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Guide: Ingesting Data into Snowflake","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThere are many different ways to get data into Snowflake. Different use cases, requirements, team skillsets, and technology choices all\ncontribute to making the right decision on how to ingest data. This quickstart will guide you through an example of the same data loaded with different methods:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESQL Inserts from the Python Connector\u003C/li\u003E\u003Cli\u003EFile Upload & Copy (Warehouse) from the Python Connector\u003C/li\u003E\u003Cli\u003EFile Upload & Copy (Snowpipe) using Python\u003C/li\u003E\u003Cli\u003EFile Upload & Copy (Serverless) from the Python Connector\u003C/li\u003E\u003Cli\u003EFrom Kafka - in Snowpipe (Batch) mode\u003C/li\u003E\u003Cli\u003EFrom Kafka - in Snowpipe Streaming mode\u003C/li\u003E\u003Cli\u003EFrom Java SDK - Using the Snowflake Ingest Service\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBy the end of this guide you should be familiar with many ways to load data, and be able to choose the right pattern for your goals and needs. Each method of ingest can be done separately and optionally as desired after going through the initial project setup and are not dependent on each other.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Account with the ability to create a User, Role, Database, Snowpipe, Serverless Task, Execute Task\u003C/li\u003E\u003Cli\u003EFamiliarity with Python, Kafka, and/or Java\u003C/li\u003E\u003Cli\u003EBasic knowledge of Docker\u003C/li\u003E\u003Cli\u003EAbility to run Docker locally or access to an environment to run Kafka and Kafka Connectors\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow and when to insert data using connectors\u003C/li\u003E\u003Cli\u003EHow and when to insert data from files\u003C/li\u003E\u003Cli\u003EHow to load data from Kafka\u003C/li\u003E\u003Cli\u003EHow to load data from a stream\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\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EMac Requirements\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.docker.com/desktop/install/mac-install/\"\u003EDocker\u003C/a\u003E Installed\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html\"\u003EConda\u003C/a\u003E Installed\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ELinux Requirements\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.docker.com/engine/install/ubuntu/\"\u003EDocker\u003C/a\u003E Installed\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html\"\u003EConda\u003C/a\u003E Installed\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWindows Requirements\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://learn.microsoft.com/en-us/windows/wsl/install\"\u003EWSL with Ubuntu\u003C/a\u003E for Windows\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.docker.com/engine/install/ubuntu/\"\u003EDocker\u003C/a\u003E Installed in Ubuntu\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html\"\u003EConda\u003C/a\u003E Installed in Ubuntu\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA project which can load data many different ways into your Snowflake account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EEnvironment Setup\u003C/h2\u003E\n","\u003Cp\u003EThis guide has a data generator and several examples which need Python 3.8, Java, and some other libraries and utilities.\u003C/p\u003E\n","\u003Cp\u003ETo set up these dependencies, we will use conda.\u003C/p\u003E\n","\u003Cp\u003ECreate a file named environment.yml with the following contents\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ename: sf-ingest-examples\nchannels:\n  - main\n  - conda-forge\n  - defaults\ndependencies:\n  - faker=28.4.1\n  - kafka-python=2.0.2\n  - maven=3.9.6\n  - openjdk=11.0.13\n  - pandas=1.5.3\n  - pip=23.0.1\n  - pyarrow=10.0.1\n  - python=3.9\n  - python-confluent-kafka\n  - python-dotenv=0.21.0\n  - python-rapidjson=1.5\n  - snowflake-connector-python=3.15.0\n  - snowflake-ingest=1.0.10\n  - pip:\n      - optional-faker==2.1.0\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo create the environment needed, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Econda env create -f environment.yml\nconda activate sf-ingest-examples\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAnytime you want to come back to this guide, you can reactivate this environment by running the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Econda activate sf-ingest-examples\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ETest Data Generation\u003C/h2\u003E\n","\u003Cp\u003EIt is nice to have real-world looking data for testing. This guide will generate fictitious lift tickets for patrons of ski resorts.\u003C/p\u003E\n","\u003Cp\u003EYou may have your own data you would like to generate, feel free to modify the data generator, the tables, and the code as you go to make it more applicable your use cases.\u003C/p\u003E\n","\u003Cp\u003EMost of the ingest patterns we will go through in this guide will actually outperform the faker library so it is best to run the data generation once and reuse that generated data in the different ingest patterns.\u003C/p\u003E\n","\u003Cp\u003ECreate a director on your computer for this project and add a file called data_generator.py. This code will take the number of tickets to create as an arg and output the json data with one lift ticket (record) per line. The rest of the files in this guide can be put in this same directory.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport sys\nimport rapidjson as json\nimport optional_faker as _\nimport uuid\nimport random\n\nfrom dotenv import load_dotenv\nfrom faker import Faker\nfrom datetime import date, datetime\n\nload_dotenv()\nfake = Faker()\nresorts = ["Vail", "Beaver Creek", "Breckenridge", "Keystone", "Crested Butte", "Park City", "Heavenly", "Northstar",\n           "Kirkwood", "Whistler Blackcomb", "Perisher", "Falls Creek", "Hotham", "Stowe", "Mount Snow", "Okemo",\n           "Hunter Mountain", "Mount Sunapee", "Attitash", "Wildcat", "Crotched", "Stevens Pass", "Liberty", "Roundtop", \n           "Whitetail", "Jack Frost", "Big Boulder", "Alpine Valley", "Boston Mills", "Brandywine", "Mad River",\n           "Hidden Valley", "Snow Creek", "Wilmot", "Afton Alps" , "Mt. Brighton", "Paoli Peaks"]    \n\n\ndef print_lift_ticket():\n    global resorts, fake\n    state = fake.state_abbr()\n    lift_ticket = {'txid': str(uuid.uuid4()),\n                   'rfid': hex(random.getrandbits(96)),\n                   'resort': fake.random_element(elements=resorts),\n                   'purchase_time': datetime.utcnow().isoformat(),\n                   'expiration_time': date(2023, 6, 1).isoformat(),\n                   'days': fake.random_int(min=1, max=7),\n                   'name': fake.name(),\n                   'address': fake.none_or({'street_address': fake.street_address(), \n                                             'city': fake.city(), 'state': state, \n                                             'postalcode': fake.postalcode_in_state(state)}),\n                   'phone': fake.none_or(fake.phone_number()),\n                   'email': fake.none_or(fake.email()),\n                   'emergency_contact' : fake.none_or({'name': fake.name(), 'phone': fake.phone_number()}),\n    }\n    d = json.dumps(lift_ticket) + '\\n'\n    sys.stdout.write(d)\n\n\nif __name__ == "__main__":\n    args = sys.argv[1:]\n    total_count = int(args[0])\n    for _ in range(total_count):\n        print_lift_ticket()\n    print('')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo test this generator, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython ./data_generator.py 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see 1 record written to output.\u003C/p\u003E\n","\u003Cp\u003EIn order to quickly have data available for the rest of the guide, dump a lot of data to a file for re-use.\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython ./data_generator.py 100000 | gzip > data.json.gz\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can increase or decrease the size of records to any number that you would like to use. This will currently output the sample data to your current directory, but you can pick any folder you would like. This file will be used in subsequent steps so note where you stored this data and replace later if needed.\u003C/p\u003E\n","\u003Ch2\u003EDatabase Setup\u003C/h2\u003E\n","\u003Cp\u003EKafka and the Snowpipe API both require the use of key pair authentication. Due to this, I will use keypair for all the ingest solutions so they are all in common.\u003C/p\u003E\n","\u003Cp\u003ECreate a database, schema, warehouse, role, and user called INGEST in your Snowflake account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ACCOUNTADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS INGEST;\nCREATE ROLE IF NOT EXISTS INGEST;\nGRANT USAGE ON WAREHOUSE INGEST TO ROLE INGEST;\nGRANT OPERATE ON WAREHOUSE INGEST TO ROLE INGEST;\nCREATE DATABASE IF NOT EXISTS INGEST;\nUSE DATABASE INGEST;\nCREATE SCHEMA IF NOT EXISTS INGEST;\nUSE SCHEMA INGEST;\nGRANT OWNERSHIP ON DATABASE INGEST TO ROLE INGEST;\nGRANT OWNERSHIP ON SCHEMA INGEST.INGEST TO ROLE INGEST;\n\nCREATE USER INGEST PASSWORD='<REDACTED>' LOGIN_NAME='INGEST' MUST_CHANGE_PASSWORD=FALSE, DISABLED=FALSE, DEFAULT_WAREHOUSE='INGEST', DEFAULT_NAMESPACE='INGEST.INGEST', DEFAULT_ROLE='INGEST';\nGRANT ROLE INGEST TO USER INGEST;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE INGEST TO USER IDENTIFIER($USERNAME);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo generate a key pair for the INGEST user, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eopenssl genrsa 4096 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\necho "ALTER USER INGEST SET RSA_PUBLIC_KEY='`cat ./rsa_key.pub`';"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ERun the sql from the output to set the RSA_PUBLIC_KEY for the INGEST user.\u003C/p\u003E\n","\u003Cp\u003ETo get the private key for this user run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003EPRVK=`cat ./rsa_key.p8 | grep -v KEY- | tr -d '\\012'`\necho "PRIVATE_KEY=$PRVK"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAdd these variables to a new .env file in your project:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESNOWFLAKE_ACCOUNT=<ACCOUNT_HERE>\nSNOWFLAKE_USER=INGEST\nPRIVATE_KEY=<PRIVATE_KEY_HERE>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EMake sure you protect your .env and .p8 file as those are credentials directly to the INGEST user.\u003C/p\u003E\n","\u003Ch2\u003ESQL Inserts from the Python Connector\u003C/h2\u003E\n","\u003Cp\u003ESnowflake has a \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/python-connector/python-connector\"\u003EPython connector\u003C/a\u003E which is an easy way to run sql and upload files. One way to get data in would be to do an SQL INSERT statement for each record. While this is a convenient way to insert data, it is not efficient as Snowflake is an OLAP engine and is optimized around writing large batches of data.\u003C/p\u003E\n","\u003Cp\u003ECreate a table in Snowflake called LIFT_TICKETS_PY_INSERT to recieve this data from the INGEST user.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_INSERT (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a file named py_insert.py. This code will read a line from standard input and insert that record into Snowflake using a SQL INSERT. Change the table names/fields as needed to support your use case.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os, sys, logging\nimport json\nimport snowflake.connector\n\nfrom dotenv import load_dotenv\nfrom cryptography.hazmat.primitives import serialization\n\nload_dotenv()\nlogging.basicConfig(level=logging.WARN)\nsnowflake.connector.paramstyle='qmark'\n\n\ndef connect_snow():\n    private_key = "-----BEGIN PRIVATE KEY-----\\n" + os.getenv("PRIVATE_KEY") + "\\n-----END PRIVATE KEY-----\\n)"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv("SNOWFLAKE_ACCOUNT"),\n        user=os.getenv("SNOWFLAKE_USER"),\n        private_key=pkb,\n        role="INGEST",\n        database="INGEST",\n        schema="INGEST",\n        warehouse="INGEST",\n        session_parameters={'QUERY_TAG': 'py-insert'}, \n    )\n\n\ndef save_to_snowflake(snow, message):\n    record = json.loads(message)\n    logging.debug('inserting record to db')\n    row = (record['txid'],record['rfid'],record["resort"],record["purchase_time"],record["expiration_time"],record['days'],record['name'],json.dumps(record['address']),record['phone'],record['email'],json.dumps(record['emergency_contact']))\n    # this dataset has variant records, so utilizing an executemany() is not possible, must insert 1 record at a time\n    snow.cursor().execute("INSERT INTO LIFT_TICKETS_PY_INSERT (\\"TXID\\",\\"RFID\\",\\"RESORT\\",\\"PURCHASE_TIME\\", \\"EXPIRATION_TIME\\",\\"DAYS\\",\\"NAME\\",\\"ADDRESS\\",\\"PHONE\\",\\"EMAIL\\",\\"EMERGENCY_CONTACT\\") SELECT ?,?,?,?,?,?,?,PARSE_JSON(?),?,?,PARSE_JSON(?)", row)\n    logging.debug(f"inserted ticket {record}")\n\n\nif __name__ == "__main__":    \n    snow = connect_snow()\n    for message in sys.stdin:\n        if message != '\\n':\n            save_to_snowflake(snow, message)\n        else:\n            break\n    snow.close()\n    logging.info("Ingest complete")\n    \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn order to test this insert, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython ./data_generator.py 1 | python py_insert.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the table to verify the data was inserted.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_PY_INSERT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWARNING, this is not a good way to load data and will take a long time. I don't really want you to have to wait for hours to load your example dataset, so lets just load 1000 records.\u003C/p\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecat data.json.gz | zcat | head -n 1000 | python py_insert.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFeel free to take a break and come back in a few minutes.\u003C/p\u003E\n","\u003Cp\u003EDo you think this could be faster if we parallelized the work?\u003C/p\u003E\n","\u003Cp\u003ETo verify if that is true or not, run 10 of these same loads in multiple terminals and see how long it takes.\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecat data.json.gz | zcat | head -n 100 | python py_insert.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe total time loading in parallel will be the same or worse than before. This is not a good pattern to get to high throughputs of records.\u003C/p\u003E\n","\u003Ch3\u003ETips\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIngest is billed based on warehouse credits consumed while online.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe connectors support multi-inserts but data containing a variant field cannot be formatted into a multi-insert.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUsing inserts and multi-inserts will not efficiently use warehouse resources (optimal at 100MB or more with some concurrency). It is better to upload data and COPY into the table.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EConnectors will switch to creating and uploading a file and doing a COPY into when large batches are set. This is not configurable.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EMany assume adding significant concurrency will support higher throughputs of data. The additional concurrent INSERTS will be blocked by other INSERTS, more frequently when small payloads are inserted. You need to move to bigger batches to get more througput.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EReview query history to see what the connector is doing.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn cases where the connector has enough data in the executemany to create a well sized file for COPY and does so, this does become as efficient as the following methods.\u003C/p\u003E\n","\u003Cp\u003EThe example above could not use executemany as it had VARIANT data.