more information you will need to build a robust streaming data pipeline. Review these resources to learn more.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowpipe-streaming-high-performance-getting-started\"\u003ETutorial: Get started with Snowpipe Streaming high performance architecture SDK\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/dynamic-tables-intro\"\u003EDynamic Tables Introduction\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EQuickstart on \u003Ca href=\"/en/developers/guides/getting-started-with-dynamic-tables/\"\u003EDynamic Tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EQuickstart on \u003Ca href=\"/en/developers/guides/getting-started-with-snowpark-for-python-streamlit/\"\u003EStreamlit\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Build streaming data pipelines in Python with Rowset API, Snowflake Dynamic Tables, and Streamlit for real-time data ingestion.","title":"Build a Streaming Data Pipeline in Python",":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nSnowflake is a powerful platform to process streaming data and do near real-time reporting.\n\nIn this guide, we will use the High-Performance Python SDK to ingest data into Snowflake tables seconds from generation. The dataset is a randomly generated dataset one would find from ski resorts. It generates Resort Tickets, Lift Rides, and Season Passes in Python and enqueues the data in a SQLite database. The streamer component reads data from the database and sends it to Snowflake and cleans up the database from data sent.\n\nTo have fast and efficient near real-time reporting, we will use Dynamic Tables to materialize reports which are then queried from a Streamlit application deployed in the account.\n\n### Prerequisites\n\n- Privileges necessary to create a service user, database, and warehouse in Snowflake\n- Access to run SQL in the Snowflake console or SnowSQL\n- Basic experience using git, GitHub, and Codespaces\n- Intermediate knowledge of Python and SQL\n\n### What You’ll Learn\n\n- How to send data from Python to Snowflake Streaming v2 REST API\n- How to prepare data for reporting using Dynamic Tables\n- How to create a basic Streamlit application for reporting\n\n### What You’ll Need\n\n- [Snowflake](https://snowflake.com) Account in an AWS commercial region\n- [GitHub](https://github.com/) Account with credits for Codespaces\n\n### What You’ll Build\n\n- Streaming pipeline to do near real-time reporting\n\n## Launch the Codespace in GitHub\n\nNavigate to the [code repository](https://github.com/Snowflake-Labs/Summit2025-DE214) in GitHub.\n\nClick on the green Code Button, go to the Codespaces tab, and click the green Create codespace on main. You must be logged into GitHub to see the Codespaces tab.\n\n\u003C!-- ------------------------ --\u003E\n## Creating the Service User & Role\n\nTo send data to Snowflake, the client must have a Service User's credentials. We will use key-pair authentication in this guide to authenticate to Snowflake and create a custom role with minimal privileges.\n\nTo generate the keypair run the following commands in the terminal in the codespace.\n\n```bash\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\nCOPY the contents of the public key in rsa_key.pub from codespaces to the clipboard.\n\nLogin to Snowsight or use SnowSQL to execute the following commands replacing `===YOUR_PUBLIC_KEY_HERE===` with the key copied previously:\n\n```sql\n\nUSE ROLE ACCOUNTADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS STREAMING_INGEST;\nCREATE ROLE IF NOT EXISTS STREAMING_INGEST;\nCREATE USER STREAMING_INGEST LOGIN_NAME='STREAMING_INGEST' DEFAULT_WAREHOUSE='STREAMING_INGEST', DEFAULT_NAMESPACE='STREAMING_INGEST.STREAMING_INGEST', DEFAULT_ROLE='STREAMING_INGEST', TYPE=SERVICE, RSA_PUBLIC_KEY='===YOUR_PUBLIC_KEY_HERE===';\nGRANT ROLE STREAMING_INGEST TO USER STREAMING_INGEST;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE STREAMING_INGEST TO USER IDENTIFIER($USERNAME);\n\nGRANT USAGE ON WAREHOUSE STREAMING_INGEST TO ROLE STREAMING_INGEST;\nGRANT OPERATE ON WAREHOUSE STREAMING_INGEST TO ROLE STREAMING_INGEST;\n```\n\n## Creating the Database and Schema for data\n\nThis step will create the database and the schema where all the data is landed. This database will also store the notebook which sets up the data pipeline, the streamlit which displays the reports, and all tasks and dynamic tables created for this guide.