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Python Connector 실행을 시작한 다음 이를 사용하여 수행할 수 있는 기본 작업에 대해 알아보겠습니다. Python Connector는 꽤나 강력하며 Pandas DataFrames와의 통합도 지원합니다.\u003C/p\u003E\n","\u003Ch3\u003E사전 필요 조건 및 지식\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPython에 대한 이해\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E학습할 내용\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPython Connector 설정 방법\u003C/li\u003E\u003Cli\u003E설치 테스트 방법\u003C/li\u003E\u003Cli\u003ESnowflake 연결 방법\u003C/li\u003E\u003Cli\u003E세션 매개 변수 설정 방법\u003C/li\u003E\u003Cli\u003E웨어하우스 생성 방법\u003C/li\u003E\u003Cli\u003E데이터베이스 생성 방법\u003C/li\u003E\u003Cli\u003E스키마 생성 방법\u003C/li\u003E\u003Cli\u003E테이블 생성 방법\u003C/li\u003E\u003Cli\u003E데이터 삽입 방법\u003C/li\u003E\u003Cli\u003E데이터 쿼리 방법\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E필요한 것\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"/\"\u003ESnowflake\u003C/a\u003E 계정\u003C/li\u003E\u003Cli\u003E텍스트 편집기\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E구축할 것\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake Python Connector 사용 예\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPython Connector 설정\u003C/h2\u003E\n","\u003Cp\u003E이 가이드에서는 Python 3을 사용하겠습니다. 여러분의 시스템에 있는 Python의 버전을 확인하겠습니다. 터미널을 열고 다음 명령을 입력하여 확인합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Epython --version\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E출력이 3.5 이상이라면 시작할 준비가 되었습니다! 아니라면 최신 버전의 Python을 설치해야 합니다. 최신 릴리스는 Python 웹 사이트에서 찾을 수 있습니다.\u003C/p\u003E\n","\u003Cp\u003E최신 Python 버전을 다운로드했다면 Python용 Snowflake Connector를 설치할 수 있습니다. Python 패키지 설치 프로그램인 \u003Ccode\u003Epip\u003C/code\u003E를 사용하고 다음 명령을 실행하여 설치하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Epip install --upgrade snowflake-connector-python\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELinux 배포판을 사용하고 있다면 여러분의 배포 리포지토리에 몇몇 패키지를 설치해야 합니다. 특히나 다음과 동등한 수준이 필요합니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Elibm-devel\u003C/li\u003E\u003Cli\u003Eopenssl-devel\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EPython과 Snowflake Connector가 모두 설치되었다면 시작할 준비가 되었습니다! 설치되었는지 확인해 보겠습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E설치 테스트\u003C/h2\u003E\n","\u003Cp\u003ESnowflake 커넥터 \u003Cem\u003E사용\u003C/em\u003E을 자세히 알아보기 전에 올바르게 설치되었는지 확인하겠습니다. 다음 스크립트를 통해 확인할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E#!/usr/bin/env python\nimport snowflake.connector\n\n# Gets the version\nctx = snowflake.connector.connect(\n    user='<your_user_name>',\n    password='<your_password>',\n    account='<your_account_name>'\n    )\ncs = ctx.cursor()\ntry:\n    cs.execute("SELECT current_version()")\n    one_row = cs.fetchone()\n    print(one_row[0])\nfinally:\n    cs.close()\nctx.close()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E텍스트 편집기를 열고 해당 스크립트를 복사한 다음 \u003Ccode\u003Evalidate.py\u003C/code\u003E로 저장합니다. 여러분의 사용자 이름, 암호 및 계정을 해당하는 필드에 입력해야 합니다.\u003C/p\u003E\n","\u003Cp\u003E파일이 저장된 위치에서 터미널을 열고 다음 명령을 실행합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Epython validate.