\u003C/p\u003E\n","\u003Cp\u003EThe next methods will show how to batch into better sized blocks of work which will drive higher throughputs and higher efficiency on Snowflake.\u003C/p\u003E\n","\u003Ch2\u003EFile Uploads\u003C/h2\u003E\n","\u003Ch3\u003EFile Upload & Copy (Warehouse) from the Python Connector\u003C/h3\u003E\n","\u003Cp\u003ETo get to better sized batches, the client can upload a file and have a warehouse copy the data into the destination. The Python connector can execute the COPY after uploading the file.\u003C/p\u003E\n","\u003Cp\u003ECreate the table which will be used for landing the data, changing as needed for your use case.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_COPY_INTO (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a file named py_copy_into.py with the following contents. You will need to change this code if you changed the data generator.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os, sys, logging\nimport json\nimport uuid\nimport snowflake.connector\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport tempfile\n\nfrom dotenv import load_dotenv\nfrom cryptography.hazmat.primitives import serialization\n\nload_dotenv()\nlogging.basicConfig(level=logging.WARN)\n\n\ndef connect_snow():\n    private_key = "-----BEGIN PRIVATE KEY-----\\n" + os.getenv("PRIVATE_KEY") + "\\n-----END PRIVATE KEY-----\\n)"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv("SNOWFLAKE_ACCOUNT"),\n        user=os.getenv("SNOWFLAKE_USER"),\n        private_key=pkb,\n        role="INGEST",\n        database="INGEST",\n        schema="INGEST",\n        warehouse="INGEST",\n        session_parameters={'QUERY_TAG': 'py-copy-into'}, \n    )\n\n\ndef save_to_snowflake(snow, batch, temp_dir):\n    logging.debug("inserting batch to db")\n    pandas_df = pd.DataFrame(\n        batch,\n        columns=[\n            "TXID",\n            "RFID",\n            "RESORT",\n            "PURCHASE_TIME",\n            "EXPIRATION_TIME",\n            "DAYS",\n            "NAME",\n            "ADDRESS",\n            "PHONE",\n            "EMAIL",\n            "EMERGENCY_CONTACT",\n        ],\n    )\n    arrow_table = pa.Table.from_pandas(pandas_df)\n    out_path = f"{temp_dir.name}/{str(uuid.uuid1())}.parquet"\n    pq.write_table(arrow_table, out_path, use_dictionary=False, compression="SNAPPY")\n    snow.cursor().execute(\n        "PUT 'file://{0}' @%LIFT_TICKETS_PY_COPY_INTO".format(out_path)\n    )\n    os.unlink(out_path)\n    snow.cursor().execute(\n        "COPY INTO LIFT_TICKETS_PY_COPY_INTO FILE_FORMAT=(TYPE='PARQUET') MATCH_BY_COLUMN_NAME=CASE_SENSITIVE PURGE=TRUE"\n    )\n    logging.debug(f"inserted {len(batch)} tickets")\n\n\nif __name__ == "__main__":\n    args = sys.argv[1:]\n    batch_size = int(args[0])\n    snow = connect_snow()\n    batch = []\n    temp_dir = tempfile.TemporaryDirectory()\n    for message in sys.stdin:\n        if message != "\\n":\n            record = json.loads(message)\n            batch.append(\n                (\n                    record["txid"],\n                    record["rfid"],\n                    record["resort"],\n                    record["purchase_time"],\n                    record["expiration_time"],\n                    record["days"],\n                    record["name"],\n                    record["address"],\n                    record["phone"],\n                    record["email"],\n                    record["emergency_contact"],\n                )\n            )\n            if len(batch) == batch_size:\n                save_to_snowflake(snow, batch, temp_dir)\n                batch = []\n        else:\n            break\n    if len(batch) > 0:\n        save_to_snowflake(snow, batch, temp_dir)\n    temp_dir.cleanup()\n    snow.close()\n    logging.info("Ingest complete")\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will see a lot of similarity of this pattern with the previous one in that the connection is the same, but instead of doing single record inserts it batches together a set of records. That batch is written into a Parquet file which is PUT to the table stage and COPY is used to insert. This data shows up immediately after the COPY call is made.\u003C/p\u003E\n","\u003Cp\u003EIn order to test this insert, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython ./data_generator.py 1 | python py_copy_into.py 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the table to verify the data was inserted.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_PY_COPY_INTO;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecat data.json.gz | zcat | python py_copy_into.py 10000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis last call will batch together 10,000 records into each file for processing. As this file gets larger, up to 100mb, you will see this be more efficient on seconds of compute used in Snowpipe and see higher throughputs. Feel free to generate more test data and increase this to get more understanding of this relationship. Review the query performance in Query History in Snowflake.\u003C/p\u003E\n","\u003Ch4\u003ETips\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIngest is billed based on warehouse credits consumed while online.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIt is very hard to fully utilize a warehouse with this pattern. Adding some concurrency will help IF the files are already well sized. Even with the best code, very few workloads have fixed data flow volumes that well match a warehouse. This is mostly a wasted effort as serverless and snowpipe solves all use cases w/o constraints.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETry to get to 100mb files for most efficiency.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EBest warehouses sizes are almost always way smaller than expected, commonly XS.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFile Upload & Copy (Snowpipe) using Python\u003C/h3\u003E\n","\u003Cp\u003EAnother way to get data into Snowflake is to use a service specifically designed for this task: \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro\"\u003ESnowpipe\u003C/a\u003E. Snowpipe uses serverless infrastructure to ingest data from a file uploaded from a client. In this use case I will upload a file to an internal stage and call the Snowpipe service to ingest the file.\u003C/p\u003E\n","\u003Cp\u003EThis is not the only way to use Snowpipe. You can use external stages as well as use eventing from those blob stores so Snowflake will automatically ingest files as they land. Kafka also uses Snowpipe internally which you will see in later examples.\u003C/p\u003E\n","\u003Cp\u003ECreate the table and the snowpipe to handle the ingest. If you changed the data generator for your use case, you will need to change this table to support your data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_SNOWPIPE (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE PIPE LIFT_TICKETS_PIPE AS COPY INTO LIFT_TICKETS_PY_SNOWPIPE\nFILE_FORMAT=(TYPE='PARQUET') \nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a file called py_snowpipe.py. This code will read a batch of lines from standard input, write a file to temporary storage, upload/put that file to LIFT_TICKETS_PY_SNOWPIPE stage, and call the API endpoint to have LIFT_TICKETS_PIPE ingest the file uploaded. Snowpipe will do the COPY INTO the table LIFT_TICKETS_PY_SNOWPIPE.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os, sys, logging\nimport json\nimport uuid\nimport snowflake.connector\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport tempfile\n\nfrom dotenv import load_dotenv\nfrom snowflake.ingest import SimpleIngestManager\nfrom snowflake.ingest import StagedFile\n\nload_dotenv()\nfrom cryptography.hazmat.primitives import serialization\n\nlogging.basicConfig(level=logging.WARN)\n\n\ndef connect_snow():\n    private_key = "-----BEGIN PRIVATE KEY-----\\n" + os.getenv("PRIVATE_KEY") + "\\n-----END PRIVATE KEY-----\\n)"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv("SNOWFLAKE_ACCOUNT"),\n        user=os.getenv("SNOWFLAKE_USER"),\n        private_key=pkb,\n        role="INGEST",\n        database="INGEST",\n        schema="INGEST",\n        warehouse="INGEST",\n        session_parameters={'QUERY_TAG': 'py-snowpipe'}, \n    )\n\n\ndef save_to_snowflake(snow, batch, temp_dir, ingest_manager):\n    logging.debug('inserting batch to db')\n    pandas_df = pd.DataFrame(batch, columns=["TXID","RFID","RESORT","PURCHASE_TIME", "EXPIRATION_TIME","DAYS","NAME","ADDRESS","PHONE","EMAIL", "EMERGENCY_CONTACT"])\n    arrow_table = pa.Table.from_pandas(pandas_df)\n    file_name = f"{str(uuid.uuid1())}.parquet"\n    out_path =  f"{temp_dir.name}/{file_name}"\n    pq.write_table(arrow_table, out_path, use_dictionary=False,compression='SNAPPY')\n    snow.cursor().execute("PUT 'file://{0}' @%LIFT_TICKETS_PY_SNOWPIPE".format(out_path))\n    os.unlink(out_path)\n    # send the new file to snowpipe to ingest (serverless)\n    resp = ingest_manager.ingest_files([StagedFile(file_name, None),])\n    logging.info(f"response from snowflake for file {file_name}: {resp['responseCode']}")\n\n\nif __name__ == "__main__":    \n    args = sys.argv[1:]\n    batch_size = int(args[0])\n    snow = connect_snow()\n    batch = []\n    temp_dir = tempfile.TemporaryDirectory()\n    private_key = "-----BEGIN PRIVATE KEY-----\\n" + os.getenv("PRIVATE_KEY") + "\\n-----END PRIVATE KEY-----\\n)"\n    host = os.getenv("SNOWFLAKE_ACCOUNT") + ".snowflakecomputing.com"\n    ingest_manager = SimpleIngestManager(account=os.getenv("SNOWFLAKE_ACCOUNT"),\n                                         host=host,\n                                         user=os.getenv("SNOWFLAKE_USER"),\n                                         pipe='INGEST.INGEST.LIFT_TICKETS_PIPE',\n                                         private_key=private_key)\n    for message in sys.stdin:\n        if message != '\\n':\n            record = json.loads(message)\n            batch.append((record['txid'],record['rfid'],record["resort"],record["purchase_time"],record["expiration_time"],record['days'],record['name'],record['address'],record['phone'],record['email'], record['emergency_contact']))\n            if len(batch) == batch_size:\n                save_to_snowflake(snow, batch, temp_dir, ingest_manager)\n                batch = []\n        else:\n            break    \n    if len(batch) > 0:\n        save_to_snowflake(snow, batch, temp_dir, ingest_manager)\n    temp_dir.cleanup()\n    snow.close()\n    logging.info("Ingest complete")\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESince this pattern is creating a file, uploading the file, and copying the results of that data it can VERY efficiently load large numbers of records. It is also only charging for the number of seconds of compute used by Snowpipe.\u003C/p\u003E\n","\u003Cp\u003EIn order to test this insert, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython ./data_generator.py 1 | python py_snowpipe.py 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the table to verify the data was inserted. You will probably see 0 records for up to a minute while Snowpipe ingests the file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_PY_SNOWPIPE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecat data.json.gz | zcat | python py_snowpipe.py 10000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis last call will batch together 10,000 records into each file for processing. As this file gets larger, up to 100mb, you will see this be more efficient on seconds of compute used in Snowpipe and see higher throughputs.\u003C/p\u003E\n","\u003Cp\u003ETest this approach with more test data and larger batch sizes. Review INFORMATION_SCHEMA PIPE_USAGE_HISTORY to see how efficient large batches are vs small batches.\u003C/p\u003E\n","\u003Ch4\u003ETips\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIngest is billed based on seconds of compute used by Snowpipe and number of files ingested.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThis is one of the most efficient and highest throughput ways to ingest data when batches are well sized.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFile size is a huge factor for cost efficiency and throughput. If you have files and batches much smaller than 100mb and cannot change them, this pattern should be avoided.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EExpect delays when Snowpipe has enqueued the request to ingest the data. This process is asynchronous. In most cases these patterns can deliver ~ minute ingest times when including the time to batch, upload, and copy but this varies based on your use case.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFile Upload & Copy (Serverless) from the Python Connector\u003C/h3\u003E\n","\u003Cp\u003EIt can be useful to leverage a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/tasks-intro\"\u003EServerless Task\u003C/a\u003E which is scheduled every minute to ingest the files uploaded by clients over the last minute.\u003C/p\u003E\n","\u003Cp\u003EThis has several advantages over using Snowpipe for Copy:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEliminates the per file costs incurred by Snowpipe.\u003C/li\u003E\u003Cli\u003ESmall files can be merged together more efficiently\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIt is also billed per second of compute so warehouse planning/optimization is not required.\u003C/p\u003E\n","\u003Cp\u003ECreate the table and task needed for this ingest pattern:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE INGEST;\nGRANT EXECUTE MANAGED TASK ON ACCOUNT TO ROLE INGEST;\n\nUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_SERVERLESS (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE TASK LIFT_TICKETS_PY_SERVERLESS \nUSER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE='XSMALL' \nAS\nCOPY INTO LIFT_TICKETS_PY_SERVERLESS\nFILE_FORMAT=(TYPE='PARQUET') \nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE \nPURGE=TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a file names py_serverless.py with the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os, sys, logging\nimport json\nimport uuid\nimport snowflake.connector\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport tempfile\n\nfrom dotenv import load_dotenv\nfrom cryptography.hazmat.primitives import serialization\n\nload_dotenv()\nlogging.basicConfig(level=logging.WARN)\n\n\ndef connect_snow():\n    private_key = "-----BEGIN PRIVATE KEY-----\\n" + os.getenv("PRIVATE_KEY") + "\\n-----END PRIVATE KEY-----\\n)"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv("SNOWFLAKE_ACCOUNT"),\n        user=os.getenv("SNOWFLAKE_USER"),\n        private_key=pkb,\n        role="INGEST",\n        database="INGEST",\n        schema="INGEST",\n        warehouse="INGEST",\n        session_parameters={'QUERY_TAG': 'py-serverless'}, \n    )\n\n\ndef save_to_snowflake(snow, batch, temp_dir):\n    logging.debug('inserting batch to db')\n    pandas_df = pd.DataFrame(batch, columns=["TXID","RFID","RESORT","PURCHASE_TIME", "EXPIRATION_TIME",\n                                            "DAYS","NAME","ADDRESS","PHONE","EMAIL", "EMERGENCY_CONTACT"])\n    arrow_table = pa.Table.from_pandas(pandas_df)\n    out_path =  f"{temp_dir.name}/{str(uuid.uuid1())}.parquet"\n    pq.write_table(arrow_table, out_path, use_dictionary=False,compression='SNAPPY')\n    snow.cursor().execute("PUT 'file://{0}' @%LIFT_TICKETS_PY_SERVERLESS".format(out_path))\n    os.unlink(out_path)\n    # this will be skipped if the task is already scheduled, no warehouse will be used\n    # when ran, the task will run as serverless\n    snow.cursor().execute("EXECUTE TASK LIFT_TICKETS_PY_SERVERLESS")\n    logging.debug(f"{len(batch)} tickets in stage")\n\n\nif __name__ == "__main__":    \n    args = sys.argv[1:]\n    batch_size = int(args[0])\n    snow = connect_snow()\n    batch = []\n    temp_dir = tempfile.TemporaryDirectory()\n    for message in sys.stdin:\n        if message != '\\n':\n            record = json.loads(message)\n            batch.append((record['txid'],record['rfid'],record["resort"],record["purchase_time"],record["expiration_time"],\n                        record['days'],record['name'],record['address'],record['phone'],record['email'], record['emergency_contact']))\n            if len(batch) == batch_size:\n                save_to_snowflake(snow, batch, temp_dir)\n                batch = []\n        else:\n            break    \n    if len(batch) > 0:\n        save_to_snowflake(snow, batch, temp_dir)\n    temp_dir.cleanup()\n    snow.close()\n    logging.info("Ingest complete")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIn order to test this insert, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Epython ./data_generator.py 1 | python py_serverless.py 1\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the table to verify the data was inserted.