\n\nLogin to Snowsight or use SnowSQL to execute the following commands:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nCREATE DATABASE IF NOT EXISTS STREAMING_INGEST;\nUSE DATABASE STREAMING_INGEST;\nALTER DATABASE STREAMING_INGEST SET USER_TASK_MINIMUM_TRIGGER_INTERVAL_IN_SECONDS=10;\nCREATE SCHEMA IF NOT EXISTS STREAMING_INGEST;\nUSE SCHEMA STREAMING_INGEST;\nGRANT OWNERSHIP ON DATABASE STREAMING_INGEST TO ROLE STREAMING_INGEST;\nGRANT OWNERSHIP ON SCHEMA STREAMING_INGEST.STREAMING_INGEST TO ROLE STREAMING_INGEST;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE STREAMING_INGEST;\n```\n\n## Creating the Tables and Pipes needed for data\n\nThis step creates the pipes which are needed to accept data from the clients and the tables which store the data from the pipes.\n\nLogin to Snowsight or use SnowSQL to execute the following commands:\n\n```sql\nUSE ROLE STREAMING_INGEST;\nUSE DATABASE STREAMING_INGEST;\nUSE SCHEMA STREAMING_INGEST;\n\nCREATE OR REPLACE TABLE RESORT_TICKET(TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, PRICE_USD DECIMAL(7,2), EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE PIPE RESORT_TICKET_PIPE AS\nCOPY INTO RESORT_TICKET\nFROM TABLE (\n      DATA_SOURCE (\n      TYPE =\u003E 'STREAMING'\n  )\n)\nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n\nCREATE OR REPLACE TABLE SEASON_PASS(TXID varchar(255), RFID varchar(255), PURCHASE_TIME datetime, PRICE_USD DECIMAL(7,2), EXPIRATION_TIME date, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE PIPE SEASON_PASS_PIPE AS\nCOPY INTO SEASON_PASS\nFROM TABLE (\n      DATA_SOURCE (\n      TYPE =\u003E 'STREAMING'\n  )\n)\nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n\nCREATE OR REPLACE TABLE LIFT_RIDE(TXID varchar(255), RFID varchar(255), RESORT varchar(255), LIFT varchar(255), RIDE_TIME datetime, ACTIVATION_DAY_COUNT integer);\n\nCREATE OR REPLACE PIPE LIFT_RIDE_PIPE AS\nCOPY INTO LIFT_RIDE\nFROM TABLE (\n      DATA_SOURCE (\n      TYPE =\u003E 'STREAMING'\n  )\n)\nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n```\n\n## Write Streaming Application\n\nTo authenticate to Snowflake, you will need to setup the environment with credentials to your account.\n\nThis will all be done in the codespace created previously.\n\nMake a copy of the env file to edit by running this command in the codespace terminal.\n\n```bash\ncp .env.example .env\n```\n\nEdit the account name in the .env file (SNOWFLAKE_ACCOUNT) to match your Snowflake account name.\n\nIf you do not know your account name, you can run this sql in Snowsight or via snowsql.\n\n```sql\nselect current_account();\n```\n\nPaste in your private key from the rsa_key.p8 file into the .env file (PRIVATE_KEY).\n\nSet the appropriate value for the host where your Snowflake endpoint resides (SNOWFLAKE_HOST). You can get the Account/Server URL from the Account Details in Snowsight.\n\n### Stream the Data to Snowflake\n\nThis repository will generate sample data and supplies the framework and dependencies needed for you to stream data to Snowflake. You need to write the code to stream data to Snowflake.\n\nYou will write the main body of the function stream_data in streamer.py. The pipe_name and 2 data access functions are passed to this function. All configuration parameters needed are in the streamer.py under `# parameters`.\n\nfn_get_data takes 2 parameters, the first parameter is the offset to read data from ex: (SELECT * where \u003E offset) and the second parameter is the maximum number of records to read. It will return a list of json strings which can be sent to Snowflake.