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E모든 것이 성공적이라면 설치된 Snowflake 버전이 나타나야 합니다. 그렇지 않으면 여러분의 상황에 해당하는 오류가 나타납니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake 연결\u003C/h2\u003E\n","\u003Cp\u003EPython용 Snowflake 커넥터를 사용할 시간입니다. Python 환경을 엽니다. 우선적으로 해야 하는 일은 Snowflake 커넥터 모듈을 가져오는 것입니다. 어떠한 Snowflake 관련 명령을 사용하기 전에 이를 가져와야 합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eimport snowflake.connector\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E환경 변수와 같은 외부 소스에서 여러분의 로그인 정보를 읽는 것을 고려해 보십시오. 이는 여러분의 스크립트에 추가적인 보안을 제공하며 장기적으로 시간을 절약합니다. 이 예에서는 \u003Ccode\u003Eos.getenv\u003C/code\u003E를 사용하여 \u003Ccode\u003EPASSWORD.\u003C/code\u003E 변수를 위한 \u003Ccode\u003ESNOWSQL_PWD\u003C/code\u003E 환경 변수를 가져오겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EPASSWORD = os.getenv('SNOWSQL_PWD')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E다음과 같이 Python Connector 내에서 이러한 변수를 사용하게 됩니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn = snowflake.connector.connect(\n    user=USER,\n    password=PASSWORD,\n    account=ACCOUNT\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E좋습니다! 이제 세부 사항을 자세히 알아보겠습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E세션 매개 변수 설정\u003C/h2\u003E\n","\u003Cp\u003E세션 매개 변수를 설정하여 여러분의 세션을 조정하고 여러분이 원하는 대로 설정할 수 있습니다. 2가지 방식으로 설정할 수 있습니다. 우선 처음 연결할 때 다음과 같이 설정할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn = snowflake.connector.connect(\n    user='XXXX',\n    password='XXXX',\n    account='XXXX',\n    session_parameters={\n        'QUERY_TAG': 'EndOfMonthFinancials',\n    }\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E아니면 연결한 다음 \u003Ccode\u003EALTER SESSION SET\u003C/code\u003E SQL 문을 실행하여 설정할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("ALTER SESSION SET QUERY_TAG = 'EndOfMonthFinancials'")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E강력한 보안 방법을 사용하기 위해 Snowflake 기능의 이점을 누리고 있으셨을 수 있습니다. 그렇다면 추가적인 단계를 밟아 Snowflake Python Connector를 구성하는 것이 좋습니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/admin-security-fed-auth-use.html#label-sso-with-command-line-clients\"\u003ESingle Sign-on (SSO)\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/python-connector-example.html#using-key-pair-authentication\"\u003E키 쌍 인증\u003C/a\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/python-connector-example.html#key-rotation\"\u003E키 회전\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/oauth-intro.html#label-oauth-intro-clients-drivers-conns\"\u003EOAuth\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E이 시점에는 Snowflake 내에서 오브젝트 조작을 시작할 수 있습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E웨어하우스 생성\u003C/h2\u003E\n","\u003Cp\u003ESnowflake와 상호 작용하기 위한 SQL 명령에 익숙하다면 Python Connector 내 명령에도 익숙하실 것입니다. 명령이 어떻게 구성되어 있는지 분석해 보겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003Econn\u003C/code\u003E은 여러분을 여러분의 Snowflake 계정과 연결하는 오브젝트임을 기억하십시오. 이를 사용하여 다음 형식을 가진 SQL 명령을 실행할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("YOUR SQL COMMAND")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E우선 \u003Ca href=\"https://docs.snowflake.com/ko/user-guide/warehouses-overview.html\"\u003E가상 웨어하우스\u003C/a\u003E를 생성하겠습니다. 