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_PY_SERVERLESS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecat data.json.gz | zcat | python py_serverless.py 10000\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you run multiple tests with different batch sizes (especially smaller sizes), you will see this can save credit consumption over the previous Snowpipe solution as it combines files into loads.\u003C/p\u003E\n","\u003Cp\u003EThe code is calling execute task after each file is uploaded. While this may not seem optimimal, it is not running after each file is uploaded. It is leveraging a feature of tasks which does not allow additional tasks to be enqueued when one is already enqueued to run.\u003C/p\u003E\n","\u003Cp\u003EIt is also common to schedule the task to run every n minutes instead of calling from the clients.\u003C/p\u003E\n","\u003Ch4\u003ETips\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EOnly run the Task as needed when enough data (> 100mb) has been loaded into stage for most efficiency.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUse Serverless Tasks to avoid per file charges and resolve small file inefficiencies.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EKafka Setup and Data Publisher\u003C/h2\u003E\n","\u003Cp\u003EThe following 2 ingest patterns will need Kafka. I will use \u003Ca href=\"http://redpanda.com\"\u003ERedpanda\u003C/a\u003E in this example, but you could also use Apache or Confluent Kafka as well as MSK from AWS and Event Hubs from Azure.\u003C/p\u003E\n","\u003Cp\u003ETo start Kafka locally, create a file called docker-compose.yml with the following contents:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yaml\"\u003Ename: redpanda\nnetworks:\n  redpanda_network:\n    driver: bridge\nvolumes:\n  redpanda-0: null\nservices:\n  redpanda-0:\n    command:\n      - redpanda\n      - start\n      - --kafka-addr internal://0.0.0.0:9092,external://0.0.0.0:19092\n      - --advertise-kafka-addr internal://redpanda-0:9092,external://localhost:19092\n      - --pandaproxy-addr internal://0.0.0.0:8082,external://0.0.0.0:18082\n      - --advertise-pandaproxy-addr internal://redpanda-0:8082,external://localhost:18082\n      - --schema-registry-addr internal://0.0.0.0:8081,external://0.0.0.0:18081\n      - --rpc-addr redpanda-0:33145\n      - --advertise-rpc-addr redpanda-0:33145\n      - --smp 1\n      - --memory 1G\n      - --mode dev-container\n      - --default-log-level=error\n    image: redpandadata/redpanda:v24.2.20\n    container_name: redpanda-0\n    volumes:\n      - redpanda-0:/var/lib/redpanda/data\n    networks:\n      - redpanda_network\n    ports:\n      - 18081:18081\n      - 18082:18082\n      - 19092:19092\n      - 19644:9644  \n  console:\n    container_name: redpanda-console\n    image: redpandadata/console:v2.8.3\n    networks:\n      - redpanda_network\n    entrypoint: /bin/sh\n    command: -c 'echo "$$CONSOLE_CONFIG_FILE" > /tmp/config.yml; /app/console'\n    environment: \n      CONFIG_FILEPATH: /tmp/config.yml\n      CONSOLE_CONFIG_FILE: |\n        kafka:\n          brokers: ["redpanda-0:9092"]\n          schemaRegistry:\n            enabled: true\n            urls: ["http://redpanda-0:8081"]\n        redpanda:\n          adminApi:\n            enabled: true\n            urls: ["http://redpanda-0:9644"]\n        connect:\n          enabled: true\n          clusters:\n            - name: local-connect-cluster\n              url: http://connect:8083\n    ports:\n      - 8080:8080\n    depends_on:\n      - redpanda-0\n  connect:\n    build: \n      dockerfile: Dockerfile\n    hostname: connect\n    container_name: connect\n    networks:\n      - redpanda_network\n    depends_on:\n      - redpanda-0\n    ports:\n      - "8083:8083"\n    environment:\n      CONNECT_CONFIGURATION: |\n          key.converter=org.apache.kafka.connect.converters.ByteArrayConverter\n          value.converter=com.snowflake.kafka.connector.records.SnowflakeJsonConverter\n          group.id=connectors-cluster\n          offset.storage.topic=_internal_connectors_offsets\n          config.storage.topic=_internal_connectors_configs\n          status.storage.topic=_internal_connectors_status\n          config.storage.replication.factor=-1\n          offset.storage.replication.factor=-1\n          status.storage.replication.factor=-1\n          offset.flush.interval.ms=1000\n          producer.linger.ms=50\n          producer.batch.size=131072\n      CONNECT_BOOTSTRAP_SERVERS: "redpanda-0:9092"\n      CONNECT_GC_LOG_ENABLED: "false"\n      CONNECT_HEAP_OPTS: -Xms512M -Xmx512M\n      CONNECT_LOG_LEVEL: info\n      CONNECT_PLUGIN_PATH: /opt/kafka/connect-plugins/\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a file called Dockerfile with the following contents:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EFROM redpandadata/connectors:v1.0.39\n\nUSER root\n\nRUN mkdir -p /opt/kafka/redpanda-plugins/snowflake\nRUN curl -o /opt/kafka/redpanda-plugins/snowflake/snowflake-kafka-connector-3.1.1.jar https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-connector/3.1.1/snowflake-kafka-connector-3.1.1.jar\nRUN curl -o /opt/kafka/redpanda-plugins/snowflake/bc-fips-1.0.1.jar https://repo1.maven.org/maven2/org/bouncycastle/bc-fips/1.0.1/bc-fips-1.0.1.jar\nRUN curl -o /opt/kafka/redpanda-plugins/snowflake/bcpkix-fips-1.0.3.jar https://repo1.maven.org/maven2/org/bouncycastle/bcpkix-fips/1.0.3/bcpkix-fips-1.0.3.jar\n\nUSER redpanda\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EStart the containers in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker compose up -d\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter starting up, you will now have a local Kafka Broker at 127.0.0.1:19092 and the Redpanda Console at \u003Ca href=\"http://localhost:8080/\"\u003Ehttp://localhost:8080/\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EAdd the broker information to your .env file.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESNOWFLAKE_ACCOUNT=<ACCOUNT_HERE>\nSNOWFLAKE_USER=INGEST\nPRIVATE_KEY=<PRIVATE_KEY_HERE>\nREDPANDA_BROKERS=127.0.0.1:19092\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe following code will used in following ingest patterns. It is a Python publisher to take data from standard input and write into the Kafka topic. Write this code to a file named publish_data.py\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport os\nimport logging\nimport sys\nimport confluent_kafka\nfrom kafka.admin import KafkaAdminClient, NewTopic\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\nlogging.basicConfig(level=logging.INFO)\n\nkafka_brokers = os.getenv("REDPANDA_BROKERS")\ntopic_name = os.getenv("KAFKA_TOPIC")\n\n\ndef create_topic():\n    admin_client = KafkaAdminClient(bootstrap_servers=kafka_brokers, client_id='publish_data')\n    topic_metadata = admin_client.list_topics()\n    if topic_name not in topic_metadata:\n        topic = NewTopic(name=topic_name, num_partitions=10, replication_factor=1)\n        admin_client.create_topics(new_topics=[topic], validate_only=False)\n\n\ndef get_kafka_producer():\n    logging.info(f"Connecting to kafka")\n    config = {'bootstrap.servers': kafka_brokers}\n    return confluent_kafka.Producer(**config)\n\n\nif __name__ == "__main__":  \n    producer = get_kafka_producer()\n    create_topic()\n    for message in sys.stdin:\n        if message != '\\n':\n            failed = True\n            while failed:\n                try:\n                    producer.produce(topic_name, value=bytes(message, encoding='utf8'))\n                    failed = False\n                except BufferError as e:\n                    producer.flush()\n                \n        else:\n            break\n    producer.flush()\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo test the code, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=TESTING\npython ./data_generator.py 1 | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis should succeed by creating the topic and inserting the data. You can view the success in the \u003Ca href=\"http://localhost:8080\"\u003ERedpanda console\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EFrom Kafka - in Snowpipe (Batch) mode\u003C/h3\u003E\n","\u003Cp\u003EThe table for the data to be written to will be automatically created by the connector.\u003C/p\u003E\n","\u003Cp\u003EConfigure and install the connector to load data. Run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_BATCH\neval $(cat .env)\n\nURL="https://$SNOWFLAKE_ACCOUNT.snowflakecomputing.com"\nNAME="LIFT_TICKETS_KAFKA_BATCH"\n\ncurl -i -X PUT -H "Content-Type:application/json" \\\n    "http://localhost:8083/connectors/$NAME/config" \\\n    -d '{\n        "connector.class":"com.snowflake.kafka.connector.SnowflakeSinkConnector",\n        "errors.log.enable":"true",\n        "snowflake.database.name":"INGEST",\n        "snowflake.private.key":"'$PRIVATE_KEY'",\n        "snowflake.schema.name":"INGEST",\n        "snowflake.role.name":"INGEST",\n        "snowflake.url.name":"'$URL'",\n        "snowflake.user.name":"'$SNOWFLAKE_USER'",\n        "topics":"'$KAFKA_TOPIC'",\n        "name":"'$NAME'",\n        "buffer.size.bytes":"250000000",\n        "buffer.flush.time":"60",\n        "buffer.count.records":"1000000",\n        "snowflake.topic2table.map":"'$KAFKA_TOPIC:$NAME'"\n    }'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the connector was created and is running in the \u003Ca href=\"http://localhost:8080\"\u003ERedpanda console\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ETo start, lets push in one message to get the table created and verify the connector is working.\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_BATCH\npython ./data_generator.py 1 | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA table named LIFT_TICKETS_KAFKA_BATCH should be created in your account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT get_ddl('table', 'LIFT_TICKETS_KAFKA_BATCH');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThere should be 1 row of data which was created by the data_generator. Note: This can take a minute or so to the flush times in configuration.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_KAFKA_BATCH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter this is verified to be successful, send in all your test data.\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_BATCH\ncat data.json.gz | zcat | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ETips\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EEvery partition will flush to a file when the bytes, time, or records is hit. This can create a LOT of tiny files if not configured well which will be inefficient.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENot all workloads can accommodate quick flush times. The more data that is flowing, the quicker data can be visible while being efficient.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EReducing the number of partitions and increasing the bytes, time, records to get to well sized files is valuable for efficiency.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you don't have time or a use case to get to well sized files, move to streaming which will match or be better in all cases.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENumber of tasks, number of nodes in the Kafka Connect cluster, amount of CPU and memory on those nodes, and number of partitions will affect performance and credit consumption.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EKafka Connector for Snowflake is billed by the second of compute needed to ingest files (Snowpipe).\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFrom Kafka - in Snowpipe Streaming mode\u003C/h3\u003E\n","\u003Cp\u003EConfigure and install a new connector to load data in streaming mode:\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING\neval $(cat .env)\n\nURL="https://$SNOWFLAKE_ACCOUNT.snowflakecomputing.com"\nNAME="LIFT_TICKETS_KAFKA_STREAMING"\n\ncurl -i -X PUT -H "Content-Type:application/json" \\\n    "http://localhost:8083/connectors/$NAME/config" \\\n    -d '{\n        "connector.class":"com.snowflake.kafka.connector.SnowflakeSinkConnector",\n        "errors.log.enable":"true",\n        "snowflake.database.name":"INGEST",\n        "snowflake.private.key":"'$PRIVATE_KEY'",\n        "snowflake.schema.name":"INGEST",\n        "snowflake.role.name":"INGEST",\n        "snowflake.url.name":"'$URL'",\n        "snowflake.user.name":"'$SNOWFLAKE_USER'",\n        "snowflake.enable.schematization": "FALSE",\n        "snowflake.ingestion.method": "SNOWPIPE_STREAMING",\n        "topics":"'$KAFKA_TOPIC'",\n        "name":"'$NAME'",\n        "value.converter":"org.apache.kafka.connect.json.JsonConverter",\n        "value.converter.schemas.enable":"false",\n        "buffer.count.records":"1000000",\n        "buffer.flush.time":"10",\n        "buffer.size.bytes":"250000000",\n        "snowflake.topic2table.map":"'$KAFKA_TOPIC:LIFT_TICKETS_KAFKA_STREAMING'"\n    }'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the connector was created and is running in the \u003Ca href=\"http://localhost:8080\"\u003ERedpanda console\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThis configuration will allow data flowing through the connector to flush much quicker. The flush time is set to 10 seconds. Previously, it was often discussed how important file sizes were. That was because the files were directly impacting the efficient use of a warehouse. Streaming removes this complexity completely.\u003C/p\u003E\n","\u003Cp\u003EData can be loaded in small pieces and will be merged together in the background efficiently by Snowflake. What is even better is that data is immediately available to query before it's merged. All use cases tested have shown Streaming to be as or MORE efficient than the previous Snowpipe only configuration.\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING\npython ./data_generator.py 1 | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA table named LIFT_TICKETS_KAFKA_STREAMING should be created in your account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT get_ddl('table', 'LIFT_TICKETS_KAFKA_STREAMING');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING\ncat data.json.gz | zcat | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the table to verify the data was inserted. Data will appear in the table in seconds!\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_KAFKA_STREAMING;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ETips\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EKafka Connector for Snowflake in Streaming mode is billed by the second of compute needed to merge files as well as the clients connected.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESetting the flush time lower can/will affect query performance as merge happens asynchronously.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENumber of tasks, number of nodes in the Kafka Connect cluster, amount of CPU and memory on those nodes, and number of partitions will affect performance and credit consumption.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EStreaming is the best ingest pattern when using Kafka.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFrom Kafka - Streaming with Schematization\u003C/h3\u003E\n","\u003Cp\u003EThe previous methods for loading data from Kafka landed the data in a Variant field. While this is flexible, it is not the most user friendly or performant way to land data. The Snowflake Connector for Kafka can use schematization to maintain the schema of the landed data.