\n\nExample usage: \n\n```python\nrows = fn_get_data(latest_committed_offset_token, BATCH_SIZE)\n```\n\nfn_delete_data takes 1 parameter: the offset to delete data up to and including ex: (DELETE * where offset \u003C= offset).\n\n```python\nfn_delete_data(current_committed_offset_token)\n```\n\nThe first thing the stream_data fn should do is to create a SnowflakeStreamingIngestClient. This client will allow you to operate on Channels which are needed to send data to Snowflake.\n\nTo create a SnowflakeStreamingIngestClient, you will need to pass it the channel name and kwargs: account, user, database, schema, private_key, ROWSET_DEV_VM_TEST_MODE.\n\n```python\nclient = SnowflakeStreamingIngestClient(client_name, account=account_name, host=host_name, user=user_name, database=database_name, schema=schema_name, private_key=private_key, ROWSET_DEV_VM_TEST_MODE=\"false\")\n```\n\nThis client can be used to open a channel you will need to send data. client.open_channel function takes the channel_name, database_name, schema_name, and the pipe_name as arguments.\n\n```python\nchannel = client.open_channel(channel_name, database_name, schema_name, pipe_name)\n```\n\nTo know where this process left off on last run (or if this is the first run) you can pull the current committed offset. This is available by calling channel.get_latest_committed_offset_token()\n\n```python\nlatest_committed_offset_token = channel.get_latest_committed_offset_token()\n```\n\nIf this returns None, there has been no data sent to Snowflake, otherwise it will be the latest offset sent.\n\nThe channel should be long lived, so there should be an event loop grabbing data. Data can be pulled using fn_get_data from the that offset, or 0 if this is the first data.\n\nData is returned from the fn_get_data in records as: (int id, string data), but the append_row function expects a single object. This can easily be converted using json.loads from the data in each row returned from fn_get_data. You can use the id as the last offset in the append_row function to set the correct offset token.\n\n```python\nfor row in rows:\n    channel.append_row(json.loads(row[1]), str(row[0]))\n```\n\nIn order to cleanup you will also want to occasionally delete the local data from the committed offset (retrieved from Snowflake). You can use the fn_delete_data function to do so. This should also be done in the event loop.\n\n### Test the Streaming Application\n\nIn the codespace, build and start the docker container.\n\n```bash\ndocker compose build\ndocker compose up\n```\n\n### Verify Data is Streaming\n\nRun the following sql to verify data is arriving in your account.\n\nVerify Season Passes are being streamed:\n\n```sql\nselect * from SEASON_PASS limit 10;\n```\n\nVerify Resort Tickets are being streamed:\n\n```sql\nselect * from RESORT_TICKET limit 10;\n```\n\nVerify Lift Rides are being streamed:\n\n```sql\nselect * from LIFT_RIDE limit 10;\n```\n\n## Import the Notebook\n\nWe have created a notebook you can use to get started building the streaming data pipeline.\n\n[Download](https://github.com/Snowflake-Labs/Summit2025-DE214/raw/refs/heads/main/transformation_notebook.ipynb) the Notebook from Github.\n\nLogin to Snowsight, click on the bottom left to get the Navigation Menu and Switch Role to STREAMING_INGEST.\n\n![Switch Role](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/SwitchRole.png?v=7253236b)\n\nClick on the +, Notebook, and Import .ipynb File.\n\n![Import .ipynb](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportNotebook.png?v=7253236b)\n\nName the notebook transformation_notebook, select the db STREAMING_INGEST and the schema STREAMING_INGEST.\n\nSelect Run on warehouse and use the query warehouse STREAMING_INGEST and notebook warehouse STREAMING_INGEST.\n\nThis will run everything on one warehouse to keep it as efficient as possible.\n\nClick Create.