가상 웨어하우스에는 Snowflake를 통해 쿼리와 DML 작업을 수행하기 위해 필요한 서버가 포함되어 있습니다. \u003Ccode\u003ECREATE WAREHOUSE\u003C/code\u003E SQL 명령을 통합하여 하나의 가상 웨어하우스를 생성할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("CREATE WAREHOUSE IF NOT EXISTS tiny_warehouse_mg")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003ECREATE WAREHOUSE\u003C/code\u003E 명령은 예상대로 웨어하우스를 생성하지만 암시적으로 해당 웨어하우스를 여러분의 세션을 위한 활성 웨어하우스로 설정하기도 합니다. 이미 웨어하우스를 생성했다면 \u003Ccode\u003EUSE WAREHOUSE\u003C/code\u003E 명령을 사용하여 명시적으로 이를 활성 웨어하우스로 설정할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("USE WAREHOUSE tiny_warehouse_mg")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E이제 웨어하우스가 있으니 데이터베이스를 다뤄 보겠습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E데이터베이스 생성\u003C/h2\u003E\n","\u003Cp\u003E다음 단계는 데이터베이스 생성입니다. 데이터베이스는 여러분의 데이터베이스 오브젝트가 포함되어 있는 스키마를 포함합니다. 웨어하우스를 생성했을 때와 동일한 방식으로 데이터베이스를 생성할 수 있습니다. 다만 이번에는 \u003Ccode\u003ECREATE DATABASE\u003C/code\u003E 명령을 사용하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("CREATE DATABASE IF NOT EXISTS testdb")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E데이터베이스 생성 또한 해당 데이터베이스를 현재 세션의 활성 데이터베이스로 설정합니다. 이미 생성된 데이터베이스를 세션을 위한 활성 데이터베이스로 설정해야 한다면 \u003Ccode\u003EUSE DATABASE\u003C/code\u003E 명령을 사용합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("USE DATABASE testdb")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E그런 다음 스키마를 생성해야 합니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E스키마 생성\u003C/h2\u003E\n","\u003Cp\u003E스키마는 여러분의 데이터베이스 오브젝트의 그룹화입니다. 스키마에는 여러분의 테이블, 테이블에 있는 데이터 및 뷰가 포함되어 있습니다. 스키마는 데이터베이스 내에 있습니다. \u003Ccode\u003ECREATE SCHEMA\u003C/code\u003E 명령으로 스키마를 생성할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("CREATE SCHEMA IF NOT EXISTS testschema")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003ECREATE SCHEMA\u003C/code\u003E는 또한 여러분의 세션을 위해 이를 활성 스키마로 설정하기에 명시적으로 \u003Ccode\u003EUSE SCHEMA\u003C/code\u003E 또한 호출할 필요가 없습니다. 이미 생성된 스키마를 사용하고 싶을 때만 사용하십시오.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("USE SCHEMA testschema")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E기본값으로 스키마는 현재 데이터베이스에서 사용됩니다. 또 다른 데이터베이스를 지정하여 이러한 다른 데이터베이스에서 사용할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("USE SCHEMA otherdb.testschema")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E스키마를 사용하고 있으니 테이블에 대해 알아볼 시간입니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E테이블 생성\u003C/h2\u003E\n","\u003Cp\u003E웨어하우스, 데이터베이스 및 스키마를 생성했으니 데이터에 있는 데이터 조작에 필요한 모든 것이 준비되었습니다. 우선 테이블을 생성해야 합니다. \u003Ccode\u003ECREATE TABLE\u003C/code\u003E 명령을 통해 생성합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute(\n    "CREATE OR REPLACE TABLE "\n    "test_table(col1 integer, col2 string)")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Econn.cursor().execute\u003C/code\u003E 내에서 이 예는 2개의 열을 가진 \u003Ccode\u003Etest_table\u003C/code\u003E라는 테이블을 생성합니다. \u003Ccode\u003Ecol1\u003C/code\u003E이라는 열에는 정수가 포함되며 \u003Ccode\u003Ecol2\u003C/code\u003E이라는 열에는 문자열이 포함됩니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E데이터 삽입\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Etest_table\u003C/code\u003E 테이블을 생성했으니 여기에 데이터를 추가할 수 있습니다. \u003Ccode\u003EINSERT\u003C/code\u003E 명령으로 추가할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute(\n    "INSERT INTO test_table(col1, col2) "\n    "VALUES(123, 'test string1'),(456, 'test string2')")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E이 명령은 행별로 데이터를 \u003Ccode\u003Etest_table\u003C/code\u003E에 삽입합니다. 