\u003C/p\u003E\n","\u003Cp\u003EConfigure and install a new connector to load data in streaming mode WITH schematization:\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\neval $(cat .env)\n\nURL="https://$SNOWFLAKE_ACCOUNT.snowflakecomputing.com"\nNAME="LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED"\n\ncurl -i -X PUT -H "Content-Type:application/json" \\\n    "http://localhost:8083/connectors/$NAME/config" \\\n    -d '{\n        "connector.class":"com.snowflake.kafka.connector.SnowflakeSinkConnector",\n        "errors.log.enable":"true",\n        "snowflake.database.name":"INGEST",\n        "snowflake.private.key":"'$PRIVATE_KEY'",\n        "snowflake.schema.name":"INGEST",\n        "snowflake.role.name":"INGEST",\n        "snowflake.url.name":"'$URL'",\n        "snowflake.user.name":"'$SNOWFLAKE_USER'",\n        "snowflake.enable.schematization": "TRUE",\n        "snowflake.ingestion.method": "SNOWPIPE_STREAMING",\n        "topics":"'$KAFKA_TOPIC'",\n        "name":"'$NAME'",\n        "value.converter":"org.apache.kafka.connect.json.JsonConverter",\n        "value.converter.schemas.enable":"false",\n        "buffer.count.records":"1000000",\n        "buffer.flush.time":"10",\n        "buffer.size.bytes":"250000000",\n        "snowflake.topic2table.map":"'$KAFKA_TOPIC:LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED'"\n    }'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVerify the connector was created and is running in the Redpanda console.\u003C/p\u003E\n","\u003Cp\u003ERun the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\npython ./data_generator.py 1 | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA table named LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED should be created in your account.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT get_ddl('table', 'LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Eexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\ncat data.json.gz | zcat | python ./publish_data.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe data will land in a table the Connector creates with the schema based on the payload. To see the table and data, run the following SQL:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EFrom Java SDK - Using the Snowflake Ingest Service\u003C/h2\u003E\n","\u003Cp\u003EMany developers want to be able to directly stream data into Snowflake (without Kafka). In order to do so, Snowflake has a Java SDK.\u003C/p\u003E\n","\u003Cp\u003EFirst, create a table for data to be insert into:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_JAVA_STREAMING (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe easiest way to get the sdk working is to use maven for all the dependencies.\u003C/p\u003E\n","\u003Cp\u003ECreate a file pom.xml with the following contents\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-xml\"\u003E<?xml version="1.0" encoding="UTF-8"?>\n\n<project xmlns="http://maven.apache.org/POM/4.0.0"\n  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"\n  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">\n  <modelVersion>4.0.0</modelVersion>\n\n  <groupId>com.snowflake.streaming.app</groupId>\n  <artifactId>java-streaming</artifactId>\n  <version>1.0-SNAPSHOT</version>\n\n  <name>java-streaming</name>\n  <!-- FIXME change it to the project's website -->\n  <url>http://www.example.com</url>\n\n  <properties>\n    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>\n    <maven.compiler.source>1.8</maven.compiler.source>\n    <maven.compiler.target>1.8</maven.compiler.target>\n  </properties>\n\n  <dependencies>\n    <dependency>\n      <groupId>junit</groupId>\n      <artifactId>junit</artifactId>\n      <version>4.13.1</version>\n      <scope>test</scope>\n    </dependency>\n    <dependency>\n      <groupId>net.snowflake</groupId>\n      <artifactId>snowflake-ingest-sdk</artifactId>\n      <version>1.1.3</version>\n    </dependency>\n    <dependency>\n      <groupId>io.github.cdimascio</groupId>\n      <artifactId>dotenv-java</artifactId>\n      <version>2.3.2</version>\n    </dependency>\n    <dependency>\n      <groupId>net.snowflake</groupId>\n      <artifactId>snowflake-jdbc</artifactId>\n      <version>3.13.30</version>\n    </dependency>\n\n    <!-- String collation-->\n    <dependency>\n      <groupId>com.ibm.icu</groupId>\n      <artifactId>icu4j</artifactId>\n      <version>70.1</version>\n    </dependency>\n\n    <!-- jwt token for key pair authentication with GS -->\n    <dependency>\n      <groupId>com.nimbusds</groupId>\n      <artifactId>nimbus-jose-jwt</artifactId>\n      <version>9.9.3</version>\n    </dependency>\n\n    <!-- Jackson for marshalling and unmarshalling JSON -->\n    <dependency>\n      <groupId>com.fasterxml.jackson.core</groupId>\n      <artifactId>jackson-core</artifactId>\n      <version>2.13.1</version>\n    </dependency>\n\n    <!-- Jackson Databind api -->\n    <dependency>\n      <groupId>com.fasterxml.jackson.core</groupId>\n      <artifactId>jackson-databind</artifactId>\n      <version>2.15.0</version>\n    </dependency>\n\n    <!-- Apache HTTP Components for actually sending requests over the network -->\n    <dependency>\n      <groupId>org.apache.httpcomponents</groupId>\n      <artifactId>httpclient</artifactId>\n      <version>4.5.13</version>\n      <exclusions>\n        <exclusion>  <!-- declare the exclusion here -->\n          <groupId>commons-codec</groupId>\n          <artifactId>commons-codec</artifactId>\n        </exclusion>\n      </exclusions>\n    </dependency>\n\n    <dependency>\n      <groupId>commons-codec</groupId>\n      <artifactId>commons-codec</artifactId>\n      <version>1.15</version>\n    </dependency>\n\n\n    <!-- the Async HTTP Client so we can delay execution -->\n    <dependency>\n      <groupId>org.apache.httpcomponents</groupId>\n      <artifactId>httpasyncclient</artifactId>\n      <version>4.1.2</version>\n    </dependency>\n\n\n    <!-- SLF4J api that a client can shim in later -->\n    <dependency>\n      <groupId>org.slf4j</groupId>\n      <artifactId>slf4j-api</artifactId>\n      <version>1.7.21</version>\n      <scope>provided</scope>\n    </dependency>\n\n\n    <!-- JDK logger backend for logging tests -->\n    <dependency>\n      <groupId>org.slf4j</groupId>\n      <artifactId>slf4j-simple</artifactId>\n      <version>1.7.21</version>\n      <scope>test</scope>\n    </dependency>\n\n    <!-- java.lang.NoClassDefFoundError: javax/xml/bind/JAXBException -->\n    <!-- https://stackoverflow.com/questions/43574426/how-to-resolve-java\n        -lang-noclassdeffounderror-javax-xml-bind-jaxbexception-in-j/48404582-->\n    <dependency>\n      <groupId>javax.xml.bind</groupId>\n      <artifactId>jaxb-api</artifactId>\n      <version>2.3.1</version>\n    </dependency>\n\n\n    <!-- JUnit so that we can make some basic unit tests -->\n    <dependency>\n      <groupId>junit</groupId>\n      <artifactId>junit</artifactId>\n      <version>4.13.1</version>\n      <scope>test</scope>\n    </dependency>\n    <dependency>\n      <groupId>org.powermock</groupId>\n      <artifactId>powermock-module-junit4</artifactId>\n      <version>2.0.2</version>\n      <scope>test</scope>\n    </dependency>\n    <dependency>\n      <groupId>org.mockito</groupId>\n      <artifactId>mockito-core</artifactId>\n      <version>3.7.7</version>\n      <scope>test</scope>\n    </dependency>\n    <dependency>\n      <groupId>org.powermock</groupId>\n      <artifactId>powermock-api-mockito2</artifactId>\n      <version>2.0.2</version>\n      <scope>test</scope>\n    </dependency>\n    <dependency>\n      <groupId>org.powermock</groupId>\n      <artifactId>powermock-core</artifactId>\n      <version>2.0.2</version>\n      <scope>test</scope>\n    </dependency>\n\n    <!-- Apache Arrow -->\n    <dependency>\n      <groupId>org.apache.arrow</groupId>\n      <artifactId>arrow-vector</artifactId>\n      <version>8.0.0</version>\n    </dependency>\n    <dependency>\n      <groupId>org.apache.arrow</groupId>\n      <artifactId>arrow-memory-netty</artifactId>\n      <version>8.0.0</version>\n      <scope>runtime</scope>\n    </dependency>\n\n    <!-- https://mvnrepository.com/artifact/io.dropwizard.metrics/metrics-core -->\n    <dependency>\n      <groupId>io.dropwizard.metrics</groupId>\n      <artifactId>metrics-core</artifactId>\n      <version>4.1.22</version>\n    </dependency>\n\n    <!-- https://mvnrepository.com/artifact/io.dropwizard.metrics/metrics-jvm -->\n    <dependency>\n      <groupId>io.dropwizard.metrics</groupId>\n      <artifactId>metrics-jvm</artifactId>\n      <version>4.1.22</version>\n    </dependency>\n\n    <!-- https://mvnrepository.com/artifact/io.dropwizard.metrics/metrics-jmx -->\n    <dependency>\n      <groupId>io.dropwizard.metrics</groupId>\n      <artifactId>metrics-jmx</artifactId>\n      <version>4.2.3</version>\n    </dependency>\n  </dependencies>\n\n  <build>\n    <pluginManagement><!-- lock down plugins versions to avoid using Maven defaults (may be moved to\n      parent pom) -->\n      <plugins>\n        <!-- clean lifecycle, see\n        https://maven.apache.org/ref/current/maven-core/lifecycles.html#clean_Lifecycle -->\n        <plugin>\n          <artifactId>maven-clean-plugin</artifactId>\n          <version>3.1.0</version>\n        </plugin>\n        <!-- default lifecycle, jar packaging: see\n        https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging -->\n        <plugin>\n          <artifactId>maven-resources-plugin</artifactId>\n          <version>3.0.2</version>\n        </plugin>\n        <plugin>\n          <artifactId>maven-compiler-plugin</artifactId>\n          <version>3.8.0</version>\n        </plugin>\n        <plugin>\n          <artifactId>maven-surefire-plugin</artifactId>\n          <version>2.22.1</version>\n        </plugin>\n        <plugin>\n          <artifactId>maven-jar-plugin</artifactId>\n          <version>3.0.2</version>\n        </plugin>\n        <plugin>\n          <artifactId>maven-install-plugin</artifactId>\n          <version>2.5.2</version>\n        </plugin>\n        <plugin>\n          <artifactId>maven-deploy-plugin</artifactId>\n          <version>2.8.2</version>\n        </plugin>\n        <!-- site lifecycle, see\n        https://maven.apache.org/ref/current/maven-core/lifecycles.html#site_Lifecycle -->\n        <plugin>\n          <artifactId>maven-site-plugin</artifactId>\n          <version>3.7.1</version>\n        </plugin>\n        <plugin>\n          <artifactId>maven-project-info-reports-plugin</artifactId>\n          <version>3.0.0</version>\n        </plugin>\n        <plugin>\n          <groupId>org.apache.maven.plugins</groupId>\n          <artifactId>maven-dependency-plugin</artifactId>\n          <version>3.5.0</version>\n          <executions>\n            <execution>\n              <id>copy-dependencies</id>\n              <phase>package</phase>\n              <goals>\n                <goal>copy-dependencies</goal>\n              </goals>\n              <configuration>\n                <outputDirectory>${project.build.directory}/alternateLocation</outputDirectory>\n                <overWriteReleases>false</overWriteReleases>\n                <overWriteSnapshots>false</overWriteSnapshots>\n                <overWriteIfNewer>true</overWriteIfNewer>\n              </configuration>\n            </execution>\n          </executions>\n        </plugin>\n      </plugins>\n    </pluginManagement>\n  </build>\n</project>\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a directory structure src/main/java/com/snowflake/streaming/app inside your project directory and create a file called App.java inside the app directory.\u003C/p\u003E\n","\u003Cp\u003EAdd the following code to App.java. This code will take the records from standard in like previous patterns and stream the data to Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-java\"\u003Epackage com.snowflake.streaming.app;\n\nimport java.io.*;\nimport java.util.Map;\nimport java.util.Properties;\n\nimport io.github.cdimascio.dotenv.Dotenv;\nimport com.fasterxml.jackson.databind.ObjectMapper;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport net.snowflake.ingest.streaming.InsertValidationResponse;\nimport net.snowflake.ingest.streaming.SnowflakeStreamingIngestChannel;\nimport net.snowflake.ingest.streaming.SnowflakeStreamingIngestClient;\nimport net.snowflake.ingest.streaming.SnowflakeStreamingIngestClientFactory;\nimport net.snowflake.ingest.streaming.OpenChannelRequest;\n\npublic class App {\n    private static final Logger LOGGER = LoggerFactory.getLogger(App.class.getName());\n\n    public static void main(String[] args) throws Exception {\n        Dotenv dotenv = Dotenv.configure().load();\n        BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\n        Properties props = new Properties();\n        props.put("user", dotenv.get("SNOWFLAKE_USER"));\n        props.put("url", "https://" + dotenv.get("SNOWFLAKE_ACCOUNT") + ".snowflakecomputing.com:443");\n        props.put("private_key", dotenv.get("PRIVATE_KEY"));\n        props.put("role", "INGEST");\n\n        try (SnowflakeStreamingIngestClient client = SnowflakeStreamingIngestClientFactory.builder("MY_CLIENT")\n                .setProperties(props).build()) {\n            OpenChannelRequest request1 = OpenChannelRequest.builder("MY_CHANNEL")\n                    .setDBName("INGEST")\n                    .setSchemaName("INGEST")\n                    .setTableName("LIFT_TICKETS_JAVA_STREAMING")\n                    .setOnErrorOption(\n                            OpenChannelRequest.OnErrorOption.ABORT)\n                    .build();\n\n            SnowflakeStreamingIngestChannel channel1 = client.openChannel(request1);\n            String line = br.readLine();\n            int val = 0;\n            while (line != null && line.length() > 0) {\n                ObjectMapper mapper = new ObjectMapper();\n                Map<String, Object> map = mapper.readValue(line, Map.class);\n\n                InsertValidationResponse response = channel1.insertRow(map, String.valueOf(val));\n                if (response.hasErrors()) {\n                    System.out.println(response.getInsertErrors().get(0).getException());\n                }\n\n                line = br.readLine();\n                val++;\n            }\n            LOGGER.info("Ingest complete");\n            channel1.close().get();\n        }\n    }\n}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo build and test this code run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Emvn install\nmvn dependency:copy-dependencies\nmvn package\n\npython ./data_generator.py 1 | java -cp "target/java-streaming-1.0-SNAPSHOT.jar:target/dependency/*" -Dorg.slf4j.simpleLogger.defaultLogLevel=error com.snowflake.streaming.app.App\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EQuery the table to verify the data was inserted. Data will appear in the table in seconds!\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_JAVA_STREAMING;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo send in all your test data, you can run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecat data.json.gz | zcat | java -cp "target/java-streaming-1.0-SNAPSHOT.jar:target/dependency/*" -Dorg.slf4j.simpleLogger.defaultLogLevel=error com.snowflake.streaming.app.App\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT count(*) FROM LIFT_TICKETS_JAVA_STREAMING;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETips\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIngest with streaming is billed by the second of compute needed to merge files as well as the clients connected.