\n\n![Create Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/CreateNotebook.png?v=7253236b)\n\nAdd the Snowflake.Core package which is required by this notebook.\n\n![Create Notebook](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportSnowflake.Core.png?v=7253236b)\n\nFollow the Notebook cells to build the data pipeline objects.\n\nAfter complete, you will have a data pipeline built on the streaming data using: views, dynamic tables, and triggered tasks.\n\n## Create the Streamlit Application\n\nA Streamlit Application will be created to demonstrate how the data prepared previously could be leveraged in an analytic dashboard inside your organization.\n\nTo create a new Streamlit Application Click on +, Streamlit App, and New Streamlit App.\n\n![Create Streamlit 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/CreateStreamlit.png?v=7253236b)\n\nChoose the App title STREAMING_INGEST, App location in STREAMING_INGEST database and STREAMING_INGEST schema, and run on the warehouse STREAMING_INGEST.\n\n![Create Streamlit 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/CreateStreamlit2.png?v=7253236b)\n\nAdd the Package plotly and pandas.\n\n![Import Plotly](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportPlotly.png?v=7253236b)\n\n![Import Pandas](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-a-streaming-data-pipeline-in-python/ImportPandas.png?v=7253236b)\n\nOverwrite all the contents of the streamlit_app.py file in the editor with the [application code](https://raw.githubusercontent.com/Snowflake-Labs/Summit2025-DE214/refs/heads/main/streamlit_app.py) available in the Github repository.\n\nRun the Streamlit to see the visualizations from the data pipeline built in this guide.\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nTo fully remove everything you did today you only need to drop some objects in your Snowflake account. From the Snowflake console or SnowSQL, as `ACCOUNTADMIN` run:\n```SQL\nUSE ROLE STREAMING_INGEST;\nDROP DATABASE IF EXISTS STREAMING_INGEST;\n\nUSE ROLE ACCOUNTADMIN;\nDROP WAREHOUSE IF EXISTS STREAMING_INGEST;\nDROP USER IF EXISTS STREAMING_INGEST;\nDROP ROLE IF EXISTS STREAMING_INGEST;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion\n\n### What we covered\n- Creating a table and pipe to receive streaming data\n- Sending data to the Snowpipe Streaming API from Python\n- Using a Notebook to create a near real-time Data Pipeline leveraging Dynamic Tables\n- Querying the streaming data from a Notebook and Streamlit\n\n### Next steps\n\nSnowflake documentation and quickstarts will provide more information you will need to build a robust streaming data pipeline. Review these resources to learn more.\n\n- [Tutorial: Get started with Snowpipe Streaming high performance architecture SDK](https://docs.snowflake.com/en/user-guide/snowpipe-streaming-high-performance-getting-started)\n- [Dynamic Tables Introduction](https://docs.snowflake.com/en/user-guide/dynamic-tables-intro)\n- Quickstart on [Dynamic Tables](/en/developers/guides/getting-started-with-dynamic-tables/)\n- Quickstart on [Streamlit](/en/developers/guides/getting-started-with-snowpark-for-python-streamlit/)\n\n\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-4e3f2256c3","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-f1f9c83366",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ba5f108cc4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-02",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-44b4317ec2","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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the Codespace in GitHub\u003C/h2\u003E","\u003Ch2\u003ECreating the Service User & Role\u003C/h2\u003E","\u003Ch2\u003ECreating the Database and Schema for data\u003C/h2\u003E","\u003Ch2\u003ECreating the Tables and Pipes needed for data\u003C/h2\u003E","\u003Ch2\u003EWrite Streaming Application\u003C/h2\u003E","\u003Ch2\u003EImport the Notebook\u003C/h2\u003E","\u003Ch2\u003ECreate the Streamlit 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