첫 열과 첫 행에서 찾은 값은 '123' 등과 같습니다.\u003C/p\u003E\n","\u003Cp\u003E그러나 행별로 데이터를 수동으로 삽입하고 싶지 않다면 대신 데이터를 로드할 수 있습니다. 이는 \u003Ccode\u003EPUT\u003C/code\u003E과 \u003Ccode\u003ECOPY INTO\u003C/code\u003E 명령의 조합으로 가능합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econn.cursor().execute("PUT file:///tmp/data/file* @%test_table")\nconn.cursor().execute("COPY INTO test_table")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E여기에서 \u003Ccode\u003EPUT\u003C/code\u003E 명령은 파일을 스테이징하고, \u003Ccode\u003ECOPY INTO\u003C/code\u003E 명령은 해당 데이터를 파일에서 복사하여 지정된 테이블로 복사합니다. 또한 \u003Ccode\u003ECOPY INTO\u003C/code\u003E 명령을 사용하여 데이터를 외부 위치에서 복사할 수 있습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E데이터 쿼리\u003C/h2\u003E\n","\u003Cp\u003E당연히 언젠가 여러분의 데이터를 쿼리하고 싶을 것입니다. Python Connector 내에서 이를 쉽게 진행할 수 있습니다. \u003Ccode\u003Eprint\u003C/code\u003E 명령으로 쉽게 테이블에서 가져온 값을 확인할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Ecol1, col2 = conn.cursor().execute("SELECT col1, col2 FROM test_table").fetchone()\nprint('{0}, {1}'.format(col1, col2))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E이 코드 조각은 \u003Ccode\u003ESELECT col1, col2 FROM test_table\u003C/code\u003E SQL 명령을 사용하여 특정 열을 선택한 다음 각각의 행 또는 첫 행에 있는 첫 값을 출력합니다.\u003C/p\u003E\n","\u003Cp\u003E비슷한 방식으로 전체 열을 출력할 수 있습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efor (col1, col2) in conn.cursor().execute("SELECT col1, col2 FROM test_table"):\n print('{0}, {1}'.format(col1, col2))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E여러분의 리소스를 효율적으로 사용하려면 쿼리를 수행한 후 명시적으로 Snowflake와의 연결을 종료하는 것이 좋습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Econnection.close()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E보십시오! 이제 Snowflake 내에서 데이터를 조작하기 위해 필요한 모든 단계를 구현했습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E다음 단계: Python으로 고급 조작\u003C/h2\u003E\n","\u003Cp\u003E이 단계에서는 Python Connector 사용의 기본 사항을 파악하셨을 것입니다. 알아차리셨을 수도 있지만 이는 이미 확립된 Snowflake SQL 명령에 상당히 의존합니다. 당연히 Connector로 수행할 수 있는 작업은 더 많습니다. 여러분의 사용 사례에 따라 관심을 가지실 만한 잠재적인 다음 단계는 다음과 같습니다.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/python-connector-example.html#binding-data\"\u003E데이터 바인딩\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/python-connector-pandas.html\"\u003EPandas Dataframes 사용\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/ko/user-guide/sqlalchemy.html\"\u003ESnowflake SQLAlchemy 도구 키트\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E이는 Snowflake로 수행할 수 있는 작업의 극히 일부입니다. 더 자세한 내용은 \u003Ca href=\"https://docs.snowflake.com/ko/\"\u003ESnowflake 설명서\u003C/a\u003E를 확인하십시오.\u003C/p\u003E"],":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## 개요\r\n\r\n\r\n다양한 언어로 Snowflake와 연결할 수 있습니다. 