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENumber of nodes/threads running the Java SDK will affect performance and credit consumption\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EBest ingest pattern when not using Kafka and are processing streaming data\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EIn order to cleanup from all the ingest patterns built, you can drop the USER, ROLE, DATABASE, and WAREHOUSE:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nDROP USER INGEST;\nDROP DATABASE INGEST;\nDROP WAREHOUSE INGEST;\nDROP ROLE INGEST;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo tear down docker, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Edocker compose down\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo delete the conda env, run the following in your shell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Econda deactivate\nconda remove -n sf-ingest-examples --all\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003EAs you've seen, there are many ways to load data into Snowflake. It is important to understand the benefits and consequenses so you can make the right choice when ingesting data into Snowflake.\u003C/p\u003E\n","\u003Cp\u003EWhile some examples only focussed on the Python connector, these patterns are often applicable to our other connectors if your language of choice is not Python. Connectors are available for Python, Java, Node.js, Go, .NET, and PHP. Note that based on the load times, batch size would be worth tuning.\u003C/p\u003E\n","\u003Cp\u003EServerless Tasks, Snowpipe, and Streaming are all built on Snowflake's serverless compute which make it much simpler to have efficient utilization of infrastructure. Managing warehouses and keeping them fully loaded is not easy or even possible in many cases.\u003C/p\u003E\n","\u003Cp\u003EIf you're using the Kafka connector for Snowflake, put it in Streaming mode. It will either be the same or less credit consumption AND make the data available more quickly.\u003C/p\u003E\n","\u003Cp\u003EWhen well-sized batches are not possible, leveraging our Streaming ingest will significantly increase efficiency. We will merge those tiny batches together in Snowflake later in a very efficient workflow while making that data available for query quickly.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to Ingest data with Connectors\u003C/li\u003E\u003Cli\u003EUsing Serverless Tasks and Snowpipe to save credit consumption\u003C/li\u003E\u003Cli\u003EHow to Use the Kafka Connectors for Snowflake\u003C/li\u003E\u003Cli\u003EHow Streaming reduces the time to Ingest AND Increases Efficiency\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/developer-guide/python-connector/python-connector\"\u003ESnowflake Connector for Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/snowflakedb/snowflake-ingest-java\"\u003EJava SDK for the Snowflake Ingest Service\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/snowflakedb/snowflake-ingest-python\"\u003EPython Snowflake Ingest Service SDK\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-snowpipe/\"\u003EGetting Started with Snowpipe\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/\"\u003EGetting Started with Snowpipe Streaming and Amazon MSK\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-streaming-integration/\"\u003EStreaming Data Integration with Snowflake\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview \n\nThere are many different ways to get data into Snowflake. Different use cases, requirements, team skillsets, and technology choices all\ncontribute to making the right decision on how to ingest data. This quickstart will guide you through an example of the same data loaded with different methods:\n\n* SQL Inserts from the Python Connector\n* File Upload & Copy (Warehouse) from the Python Connector\n* File Upload & Copy (Snowpipe) using Python\n* File Upload & Copy (Serverless) from the Python Connector\n* From Kafka - in Snowpipe (Batch) mode\n* From Kafka - in Snowpipe Streaming mode\n* From Java SDK - Using the Snowflake Ingest Service\n\nBy the end of this guide you should be familiar with many ways to load data, and be able to choose the right pattern for your goals and needs. Each method of ingest can be done separately and optionally as desired after going through the initial project setup and are not dependent on each other.\n\n### Prerequisites\n- Snowflake Account with the ability to create a User, Role, Database, Snowpipe, Serverless Task, Execute Task\n- Familiarity with Python, Kafka, and/or Java\n- Basic knowledge of Docker\n- Ability to run Docker locally or access to an environment to run Kafka and Kafka Connectors\n\n### What You’ll Learn \n- How and when to insert data using connectors\n- How and when to insert data from files\n- How to load data from Kafka\n- How to load data from a stream\n\n### What You’ll Need\n- [Snowflake](https://snowflake.com/) Account \n\n### Mac Requirements \n- [Docker](https://docs.docker.com/desktop/install/mac-install/) Installed\n- [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html) Installed\n\n### Linux Requirements\n- [Docker](https://docs.docker.com/engine/install/ubuntu/) Installed\n- [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html) Installed\n\n### Windows Requirements\n- [WSL with Ubuntu](https://learn.microsoft.com/en-us/windows/wsl/install) for Windows\n- [Docker](https://docs.docker.com/engine/install/ubuntu/) Installed in Ubuntu\n- [Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/install/linux.html) Installed in Ubuntu\n\n### What You’ll Build \n- A project which can load data many different ways into your Snowflake account.\n\n## Environment Setup\n\nThis guide has a data generator and several examples which need Python 3.8, Java, and some other libraries and utilities. \n\nTo set up these dependencies, we will use conda.\n\nCreate a file named environment.yml with the following contents\n\n```yaml\nname: sf-ingest-examples\nchannels:\n  - main\n  - conda-forge\n  - defaults\ndependencies:\n  - faker=28.4.1\n  - kafka-python=2.0.2\n  - maven=3.9.6\n  - openjdk=11.0.13\n  - pandas=1.5.3\n  - pip=23.0.1\n  - pyarrow=10.0.1\n  - python=3.9\n  - python-confluent-kafka\n  - python-dotenv=0.21.0\n  - python-rapidjson=1.5\n  - snowflake-connector-python=3.15.0\n  - snowflake-ingest=1.0.10\n  - pip:\n      - optional-faker==2.1.0\n```\n\nTo create the environment needed, run the following in your shell:\n\n```bash\nconda env create -f environment.yml\nconda activate sf-ingest-examples\n```\n\nAnytime you want to come back to this guide, you can reactivate this environment by running the following in your shell:\n\n```bash\nconda activate sf-ingest-examples\n```\n\n## Test Data Generation\n\nIt is nice to have real-world looking data for testing. This guide will generate fictitious lift tickets for patrons of ski resorts.\n\nYou may have your own data you would like to generate, feel free to modify the data generator, the tables, and the code as you go to make it more applicable your use cases.\n\nMost of the ingest patterns we will go through in this guide will actually outperform the faker library so it is best to run the data generation once and reuse that generated data in the different ingest patterns.\n\nCreate a director on your computer for this project and add a file called data_generator.py. This code will take the number of tickets to create as an arg and output the json data with one lift ticket (record) per line. The rest of the files in this guide can be put in this same directory.\n\n```python\nimport sys\nimport rapidjson as json\nimport optional_faker as _\nimport uuid\nimport random\n\nfrom dotenv import load_dotenv\nfrom faker import Faker\nfrom datetime import date, datetime\n\nload_dotenv()\nfake = Faker()\nresorts = [\"Vail\", \"Beaver Creek\", \"Breckenridge\", \"Keystone\", \"Crested Butte\", \"Park City\", \"Heavenly\", \"Northstar\",\n           \"Kirkwood\", \"Whistler Blackcomb\", \"Perisher\", \"Falls Creek\", \"Hotham\", \"Stowe\", \"Mount Snow\", \"Okemo\",\n           \"Hunter Mountain\", \"Mount Sunapee\", \"Attitash\", \"Wildcat\", \"Crotched\", \"Stevens Pass\", \"Liberty\", \"Roundtop\", \n           \"Whitetail\", \"Jack Frost\", \"Big Boulder\", \"Alpine Valley\", \"Boston Mills\", \"Brandywine\", \"Mad River\",\n           \"Hidden Valley\", \"Snow Creek\", \"Wilmot\", \"Afton Alps\" , \"Mt. Brighton\", \"Paoli Peaks\"]    \n\n\ndef print_lift_ticket():\n    global resorts, fake\n    state = fake.state_abbr()\n    lift_ticket = {'txid': str(uuid.uuid4()),\n                   'rfid': hex(random.getrandbits(96)),\n                   'resort': fake.random_element(elements=resorts),\n                   'purchase_time': datetime.utcnow().isoformat(),\n                   'expiration_time': date(2023, 6, 1).isoformat(),\n                   'days': fake.random_int(min=1, max=7),\n                   'name': fake.name(),\n                   'address': fake.none_or({'street_address': fake.street_address(), \n                                             'city': fake.city(), 'state': state, \n                                             'postalcode': fake.postalcode_in_state(state)}),\n                   'phone': fake.none_or(fake.phone_number()),\n                   'email': fake.none_or(fake.email()),\n                   'emergency_contact' : fake.none_or({'name': fake.name(), 'phone': fake.phone_number()}),\n    }\n    d = json.dumps(lift_ticket) + '\\n'\n    sys.stdout.write(d)\n\n\nif __name__ == \"__main__\":\n    args = sys.argv[1:]\n    total_count = int(args[0])\n    for _ in range(total_count):\n        print_lift_ticket()\n    print('')\n\n```\n\nTo test this generator, run the following in your shell:\n\n```bash\npython ./data_generator.py 1\n```\n\nYou should see 1 record written to output.\n\nIn order to quickly have data available for the rest of the guide, dump a lot of data to a file for re-use.\n\nRun the following in your shell:\n\n```bash\npython ./data_generator.py 100000 | gzip \u003E data.json.gz\n```\n\nYou can increase or decrease the size of records to any number that you would like to use. This will currently output the sample data to your current directory, but you can pick any folder you would like. This file will be used in subsequent steps so note where you stored this data and replace later if needed.\n\n## Database Setup\n\nKafka and the Snowpipe API both require the use of key pair authentication. Due to this, I will use keypair for all the ingest solutions so they are all in common. \n\nCreate a database, schema, warehouse, role, and user called INGEST in your Snowflake account.\n\n```sql\nUSE ACCOUNTADMIN;\n\nCREATE WAREHOUSE IF NOT EXISTS INGEST;\nCREATE ROLE IF NOT EXISTS INGEST;\nGRANT USAGE ON WAREHOUSE INGEST TO ROLE INGEST;\nGRANT OPERATE ON WAREHOUSE INGEST TO ROLE INGEST;\nCREATE DATABASE IF NOT EXISTS INGEST;\nUSE DATABASE INGEST;\nCREATE SCHEMA IF NOT EXISTS INGEST;\nUSE SCHEMA INGEST;\nGRANT OWNERSHIP ON DATABASE INGEST TO ROLE INGEST;\nGRANT OWNERSHIP ON SCHEMA INGEST.INGEST TO ROLE INGEST;\n\nCREATE USER INGEST PASSWORD='\u003CREDACTED\u003E' LOGIN_NAME='INGEST' MUST_CHANGE_PASSWORD=FALSE, DISABLED=FALSE, DEFAULT_WAREHOUSE='INGEST', DEFAULT_NAMESPACE='INGEST.INGEST', DEFAULT_ROLE='INGEST';\nGRANT ROLE INGEST TO USER INGEST;\nSET USERNAME=CURRENT_USER();\nGRANT ROLE INGEST TO USER IDENTIFIER($USERNAME);\n```\n\nTo generate a key pair for the INGEST user, run the following in your shell:\n\n```bash\nopenssl genrsa 4096 | openssl pkcs8 -topk8 -inform PEM -out rsa_key.p8 -nocrypt\nopenssl rsa -in rsa_key.p8 -pubout -out rsa_key.pub\necho \"ALTER USER INGEST SET RSA_PUBLIC_KEY='`cat ./rsa_key.pub`';\"\n```\n\nRun the sql from the output to set the RSA_PUBLIC_KEY for the INGEST user.\n\nTo get the private key for this user run the following in your shell:\n\n```bash\nPRVK=`cat ./rsa_key.p8 | grep -v KEY- | tr -d '\\012'`\necho \"PRIVATE_KEY=$PRVK\"\n```\n\nAdd these variables to a new .env file in your project:\n\n```\nSNOWFLAKE_ACCOUNT=\u003CACCOUNT_HERE\u003E\nSNOWFLAKE_USER=INGEST\nPRIVATE_KEY=\u003CPRIVATE_KEY_HERE\u003E\n```\n\nMake sure you protect your .env and .p8 file as those are credentials directly to the INGEST user.\n\n## SQL Inserts from the Python Connector\n\nSnowflake has a [Python connector](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector) which is an easy way to run sql and upload files. One way to get data in would be to do an SQL INSERT statement for each record. While this is a convenient way to insert data, it is not efficient as Snowflake is an OLAP engine and is optimized around writing large batches of data.\n\nCreate a table in Snowflake called LIFT_TICKETS_PY_INSERT to recieve this data from the INGEST user.\n\n```sql\nUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_INSERT (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n```\n\nCreate a file named py_insert.py. This code will read a line from standard input and insert that record into Snowflake using a SQL INSERT. Change the table names/fields as needed to support your use case.\n\n```python\nimport os, sys, logging\nimport json\nimport snowflake.connector\n\nfrom dotenv import load_dotenv\nfrom cryptography.hazmat.primitives import serialization\n\nload_dotenv()\nlogging.basicConfig(level=logging.WARN)\nsnowflake.connector.paramstyle='qmark'\n\n\ndef connect_snow():\n    private_key = \"-----BEGIN PRIVATE KEY-----\\n\" + os.getenv(\"PRIVATE_KEY\") + \"\\n-----END PRIVATE KEY-----\\n)\"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n        user=os.getenv(\"SNOWFLAKE_USER\"),\n        private_key=pkb,\n        role=\"INGEST\",\n        database=\"INGEST\",\n        schema=\"INGEST\",\n        warehouse=\"INGEST\",\n        session_parameters={'QUERY_TAG': 'py-insert'}, \n    )\n\n\ndef save_to_snowflake(snow, message):\n    record = json.loads(message)\n    logging.debug('inserting record to db')\n    row = (record['txid'],record['rfid'],record[\"resort\"],record[\"purchase_time\"],record[\"expiration_time\"],record['days'],record['name'],json.dumps(record['address']),record['phone'],record['email'],json.dumps(record['emergency_contact']))\n    # this dataset has variant records, so utilizing an executemany() is not possible, must insert 1 record at a time\n    snow.cursor().execute(\"INSERT INTO LIFT_TICKETS_PY_INSERT (\\\"TXID\\\",\\\"RFID\\\",\\\"RESORT\\\",\\\"PURCHASE_TIME\\\", \\\"EXPIRATION_TIME\\\",\\\"DAYS\\\",\\\"NAME\\\",\\\"ADDRESS\\\",\\\"PHONE\\\",\\\"EMAIL\\\",\\\"EMERGENCY_CONTACT\\\") SELECT ?,?,?,?,?,?,?,PARSE_JSON(?),?,?,PARSE_JSON(?)\", row)\n    logging.debug(f\"inserted ticket {record}\")\n\n\nif __name__ == \"__main__\":    \n    snow = connect_snow()\n    for message in sys.stdin:\n        if message != '\\n':\n            save_to_snowflake(snow, message)\n        else:\n            break\n    snow.close()\n    logging.info(\"Ingest complete\")\n    \n```\n\nIn order to test this insert, run the following in your shell:\n\n```bash\npython ./data_generator.py 1 | python py_insert.py\n```\n\nQuery the table to verify the data was inserted.\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_PY_INSERT;\n```\n\nWARNING, this is not a good way to load data and will take a long time. I don't really want you to have to wait for hours to load your example dataset, so lets just load 1000 records.\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\ncat data.json.gz | zcat | head -n 1000 | python py_insert.py\n```\n\nFeel free to take a break and come back in a few minutes.\n\nDo you think this could be faster if we parallelized the work? \n\nTo verify if that is true or not, run 10 of these same loads in multiple terminals and see how long it takes.\n\nRun the following in your shell:\n\n```bash\ncat data.json.gz | zcat | head -n 100 | python py_insert.py\n```\n\nThe total time loading in parallel will be the same or worse than before. This is not a good pattern to get to high throughputs of records.\n\n### Tips\n\n* Ingest is billed based on warehouse credits consumed while online.\n\n* The connectors support multi-inserts but data containing a variant field cannot be formatted into a multi-insert.\n\n* Using inserts and multi-inserts will not efficiently use warehouse resources (optimal at 100MB or more with some concurrency). It is better to upload data and COPY into the table.\n\n* Connectors will switch to creating and uploading a file and doing a COPY into when large batches are set. This is not configurable.\n\n* Many assume adding significant concurrency will support higher throughputs of data. The additional concurrent INSERTS will be blocked by other INSERTS, more frequently when small payloads are inserted. You need to move to bigger batches to get more througput.\n\n* Review query history to see what the connector is doing.\n\nIn cases where the connector has enough data in the executemany to create a well sized file for COPY and does so, this does become as efficient as the following methods.\n\nThe example above could not use executemany as it had VARIANT data.\n\nThe next methods will show how to batch into better sized blocks of work which will drive higher throughputs and higher efficiency on Snowflake.\n\n\n##  File Uploads\n\n### File Upload & Copy (Warehouse) from the Python Connector\n\nTo get to better sized batches, the client can upload a file and have a warehouse copy the data into the destination. The Python connector can execute the COPY after uploading the file.\n\nCreate the table which will be used for landing the data, changing as needed for your use case.\n\n```sql\nUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_COPY_INTO (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n```\n\nCreate a file named py_copy_into.py with the following contents. You will need to change this code if you changed the data generator.\n\n```python\nimport os, sys, logging\nimport json\nimport uuid\nimport snowflake.connector\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport tempfile\n\nfrom dotenv import load_dotenv\nfrom cryptography.hazmat.primitives import serialization\n\nload_dotenv()\nlogging.basicConfig(level=logging.WARN)\n\n\ndef connect_snow():\n    private_key = \"-----BEGIN PRIVATE KEY-----\\n\" + os.getenv(\"PRIVATE_KEY\") + \"\\n-----END PRIVATE KEY-----\\n)\"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n        user=os.getenv(\"SNOWFLAKE_USER\"),\n        private_key=pkb,\n        role=\"INGEST\",\n        database=\"INGEST\",\n        schema=\"INGEST\",\n        warehouse=\"INGEST\",\n        session_parameters={'QUERY_TAG': 'py-copy-into'}, \n    )\n\n\ndef save_to_snowflake(snow, batch, temp_dir):\n    logging.debug(\"inserting batch to db\")\n    pandas_df = pd.DataFrame(\n        batch,\n        columns=[\n            \"TXID\",\n            \"RFID\",\n            \"RESORT\",\n            \"PURCHASE_TIME\",\n            \"EXPIRATION_TIME\",\n            \"DAYS\",\n            \"NAME\",\n            \"ADDRESS\",\n            \"PHONE\",\n            \"EMAIL\",\n            \"EMERGENCY_CONTACT\",\n        ],\n    )\n    arrow_table = pa.Table.from_pandas(pandas_df)\n    out_path = f\"{temp_dir.name}/{str(uuid.uuid1())}.parquet\"\n    pq.write_table(arrow_table, out_path, use_dictionary=False, compression=\"SNAPPY\")\n    snow.cursor().execute(\n        \"PUT 'file://{0}' @%LIFT_TICKETS_PY_COPY_INTO\".format(out_path)\n    )\n    os.unlink(out_path)\n    snow.cursor().execute(\n        \"COPY INTO LIFT_TICKETS_PY_COPY_INTO FILE_FORMAT=(TYPE='PARQUET') MATCH_BY_COLUMN_NAME=CASE_SENSITIVE PURGE=TRUE\"\n    )\n    logging.debug(f\"inserted {len(batch)} tickets\")\n\n\nif __name__ == \"__main__\":\n    args = sys.argv[1:]\n    batch_size = int(args[0])\n    snow = connect_snow()\n    batch = []\n    temp_dir = tempfile.TemporaryDirectory()\n    for message in sys.stdin:\n        if message != \"\\n\":\n            record = json.loads(message)\n            batch.append(\n                (\n                    record[\"txid\"],\n                    record[\"rfid\"],\n                    record[\"resort\"],\n                    record[\"purchase_time\"],\n                    record[\"expiration_time\"],\n                    record[\"days\"],\n                    record[\"name\"],\n                    record[\"address\"],\n                    record[\"phone\"],\n                    record[\"email\"],\n                    record[\"emergency_contact\"],\n                )\n            )\n            if len(batch) == batch_size:\n                save_to_snowflake(snow, batch, temp_dir)\n                batch = []\n        else:\n            break\n    if len(batch) \u003E 0:\n        save_to_snowflake(snow, batch, temp_dir)\n    temp_dir.cleanup()\n    snow.close()\n    logging.info(\"Ingest complete\")\n\n\n```\n\nYou will see a lot of similarity of this pattern with the previous one in that the connection is the same, but instead of doing single record inserts it batches together a set of records. That batch is written into a Parquet file which is PUT to the table stage and COPY is used to insert. This data shows up immediately after the COPY call is made.\n\nIn order to test this insert, run the following in your shell:\n\n```bash\npython ./data_generator.py 1 | python py_copy_into.py 1\n```\n\nQuery the table to verify the data was inserted.\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_PY_COPY_INTO;\n```\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\ncat data.json.gz | zcat | python py_copy_into.py 10000\n```\n\nThis last call will batch together 10,000 records into each file for processing. As this file gets larger, up to 100mb, you will see this be more efficient on seconds of compute used in Snowpipe and see higher throughputs. Feel free to generate more test data and increase this to get more understanding of this relationship. Review the query performance in Query History in Snowflake.\n\n#### Tips\n\n* Ingest is billed based on warehouse credits consumed while online.\n\n* It is very hard to fully utilize a warehouse with this pattern. Adding some concurrency will help IF the files are already well sized. Even with the best code, very few workloads have fixed data flow volumes that well match a warehouse. This is mostly a wasted effort as serverless and snowpipe solves all use cases w/o constraints.\n\n* Try to get to 100mb files for most efficiency.\n\n* Best warehouses sizes are almost always way smaller than expected, commonly XS.\n\n\n### File Upload & Copy (Snowpipe) using Python\n\nAnother way to get data into Snowflake is to use a service specifically designed for this task: [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro). Snowpipe uses serverless infrastructure to ingest data from a file uploaded from a client. In this use case I will upload a file to an internal stage and call the Snowpipe service to ingest the file.\n\nThis is not the only way to use Snowpipe. You can use external stages as well as use eventing from those blob stores so Snowflake will automatically ingest files as they land. Kafka also uses Snowpipe internally which you will see in later examples.\n\nCreate the table and the snowpipe to handle the ingest. If you changed the data generator for your use case, you will need to change this table to support your data.\n\n```sql\nUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_SNOWPIPE (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE PIPE LIFT_TICKETS_PIPE AS COPY INTO LIFT_TICKETS_PY_SNOWPIPE\nFILE_FORMAT=(TYPE='PARQUET') \nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE;\n```\n\nCreate a file called py_snowpipe.py. This code will read a batch of lines from standard input, write a file to temporary storage, upload/put that file to LIFT_TICKETS_PY_SNOWPIPE stage, and call the API endpoint to have LIFT_TICKETS_PIPE ingest the file uploaded. Snowpipe will do the COPY INTO the table LIFT_TICKETS_PY_SNOWPIPE.\n\n```python\nimport os, sys, logging\nimport json\nimport uuid\nimport snowflake.connector\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport tempfile\n\nfrom dotenv import load_dotenv\nfrom snowflake.ingest import SimpleIngestManager\nfrom snowflake.ingest import StagedFile\n\nload_dotenv()\nfrom cryptography.hazmat.primitives import serialization\n\nlogging.basicConfig(level=logging.WARN)\n\n\ndef connect_snow():\n    private_key = \"-----BEGIN PRIVATE KEY-----\\n\" + os.getenv(\"PRIVATE_KEY\") + \"\\n-----END PRIVATE KEY-----\\n)\"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n        user=os.getenv(\"SNOWFLAKE_USER\"),\n        private_key=pkb,\n        role=\"INGEST\",\n        database=\"INGEST\",\n        schema=\"INGEST\",\n        warehouse=\"INGEST\",\n        session_parameters={'QUERY_TAG': 'py-snowpipe'}, \n    )\n\n\ndef save_to_snowflake(snow, batch, temp_dir, ingest_manager):\n    logging.debug('inserting batch to db')\n    pandas_df = pd.DataFrame(batch, columns=[\"TXID\",\"RFID\",\"RESORT\",\"PURCHASE_TIME\", \"EXPIRATION_TIME\",\"DAYS\",\"NAME\",\"ADDRESS\",\"PHONE\",\"EMAIL\", \"EMERGENCY_CONTACT\"])\n    arrow_table = pa.Table.from_pandas(pandas_df)\n    file_name = f\"{str(uuid.uuid1())}.parquet\"\n    out_path =  f\"{temp_dir.name}/{file_name}\"\n    pq.write_table(arrow_table, out_path, use_dictionary=False,compression='SNAPPY')\n    snow.cursor().execute(\"PUT 'file://{0}' @%LIFT_TICKETS_PY_SNOWPIPE\".format(out_path))\n    os.unlink(out_path)\n    # send the new file to snowpipe to ingest (serverless)\n    resp = ingest_manager.ingest_files([StagedFile(file_name, None),])\n    logging.info(f\"response from snowflake for file {file_name}: {resp['responseCode']}\")\n\n\nif __name__ == \"__main__\":    \n    args = sys.argv[1:]\n    batch_size = int(args[0])\n    snow = connect_snow()\n    batch = []\n    temp_dir = tempfile.TemporaryDirectory()\n    private_key = \"-----BEGIN PRIVATE KEY-----\\n\" + os.getenv(\"PRIVATE_KEY\") + \"\\n-----END PRIVATE KEY-----\\n)\"\n    host = os.getenv(\"SNOWFLAKE_ACCOUNT\") + \".snowflakecomputing.com\"\n    ingest_manager = SimpleIngestManager(account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n                                         host=host,\n                                         user=os.getenv(\"SNOWFLAKE_USER\"),\n                                         pipe='INGEST.INGEST.LIFT_TICKETS_PIPE',\n                                         private_key=private_key)\n    for message in sys.stdin:\n        if message != '\\n':\n            record = json.loads(message)\n            batch.append((record['txid'],record['rfid'],record[\"resort\"],record[\"purchase_time\"],record[\"expiration_time\"],record['days'],record['name'],record['address'],record['phone'],record['email'], record['emergency_contact']))\n            if len(batch) == batch_size:\n                save_to_snowflake(snow, batch, temp_dir, ingest_manager)\n                batch = []\n        else:\n            break    \n    if len(batch) \u003E 0:\n        save_to_snowflake(snow, batch, temp_dir, ingest_manager)\n    temp_dir.cleanup()\n    snow.close()\n    logging.info(\"Ingest complete\")\n\n```\n\nSince this pattern is creating a file, uploading the file, and copying the results of that data it can VERY efficiently load large numbers of records. It is also only charging for the number of seconds of compute used by Snowpipe.\n\nIn order to test this insert, run the following in your shell:\n\n```bash\npython ./data_generator.py 1 | python py_snowpipe.py 1\n```\n\nQuery the table to verify the data was inserted. You will probably see 0 records for up to a minute while Snowpipe ingests the file.\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_PY_SNOWPIPE;\n```\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\ncat data.json.gz | zcat | python py_snowpipe.py 10000\n```\n\nThis last call will batch together 10,000 records into each file for processing. As this file gets larger, up to 100mb, you will see this be more efficient on seconds of compute used in Snowpipe and see higher throughputs.\n\nTest this approach with more test data and larger batch sizes. Review INFORMATION_SCHEMA PIPE_USAGE_HISTORY to see how efficient large batches are vs small batches.\n\n#### Tips\n\n* Ingest is billed based on seconds of compute used by Snowpipe and number of files ingested.\n\n* This is one of the most efficient and highest throughput ways to ingest data when batches are well sized.\n\n* File size is a huge factor for cost efficiency and throughput. If you have files and batches much smaller than 100mb and cannot change them, this pattern should be avoided.\n\n* Expect delays when Snowpipe has enqueued the request to ingest the data. This process is asynchronous. In most cases these patterns can deliver ~ minute ingest times when including the time to batch, upload, and copy but this varies based on your use case.\n\n### File Upload & Copy (Serverless) from the Python Connector\n\nIt can be useful to leverage a [Serverless Task](https://docs.snowflake.com/en/user-guide/tasks-intro) which is scheduled every minute to ingest the files uploaded by clients over the last minute.\n\nThis has several advantages over using Snowpipe for Copy:\n* Eliminates the per file costs incurred by Snowpipe.\n* Small files can be merged together more efficiently\n\nIt is also billed per second of compute so warehouse planning/optimization is not required.\n\nCreate the table and task needed for this ingest pattern:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nGRANT EXECUTE TASK ON ACCOUNT TO ROLE INGEST;\nGRANT EXECUTE MANAGED TASK ON ACCOUNT TO ROLE INGEST;\n\nUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_PY_SERVERLESS (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n\nCREATE OR REPLACE TASK LIFT_TICKETS_PY_SERVERLESS \nUSER_TASK_MANAGED_INITIAL_WAREHOUSE_SIZE='XSMALL' \nAS\nCOPY INTO LIFT_TICKETS_PY_SERVERLESS\nFILE_FORMAT=(TYPE='PARQUET') \nMATCH_BY_COLUMN_NAME=CASE_SENSITIVE \nPURGE=TRUE;\n```\n\nCreate a file names py_serverless.py with the following code:\n\n```python\nimport os, sys, logging\nimport json\nimport uuid\nimport snowflake.connector\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport tempfile\n\nfrom dotenv import load_dotenv\nfrom cryptography.hazmat.primitives import serialization\n\nload_dotenv()\nlogging.basicConfig(level=logging.WARN)\n\n\ndef connect_snow():\n    private_key = \"-----BEGIN PRIVATE KEY-----\\n\" + os.getenv(\"PRIVATE_KEY\") + \"\\n-----END PRIVATE KEY-----\\n)\"\n    p_key = serialization.load_pem_private_key(\n        bytes(private_key, 'utf-8'),\n        password=None\n    )\n    pkb = p_key.private_bytes(\n        encoding=serialization.Encoding.DER,\n        format=serialization.PrivateFormat.PKCS8,\n        encryption_algorithm=serialization.NoEncryption())\n\n    return snowflake.connector.connect(\n        account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n        user=os.getenv(\"SNOWFLAKE_USER\"),\n        private_key=pkb,\n        role=\"INGEST\",\n        database=\"INGEST\",\n        schema=\"INGEST\",\n        warehouse=\"INGEST\",\n        session_parameters={'QUERY_TAG': 'py-serverless'}, \n    )\n\n\ndef save_to_snowflake(snow, batch, temp_dir):\n    logging.debug('inserting batch to db')\n    pandas_df = pd.DataFrame(batch, columns=[\"TXID\",\"RFID\",\"RESORT\",\"PURCHASE_TIME\", \"EXPIRATION_TIME\",\n                                            \"DAYS\",\"NAME\",\"ADDRESS\",\"PHONE\",\"EMAIL\", \"EMERGENCY_CONTACT\"])\n    arrow_table = pa.Table.from_pandas(pandas_df)\n    out_path =  f\"{temp_dir.name}/{str(uuid.uuid1())}.parquet\"\n    pq.write_table(arrow_table, out_path, use_dictionary=False,compression='SNAPPY')\n    snow.cursor().execute(\"PUT 'file://{0}' @%LIFT_TICKETS_PY_SERVERLESS\".format(out_path))\n    os.unlink(out_path)\n    # this will be skipped if the task is already scheduled, no warehouse will be used\n    # when ran, the task will run as serverless\n    snow.cursor().execute(\"EXECUTE TASK LIFT_TICKETS_PY_SERVERLESS\")\n    logging.debug(f\"{len(batch)} tickets in stage\")\n\n\nif __name__ == \"__main__\":    \n    args = sys.argv[1:]\n    batch_size = int(args[0])\n    snow = connect_snow()\n    batch = []\n    temp_dir = tempfile.TemporaryDirectory()\n    for message in sys.stdin:\n        if message != '\\n':\n            record = json.loads(message)\n            batch.append((record['txid'],record['rfid'],record[\"resort\"],record[\"purchase_time\"],record[\"expiration_time\"],\n                        record['days'],record['name'],record['address'],record['phone'],record['email'], record['emergency_contact']))\n            if len(batch) == batch_size:\n                save_to_snowflake(snow, batch, temp_dir)\n                batch = []\n        else:\n            break    \n    if len(batch) \u003E 0:\n        save_to_snowflake(snow, batch, temp_dir)\n    temp_dir.cleanup()\n    snow.close()\n    logging.info(\"Ingest complete\")\n```\n\nIn order to test this insert, run the following in your shell:\n\n```bash\npython ./data_generator.py 1 | python py_serverless.py 1\n```\n\nQuery the table to verify the data was inserted.\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_PY_SERVERLESS;\n```\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\ncat data.json.gz | zcat | python py_serverless.py 10000\n```\n\nIf you run multiple tests with different batch sizes (especially smaller sizes), you will see this can save credit consumption over the previous Snowpipe solution as it combines files into loads.\n\nThe code is calling execute task after each file is uploaded. While this may not seem optimimal, it is not running after each file is uploaded. It is leveraging a feature of tasks which does not allow additional tasks to be enqueued when one is already enqueued to run.\n\nIt is also common to schedule the task to run every n minutes instead of calling from the clients.\n\n#### Tips\n\n* Only run the Task as needed when enough data (\u003E 100mb) has been loaded into stage for most efficiency.\n\n* Use Serverless Tasks to avoid per file charges and resolve small file inefficiencies.\n\n\n## Kafka Setup and Data Publisher\n\nThe following 2 ingest patterns will need Kafka. I will use [Redpanda](http://redpanda.com) in this example, but you could also use Apache or Confluent Kafka as well as MSK from AWS and Event Hubs from Azure.\n\nTo start Kafka locally, create a file called docker-compose.yml with the following contents:\n\n```yaml\nname: redpanda\nnetworks:\n  redpanda_network:\n    driver: bridge\nvolumes:\n  redpanda-0: null\nservices:\n  redpanda-0:\n    command:\n      - redpanda\n      - start\n      - --kafka-addr internal://0.0.0.0:9092,external://0.0.0.0:19092\n      - --advertise-kafka-addr internal://redpanda-0:9092,external://localhost:19092\n      - --pandaproxy-addr internal://0.0.0.0:8082,external://0.0.0.0:18082\n      - --advertise-pandaproxy-addr internal://redpanda-0:8082,external://localhost:18082\n      - --schema-registry-addr internal://0.0.0.0:8081,external://0.0.0.0:18081\n      - --rpc-addr redpanda-0:33145\n      - --advertise-rpc-addr redpanda-0:33145\n      - --smp 1\n      - --memory 1G\n      - --mode dev-container\n      - --default-log-level=error\n    image: redpandadata/redpanda:v24.2.20\n    container_name: redpanda-0\n    volumes:\n      - redpanda-0:/var/lib/redpanda/data\n    networks:\n      - redpanda_network\n    ports:\n      - 18081:18081\n      - 18082:18082\n      - 19092:19092\n      - 19644:9644  \n  console:\n    container_name: redpanda-console\n    image: redpandadata/console:v2.8.3\n    networks:\n      - redpanda_network\n    entrypoint: /bin/sh\n    command: -c 'echo \"$$CONSOLE_CONFIG_FILE\" \u003E /tmp/config.yml; /app/console'\n    environment: \n      CONFIG_FILEPATH: /tmp/config.yml\n      CONSOLE_CONFIG_FILE: |\n        kafka:\n          brokers: [\"redpanda-0:9092\"]\n          schemaRegistry:\n            enabled: true\n            urls: [\"http://redpanda-0:8081\"]\n        redpanda:\n          adminApi:\n            enabled: true\n            urls: [\"http://redpanda-0:9644\"]\n        connect:\n          enabled: true\n          clusters:\n            - name: local-connect-cluster\n              url: http://connect:8083\n    ports:\n      - 8080:8080\n    depends_on:\n      - redpanda-0\n  connect:\n    build: \n      dockerfile: Dockerfile\n    hostname: connect\n    container_name: connect\n    networks:\n      - redpanda_network\n    depends_on:\n      - redpanda-0\n    ports:\n      - \"8083:8083\"\n    environment:\n      CONNECT_CONFIGURATION: |\n          key.converter=org.apache.kafka.connect.converters.ByteArrayConverter\n          value.converter=com.snowflake.kafka.connector.records.SnowflakeJsonConverter\n          group.id=connectors-cluster\n          offset.storage.topic=_internal_connectors_offsets\n          config.storage.topic=_internal_connectors_configs\n          status.storage.topic=_internal_connectors_status\n          config.storage.replication.factor=-1\n          offset.storage.replication.factor=-1\n          status.storage.replication.factor=-1\n          offset.flush.interval.ms=1000\n          producer.linger.ms=50\n          producer.batch.size=131072\n      CONNECT_BOOTSTRAP_SERVERS: \"redpanda-0:9092\"\n      CONNECT_GC_LOG_ENABLED: \"false\"\n      CONNECT_HEAP_OPTS: -Xms512M -Xmx512M\n      CONNECT_LOG_LEVEL: info\n      CONNECT_PLUGIN_PATH: /opt/kafka/connect-plugins/\n\n```\n\nCreate a file called Dockerfile with the following contents:\n\n```\nFROM redpandadata/connectors:v1.0.39\n\nUSER root\n\nRUN mkdir -p /opt/kafka/redpanda-plugins/snowflake\nRUN curl -o /opt/kafka/redpanda-plugins/snowflake/snowflake-kafka-connector-3.1.1.jar https://repo1.maven.org/maven2/com/snowflake/snowflake-kafka-connector/3.1.1/snowflake-kafka-connector-3.1.1.jar\nRUN curl -o /opt/kafka/redpanda-plugins/snowflake/bc-fips-1.0.1.jar https://repo1.maven.org/maven2/org/bouncycastle/bc-fips/1.0.1/bc-fips-1.0.1.jar\nRUN curl -o /opt/kafka/redpanda-plugins/snowflake/bcpkix-fips-1.0.3.jar https://repo1.maven.org/maven2/org/bouncycastle/bcpkix-fips/1.0.3/bcpkix-fips-1.0.3.jar\n\nUSER redpanda\n\n```\n\nStart the containers in your shell:\n\n```bash\ndocker compose up -d\n```\n\nAfter starting up, you will now have a local Kafka Broker at 127.0.0.1:19092 and the Redpanda Console at [http://localhost:8080/](http://localhost:8080/).\n\nAdd the broker information to your .env file.\n\n```\nSNOWFLAKE_ACCOUNT=\u003CACCOUNT_HERE\u003E\nSNOWFLAKE_USER=INGEST\nPRIVATE_KEY=\u003CPRIVATE_KEY_HERE\u003E\nREDPANDA_BROKERS=127.0.0.1:19092\n```\n\nThe following code will used in following ingest patterns. It is a Python publisher to take data from standard input and write into the Kafka topic. Write this code to a file named publish_data.py\n\n```python\nimport os\nimport logging\nimport sys\nimport confluent_kafka\nfrom kafka.admin import KafkaAdminClient, NewTopic\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\nlogging.basicConfig(level=logging.INFO)\n\nkafka_brokers = os.getenv(\"REDPANDA_BROKERS\")\ntopic_name = os.getenv(\"KAFKA_TOPIC\")\n\n\ndef create_topic():\n    admin_client = KafkaAdminClient(bootstrap_servers=kafka_brokers, client_id='publish_data')\n    topic_metadata = admin_client.list_topics()\n    if topic_name not in topic_metadata:\n        topic = NewTopic(name=topic_name, num_partitions=10, replication_factor=1)\n        admin_client.create_topics(new_topics=[topic], validate_only=False)\n\n\ndef get_kafka_producer():\n    logging.info(f\"Connecting to kafka\")\n    config = {'bootstrap.servers': kafka_brokers}\n    return confluent_kafka.Producer(**config)\n\n\nif __name__ == \"__main__\":  \n    producer = get_kafka_producer()\n    create_topic()\n    for message in sys.stdin:\n        if message != '\\n':\n            failed = True\n            while failed:\n                try:\n                    producer.produce(topic_name, value=bytes(message, encoding='utf8'))\n                    failed = False\n                except BufferError as e:\n                    producer.flush()\n                \n        else:\n            break\n    producer.flush()\n\n```\n\nTo test the code, you can run the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=TESTING\npython ./data_generator.py 1 | python ./publish_data.py\n```\n\nThis should succeed by creating the topic and inserting the data. You can view the success in the [Redpanda console](http://localhost:8080).\n\n### From Kafka - in Snowpipe (Batch) mode\n\nThe table for the data to be written to will be automatically created by the connector.\n\nConfigure and install the connector to load data. Run the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_BATCH\neval $(cat .env)\n\nURL=\"https://$SNOWFLAKE_ACCOUNT.snowflakecomputing.com\"\nNAME=\"LIFT_TICKETS_KAFKA_BATCH\"\n\ncurl -i -X PUT -H \"Content-Type:application/json\" \\\n    \"http://localhost:8083/connectors/$NAME/config\" \\\n    -d '{\n        \"connector.class\":\"com.snowflake.kafka.connector.SnowflakeSinkConnector\",\n        \"errors.log.enable\":\"true\",\n        \"snowflake.database.name\":\"INGEST\",\n        \"snowflake.private.key\":\"'$PRIVATE_KEY'\",\n        \"snowflake.schema.name\":\"INGEST\",\n        \"snowflake.role.name\":\"INGEST\",\n        \"snowflake.url.name\":\"'$URL'\",\n        \"snowflake.user.name\":\"'$SNOWFLAKE_USER'\",\n        \"topics\":\"'$KAFKA_TOPIC'\",\n        \"name\":\"'$NAME'\",\n        \"buffer.size.bytes\":\"250000000\",\n        \"buffer.flush.time\":\"60\",\n        \"buffer.count.records\":\"1000000\",\n        \"snowflake.topic2table.map\":\"'$KAFKA_TOPIC:$NAME'\"\n    }'\n```\n\nVerify the connector was created and is running in the [Redpanda console](http://localhost:8080).\n\nTo start, lets push in one message to get the table created and verify the connector is working.\n\nRun the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_BATCH\npython ./data_generator.py 1 | python ./publish_data.py\n```\n\nA table named LIFT_TICKETS_KAFKA_BATCH should be created in your account.\n\n```sql\nSELECT get_ddl('table', 'LIFT_TICKETS_KAFKA_BATCH');\n```\n\nThere should be 1 row of data which was created by the data_generator. Note: This can take a minute or so to the flush times in configuration.\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_KAFKA_BATCH;\n```\n\nAfter this is verified to be successful, send in all your test data.\n\nRun the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_BATCH\ncat data.json.gz | zcat | python ./publish_data.py\n```\n\n#### Tips\n\n* Every partition will flush to a file when the bytes, time, or records is hit. This can create a LOT of tiny files if not configured well which will be inefficient.\n\n* Not all workloads can accommodate quick flush times. The more data that is flowing, the quicker data can be visible while being efficient.\n\n* Reducing the number of partitions and increasing the bytes, time, records to get to well sized files is valuable for efficiency.\n\n* If you don't have time or a use case to get to well sized files, move to streaming which will match or be better in all cases.\n\n* Number of tasks, number of nodes in the Kafka Connect cluster, amount of CPU and memory on those nodes, and number of partitions will affect performance and credit consumption.\n\n* Kafka Connector for Snowflake is billed by the second of compute needed to ingest files (Snowpipe).\n\n### From Kafka - in Snowpipe Streaming mode\n\nConfigure and install a new connector to load data in streaming mode:\n\nRun the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING\neval $(cat .env)\n\nURL=\"https://$SNOWFLAKE_ACCOUNT.snowflakecomputing.com\"\nNAME=\"LIFT_TICKETS_KAFKA_STREAMING\"\n\ncurl -i -X PUT -H \"Content-Type:application/json\" \\\n    \"http://localhost:8083/connectors/$NAME/config\" \\\n    -d '{\n        \"connector.class\":\"com.snowflake.kafka.connector.SnowflakeSinkConnector\",\n        \"errors.log.enable\":\"true\",\n        \"snowflake.database.name\":\"INGEST\",\n        \"snowflake.private.key\":\"'$PRIVATE_KEY'\",\n        \"snowflake.schema.name\":\"INGEST\",\n        \"snowflake.role.name\":\"INGEST\",\n        \"snowflake.url.name\":\"'$URL'\",\n        \"snowflake.user.name\":\"'$SNOWFLAKE_USER'\",\n        \"snowflake.enable.schematization\": \"FALSE\",\n        \"snowflake.ingestion.method\": \"SNOWPIPE_STREAMING\",\n        \"topics\":\"'$KAFKA_TOPIC'\",\n        \"name\":\"'$NAME'\",\n        \"value.converter\":\"org.apache.kafka.connect.json.JsonConverter\",\n        \"value.converter.schemas.enable\":\"false\",\n        \"buffer.count.records\":\"1000000\",\n        \"buffer.flush.time\":\"10\",\n        \"buffer.size.bytes\":\"250000000\",\n        \"snowflake.topic2table.map\":\"'$KAFKA_TOPIC:LIFT_TICKETS_KAFKA_STREAMING'\"\n    }'\n```\n\nVerify the connector was created and is running in the [Redpanda console](http://localhost:8080).\n\nThis configuration will allow data flowing through the connector to flush much quicker. The flush time is set to 10 seconds. Previously, it was often discussed how important file sizes were. That was because the files were directly impacting the efficient use of a warehouse. Streaming removes this complexity completely.\n\nData can be loaded in small pieces and will be merged together in the background efficiently by Snowflake. What is even better is that data is immediately available to query before it's merged. All use cases tested have shown Streaming to be as or MORE efficient than the previous Snowpipe only configuration.\n\nRun the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING\npython ./data_generator.py 1 | python ./publish_data.py\n```\n\nA table named LIFT_TICKETS_KAFKA_STREAMING should be created in your account.\n\n```sql\nSELECT get_ddl('table', 'LIFT_TICKETS_KAFKA_STREAMING');\n```\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING\ncat data.json.gz | zcat | python ./publish_data.py\n```\n\nQuery the table to verify the data was inserted. Data will appear in the table in seconds!\n```sql\nSELECT count(*) FROM LIFT_TICKETS_KAFKA_STREAMING;\n```\n\n#### Tips\n\n* Kafka Connector for Snowflake in Streaming mode is billed by the second of compute needed to merge files as well as the clients connected.\n\n* Setting the flush time lower can/will affect query performance as merge happens asynchronously.\n\n* Number of tasks, number of nodes in the Kafka Connect cluster, amount of CPU and memory on those nodes, and number of partitions will affect performance and credit consumption.\n\n* Streaming is the best ingest pattern when using Kafka.\n\n### From Kafka - Streaming with Schematization\n\nThe previous methods for loading data from Kafka landed the data in a Variant field. While this is flexible, it is not the most user friendly or performant way to land data. The Snowflake Connector for Kafka can use schematization to maintain the schema of the landed data.\n\nConfigure and install a new connector to load data in streaming mode WITH schematization:\n\nRun the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\neval $(cat .env)\n\nURL=\"https://$SNOWFLAKE_ACCOUNT.snowflakecomputing.com\"\nNAME=\"LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\"\n\ncurl -i -X PUT -H \"Content-Type:application/json\" \\\n    \"http://localhost:8083/connectors/$NAME/config\" \\\n    -d '{\n        \"connector.class\":\"com.snowflake.kafka.connector.SnowflakeSinkConnector\",\n        \"errors.log.enable\":\"true\",\n        \"snowflake.database.name\":\"INGEST\",\n        \"snowflake.private.key\":\"'$PRIVATE_KEY'\",\n        \"snowflake.schema.name\":\"INGEST\",\n        \"snowflake.role.name\":\"INGEST\",\n        \"snowflake.url.name\":\"'$URL'\",\n        \"snowflake.user.name\":\"'$SNOWFLAKE_USER'\",\n        \"snowflake.enable.schematization\": \"TRUE\",\n        \"snowflake.ingestion.method\": \"SNOWPIPE_STREAMING\",\n        \"topics\":\"'$KAFKA_TOPIC'\",\n        \"name\":\"'$NAME'\",\n        \"value.converter\":\"org.apache.kafka.connect.json.JsonConverter\",\n        \"value.converter.schemas.enable\":\"false\",\n        \"buffer.count.records\":\"1000000\",\n        \"buffer.flush.time\":\"10\",\n        \"buffer.size.bytes\":\"250000000\",\n        \"snowflake.topic2table.map\":\"'$KAFKA_TOPIC:LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED'\"\n    }'\n```\n\nVerify the connector was created and is running in the Redpanda console.\n\nRun the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\npython ./data_generator.py 1 | python ./publish_data.py\n```\n\nA table named LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED should be created in your account.\n\n```sql\nSELECT get_ddl('table', 'LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED');\n```\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\nexport KAFKA_TOPIC=LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED\ncat data.json.gz | zcat | python ./publish_data.py\n```\n\nThe data will land in a table the Connector creates with the schema based on the payload. To see the table and data, run the following SQL:\n\n```sql\nSELECT * FROM LIFT_TICKETS_KAFKA_STREAMING_SCHEMATIZED;\n```\n\n## From Java SDK - Using the Snowflake Ingest Service\n\nMany developers want to be able to directly stream data into Snowflake (without Kafka). In order to do so, Snowflake has a Java SDK.\n\nFirst, create a table for data to be insert into:\n```sql\nUSE ROLE INGEST;\nCREATE OR REPLACE TABLE LIFT_TICKETS_JAVA_STREAMING (TXID varchar(255), RFID varchar(255), RESORT varchar(255), PURCHASE_TIME datetime, EXPIRATION_TIME date, DAYS number, NAME varchar(255), ADDRESS variant, PHONE varchar(255), EMAIL varchar(255), EMERGENCY_CONTACT variant);\n```\n\nThe easiest way to get the sdk working is to use maven for all the dependencies.\n\nCreate a file pom.xml with the following contents\n```xml\n\u003C?xml version=\"1.0\" encoding=\"UTF-8\"?\u003E\n\n\u003Cproject xmlns=\"http://maven.apache.org/POM/4.0.0\"\n  xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\"\n  xsi:schemaLocation=\"http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd\"\u003E\n  \u003CmodelVersion\u003E4.0.0\u003C/modelVersion\u003E\n\n  \u003CgroupId\u003Ecom.snowflake.streaming.app\u003C/groupId\u003E\n  \u003CartifactId\u003Ejava-streaming\u003C/artifactId\u003E\n  \u003Cversion\u003E1.0-SNAPSHOT\u003C/version\u003E\n\n  \u003Cname\u003Ejava-streaming\u003C/name\u003E\n  \u003C!-- FIXME change it to the project's website --\u003E\n  \u003Curl\u003Ehttp://www.example.com\u003C/url\u003E\n\n  \u003Cproperties\u003E\n    \u003Cproject.build.sourceEncoding\u003EUTF-8\u003C/project.build.sourceEncoding\u003E\n    \u003Cmaven.compiler.source\u003E1.8\u003C/maven.compiler.source\u003E\n    \u003Cmaven.compiler.target\u003E1.8\u003C/maven.compiler.target\u003E\n  \u003C/properties\u003E\n\n  \u003Cdependencies\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ejunit\u003C/groupId\u003E\n      \u003CartifactId\u003Ejunit\u003C/artifactId\u003E\n      \u003Cversion\u003E4.13.1\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Enet.snowflake\u003C/groupId\u003E\n      \u003CartifactId\u003Esnowflake-ingest-sdk\u003C/artifactId\u003E\n      \u003Cversion\u003E1.1.3\u003C/version\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eio.github.cdimascio\u003C/groupId\u003E\n      \u003CartifactId\u003Edotenv-java\u003C/artifactId\u003E\n      \u003Cversion\u003E2.3.2\u003C/version\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Enet.snowflake\u003C/groupId\u003E\n      \u003CartifactId\u003Esnowflake-jdbc\u003C/artifactId\u003E\n      \u003Cversion\u003E3.13.30\u003C/version\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- String collation--\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ecom.ibm.icu\u003C/groupId\u003E\n      \u003CartifactId\u003Eicu4j\u003C/artifactId\u003E\n      \u003Cversion\u003E70.1\u003C/version\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- jwt token for key pair authentication with GS --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ecom.nimbusds\u003C/groupId\u003E\n      \u003CartifactId\u003Enimbus-jose-jwt\u003C/artifactId\u003E\n      \u003Cversion\u003E9.9.3\u003C/version\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- Jackson for marshalling and unmarshalling JSON --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ecom.fasterxml.jackson.core\u003C/groupId\u003E\n      \u003CartifactId\u003Ejackson-core\u003C/artifactId\u003E\n      \u003Cversion\u003E2.13.1\u003C/version\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- Jackson Databind api --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ecom.fasterxml.jackson.core\u003C/groupId\u003E\n      \u003CartifactId\u003Ejackson-databind\u003C/artifactId\u003E\n      \u003Cversion\u003E2.15.0\u003C/version\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- Apache HTTP Components for actually sending requests over the network --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.apache.httpcomponents\u003C/groupId\u003E\n      \u003CartifactId\u003Ehttpclient\u003C/artifactId\u003E\n      \u003Cversion\u003E4.5.13\u003C/version\u003E\n      \u003Cexclusions\u003E\n        \u003Cexclusion\u003E  \u003C!-- declare the exclusion here --\u003E\n          \u003CgroupId\u003Ecommons-codec\u003C/groupId\u003E\n          \u003CartifactId\u003Ecommons-codec\u003C/artifactId\u003E\n        \u003C/exclusion\u003E\n      \u003C/exclusions\u003E\n    \u003C/dependency\u003E\n\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ecommons-codec\u003C/groupId\u003E\n      \u003CartifactId\u003Ecommons-codec\u003C/artifactId\u003E\n      \u003Cversion\u003E1.15\u003C/version\u003E\n    \u003C/dependency\u003E\n\n\n    \u003C!-- the Async HTTP Client so we can delay execution --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.apache.httpcomponents\u003C/groupId\u003E\n      \u003CartifactId\u003Ehttpasyncclient\u003C/artifactId\u003E\n      \u003Cversion\u003E4.1.2\u003C/version\u003E\n    \u003C/dependency\u003E\n\n\n    \u003C!-- SLF4J api that a client can shim in later --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.slf4j\u003C/groupId\u003E\n      \u003CartifactId\u003Eslf4j-api\u003C/artifactId\u003E\n      \u003Cversion\u003E1.7.21\u003C/version\u003E\n      \u003Cscope\u003Eprovided\u003C/scope\u003E\n    \u003C/dependency\u003E\n\n\n    \u003C!-- JDK logger backend for logging tests --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.slf4j\u003C/groupId\u003E\n      \u003CartifactId\u003Eslf4j-simple\u003C/artifactId\u003E\n      \u003Cversion\u003E1.7.21\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- java.lang.NoClassDefFoundError: javax/xml/bind/JAXBException --\u003E\n    \u003C!-- https://stackoverflow.com/questions/43574426/how-to-resolve-java\n        -lang-noclassdeffounderror-javax-xml-bind-jaxbexception-in-j/48404582--\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ejavax.xml.bind\u003C/groupId\u003E\n      \u003CartifactId\u003Ejaxb-api\u003C/artifactId\u003E\n      \u003Cversion\u003E2.3.1\u003C/version\u003E\n    \u003C/dependency\u003E\n\n\n    \u003C!-- JUnit so that we can make some basic unit tests --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Ejunit\u003C/groupId\u003E\n      \u003CartifactId\u003Ejunit\u003C/artifactId\u003E\n      \u003Cversion\u003E4.13.1\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.powermock\u003C/groupId\u003E\n      \u003CartifactId\u003Epowermock-module-junit4\u003C/artifactId\u003E\n      \u003Cversion\u003E2.0.2\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.mockito\u003C/groupId\u003E\n      \u003CartifactId\u003Emockito-core\u003C/artifactId\u003E\n      \u003Cversion\u003E3.7.7\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.powermock\u003C/groupId\u003E\n      \u003CartifactId\u003Epowermock-api-mockito2\u003C/artifactId\u003E\n      \u003Cversion\u003E2.0.2\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.powermock\u003C/groupId\u003E\n      \u003CartifactId\u003Epowermock-core\u003C/artifactId\u003E\n      \u003Cversion\u003E2.0.2\u003C/version\u003E\n      \u003Cscope\u003Etest\u003C/scope\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- Apache Arrow --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.apache.arrow\u003C/groupId\u003E\n      \u003CartifactId\u003Earrow-vector\u003C/artifactId\u003E\n      \u003Cversion\u003E8.0.0\u003C/version\u003E\n    \u003C/dependency\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eorg.apache.arrow\u003C/groupId\u003E\n      \u003CartifactId\u003Earrow-memory-netty\u003C/artifactId\u003E\n      \u003Cversion\u003E8.0.0\u003C/version\u003E\n      \u003Cscope\u003Eruntime\u003C/scope\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- https://mvnrepository.com/artifact/io.dropwizard.metrics/metrics-core --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eio.dropwizard.metrics\u003C/groupId\u003E\n      \u003CartifactId\u003Emetrics-core\u003C/artifactId\u003E\n      \u003Cversion\u003E4.1.22\u003C/version\u003E\n    \u003C/dependency\u003E\n\n    \u003C!-- https://mvnrepository.com/artifact/io.dropwizard.metrics/metrics-jvm --\u003E\n    \u003Cdependency\u003E\n      \u003CgroupId\u003Eio.dropwizard.metrics\u003C/groupId\u003E\n      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    \u003C/plugin\u003E\n        \u003C!-- default lifecycle, jar packaging: see\n        https://maven.apache.org/ref/current/maven-core/default-bindings.html#Plugin_bindings_for_jar_packaging --\u003E\n        \u003Cplugin\u003E\n          \u003CartifactId\u003Emaven-resources-plugin\u003C/artifactId\u003E\n          \u003Cversion\u003E3.0.2\u003C/version\u003E\n        \u003C/plugin\u003E\n        \u003Cplugin\u003E\n          \u003CartifactId\u003Emaven-compiler-plugin\u003C/artifactId\u003E\n          \u003Cversion\u003E3.8.0\u003C/version\u003E\n        \u003C/plugin\u003E\n        \u003Cplugin\u003E\n          \u003CartifactId\u003Emaven-surefire-plugin\u003C/artifactId\u003E\n          \u003Cversion\u003E2.22.1\u003C/version\u003E\n        \u003C/plugin\u003E\n        \u003Cplugin\u003E\n          \u003CartifactId\u003Emaven-jar-plugin\u003C/artifactId\u003E\n          \u003Cversion\u003E3.0.2\u003C/version\u003E\n        \u003C/plugin\u003E\n        \u003Cplugin\u003E\n        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\u003C/plugins\u003E\n    \u003C/pluginManagement\u003E\n  \u003C/build\u003E\n\u003C/project\u003E\n```\n\nCreate a directory structure src/main/java/com/snowflake/streaming/app inside your project directory and create a file called App.java inside the app directory.\n\nAdd the following code to App.java. This code will take the records from standard in like previous patterns and stream the data to Snowflake.\n\n```java\npackage com.snowflake.streaming.app;\n\nimport java.io.*;\nimport java.util.Map;\nimport java.util.Properties;\n\nimport io.github.cdimascio.dotenv.Dotenv;\nimport com.fasterxml.jackson.databind.ObjectMapper;\nimport org.slf4j.Logger;\nimport org.slf4j.LoggerFactory;\nimport net.snowflake.ingest.streaming.InsertValidationResponse;\nimport net.snowflake.ingest.streaming.SnowflakeStreamingIngestChannel;\nimport net.snowflake.ingest.streaming.SnowflakeStreamingIngestClient;\nimport net.snowflake.ingest.streaming.SnowflakeStreamingIngestClientFactory;\nimport net.snowflake.ingest.streaming.OpenChannelRequest;\n\npublic class App {\n    private static final Logger LOGGER = LoggerFactory.getLogger(App.class.getName());\n\n    public static void main(String[] args) throws Exception {\n        Dotenv dotenv = Dotenv.configure().load();\n        BufferedReader br = new BufferedReader(new InputStreamReader(System.in));\n        Properties props = new Properties();\n        props.put(\"user\", dotenv.get(\"SNOWFLAKE_USER\"));\n        props.put(\"url\", \"https://\" + dotenv.get(\"SNOWFLAKE_ACCOUNT\") + \".snowflakecomputing.com:443\");\n        props.put(\"private_key\", dotenv.get(\"PRIVATE_KEY\"));\n        props.put(\"role\", \"INGEST\");\n\n        try (SnowflakeStreamingIngestClient client = SnowflakeStreamingIngestClientFactory.builder(\"MY_CLIENT\")\n                .setProperties(props).build()) {\n            OpenChannelRequest request1 = OpenChannelRequest.builder(\"MY_CHANNEL\")\n                    .setDBName(\"INGEST\")\n                    .setSchemaName(\"INGEST\")\n                    .setTableName(\"LIFT_TICKETS_JAVA_STREAMING\")\n                    .setOnErrorOption(\n                            OpenChannelRequest.OnErrorOption.ABORT)\n                    .build();\n\n            SnowflakeStreamingIngestChannel channel1 = client.openChannel(request1);\n            String line = br.readLine();\n            int val = 0;\n            while (line != null && line.length() \u003E 0) {\n                ObjectMapper mapper = new ObjectMapper();\n                Map\u003CString, Object\u003E map = mapper.readValue(line, Map.class);\n\n                InsertValidationResponse response = channel1.insertRow(map, String.valueOf(val));\n                if (response.hasErrors()) {\n                    System.out.println(response.getInsertErrors().get(0).getException());\n                }\n\n                line = br.readLine();\n                val++;\n            }\n            LOGGER.info(\"Ingest complete\");\n            channel1.close().get();\n        }\n    }\n}\n```\n\nTo build and test this code run the following in your shell:\n\n```bash\nmvn install\nmvn dependency:copy-dependencies\nmvn package\n\npython ./data_generator.py 1 | java -cp \"target/java-streaming-1.0-SNAPSHOT.jar:target/dependency/*\" -Dorg.slf4j.simpleLogger.defaultLogLevel=error com.snowflake.streaming.app.App\n```\n\nQuery the table to verify the data was inserted. Data will appear in the table in seconds!\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_JAVA_STREAMING;\n```\n\nTo send in all your test data, you can run the following in your shell:\n\n```bash\ncat data.json.gz | zcat | java -cp \"target/java-streaming-1.0-SNAPSHOT.jar:target/dependency/*\" -Dorg.slf4j.simpleLogger.defaultLogLevel=error com.snowflake.streaming.app.App\n```\n\n```sql\nSELECT count(*) FROM LIFT_TICKETS_JAVA_STREAMING;\n```\n\n### Tips\n\n* Ingest with streaming is billed by the second of compute needed to merge files as well as the clients connected.\n\n* Number of nodes/threads running the Java SDK will affect performance and credit consumption\n\n* Best ingest pattern when not using Kafka and are processing streaming data\n\n## Cleanup\n\nIn order to cleanup from all the ingest patterns built, you can drop the USER, ROLE, DATABASE, and WAREHOUSE:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nDROP USER INGEST;\nDROP DATABASE INGEST;\nDROP WAREHOUSE INGEST;\nDROP ROLE INGEST;\n```\n\nTo tear down docker, run the following in your shell:\n\n```bash\ndocker compose down\n```\n\nTo delete the conda env, run the following in your shell:\n\n```bash\nconda deactivate\nconda remove -n sf-ingest-examples --all\n```\n\n## Conclusion And Resources\n\nAs you've seen, there are many ways to load data into Snowflake. It is important to understand the benefits and consequenses so you can make the right choice when ingesting data into Snowflake. \n\nWhile some examples only focussed on the Python connector, these patterns are often applicable to our other connectors if your language of choice is not Python. Connectors are available for Python, Java, Node.js, Go, .NET, and PHP. Note that based on the load times, batch size would be worth tuning.\n\nServerless Tasks, Snowpipe, and Streaming are all built on Snowflake's serverless compute which make it much simpler to have efficient utilization of infrastructure. Managing warehouses and keeping them fully loaded is not easy or even possible in many cases.\n\nIf you're using the Kafka connector for Snowflake, put it in Streaming mode. It will either be the same or less credit consumption AND make the data available more quickly.\n\nWhen well-sized batches are not possible, leveraging our Streaming ingest will significantly increase efficiency. We will merge those tiny batches together in Snowflake later in a very efficient workflow while making that data available for query quickly.\n\n### What You Learned\n- How to Ingest data with Connectors\n- Using Serverless Tasks and Snowpipe to save credit consumption\n- How to Use the Kafka Connectors for Snowflake\n- How Streaming reduces the time to Ingest AND Increases Efficiency\n\n### Related Resources\n- [Snowflake Connector for Python](https://docs.snowflake.com/en/developer-guide/python-connector/python-connector)\n- [Java SDK for the Snowflake Ingest Service](https://github.com/snowflakedb/snowflake-ingest-java)\n- [Python Snowflake Ingest Service SDK](https://github.com/snowflakedb/snowflake-ingest-python)\n- [Getting Started with Snowpipe](/en/developers/guides/getting-started-with-snowpipe/)\n- [Getting Started with Snowpipe Streaming and Amazon MSK](/en/developers/guides/getting-started-with-snowpipe-streaming-aws-msk/)\n- [Streaming Data Integration with Snowflake](/en/developers/guides/data-engineering-streaming-integration/)\n\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"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d3107b58ff","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-17e2fca3c1",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-5c370ede32","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a1644007ba","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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