선택한 언어가 Python이라면 Snowflake와의 연결을 여기에서 시작하는 것이 좋습니다. Python Connector 실행을 시작한 다음 이를 사용하여 수행할 수 있는 기본 작업에 대해 알아보겠습니다. Python Connector는 꽤나 강력하며 Pandas DataFrames와의 통합도 지원합니다.\r\n\r\n### 사전 필요 조건 및 지식\r\n\r\n- Python에 대한 이해\r\n\r\n### 학습할 내용\r\n\r\n- Python Connector 설정 방법\r\n- 설치 테스트 방법\r\n- Snowflake 연결 방법\r\n- 세션 매개 변수 설정 방법\r\n- 웨어하우스 생성 방법\r\n- 데이터베이스 생성 방법\r\n- 스키마 생성 방법\r\n- 테이블 생성 방법\r\n- 데이터 삽입 방법\r\n- 데이터 쿼리 방법\r\n\r\n### 필요한 것\r\n\r\n- [Snowflake](/) 계정\r\n- 텍스트 편집기\r\n\r\n### 구축할 것\r\n\r\n- Snowflake Python Connector 사용 예\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Python Connector 설정\r\n\r\n\r\n이 가이드에서는 Python 3을 사용하겠습니다. 여러분의 시스템에 있는 Python의 버전을 확인하겠습니다. 터미널을 열고 다음 명령을 입력하여 확인합니다.\r\n\r\n```\r\npython --version\r\n```\r\n\r\n출력이 3.5 이상이라면 시작할 준비가 되었습니다! 아니라면 최신 버전의 Python을 설치해야 합니다. 최신 릴리스는 Python 웹 사이트에서 찾을 수 있습니다.\r\n\r\n최신 Python 버전을 다운로드했다면 Python용 Snowflake Connector를 설치할 수 있습니다. Python 패키지 설치 프로그램인 `pip`를 사용하고 다음 명령을 실행하여 설치하겠습니다.\r\n\r\n```\r\npip install --upgrade snowflake-connector-python\r\n```\r\n\r\nLinux 배포판을 사용하고 있다면 여러분의 배포 리포지토리에 몇몇 패키지를 설치해야 합니다. 특히나 다음과 동등한 수준이 필요합니다.\r\n\r\n* libm-devel\r\n* openssl-devel\r\n\r\nPython과 Snowflake Connector가 모두 설치되었다면 시작할 준비가 되었습니다! 설치되었는지 확인해 보겠습니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 설치 테스트\r\n\r\n\r\nSnowflake 커넥터 *사용*을 자세히 알아보기 전에 올바르게 설치되었는지 확인하겠습니다. 다음 스크립트를 통해 확인할 수 있습니다.\r\n\r\n```\r\n#!/usr/bin/env python\r\nimport snowflake.connector\r\n\r\n# Gets the version\r\nctx = snowflake.connector.connect(\r\n    user='\u003Cyour_user_name\u003E',\r\n    password='\u003Cyour_password\u003E',\r\n    account='\u003Cyour_account_name\u003E'\r\n    )\r\ncs = ctx.cursor()\r\ntry:\r\n    cs.execute(\"SELECT current_version()\")\r\n    one_row = cs.fetchone()\r\n    print(one_row[0])\r\nfinally:\r\n    cs.close()\r\nctx.close()\r\n```\r\n\r\n텍스트 편집기를 열고 해당 스크립트를 복사한 다음 `validate.py`로 저장합니다. 여러분의 사용자 이름, 암호 및 계정을 해당하는 필드에 입력해야 합니다.\r\n\r\n파일이 저장된 위치에서 터미널을 열고 다음 명령을 실행합니다.\r\n\r\n```\r\npython validate.py\r\n```\r\n\r\n모든 것이 성공적이라면 설치된 Snowflake 버전이 나타나야 합니다. 그렇지 않으면 여러분의 상황에 해당하는 오류가 나타납니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Snowflake 연결\r\n\r\n\r\nPython용 Snowflake 커넥터를 사용할 시간입니다. Python 환경을 엽니다. 우선적으로 해야 하는 일은 Snowflake 커넥터 모듈을 가져오는 것입니다. 어떠한 Snowflake 관련 명령을 사용하기 전에 이를 가져와야 합니다.\r\n\r\n```\r\nimport snowflake.connector\r\n```\r\n\r\n환경 변수와 같은 외부 소스에서 여러분의 로그인 정보를 읽는 것을 고려해 보십시오. 이는 여러분의 스크립트에 추가적인 보안을 제공하며 장기적으로 시간을 절약합니다. 이 예에서는 `os.getenv`를 사용하여 `PASSWORD.` 변수를 위한 `SNOWSQL_PWD` 환경 변수를 가져오겠습니다.\r\n\r\n```\r\nPASSWORD = os.getenv('SNOWSQL_PWD')\r\n```\r\n\r\n다음과 같이 Python Connector 내에서 이러한 변수를 사용하게 됩니다.\r\n\r\n```\r\nconn = snowflake.connector.connect(\r\n    user=USER,\r\n    password=PASSWORD,\r\n    account=ACCOUNT\r\n    )\r\n```\r\n\r\n좋습니다! 이제 세부 사항을 자세히 알아보겠습니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 세션 매개 변수 설정\r\n\r\n\r\n세션 매개 변수를 설정하여 여러분의 세션을 조정하고 여러분이 원하는 대로 설정할 수 있습니다. 2가지 방식으로 설정할 수 있습니다. 우선 처음 연결할 때 다음과 같이 설정할 수 있습니다.\r\n\r\n```\r\nconn = snowflake.connector.connect(\r\n    user='XXXX',\r\n    password='XXXX',\r\n    account='XXXX',\r\n    session_parameters={\r\n        'QUERY_TAG': 'EndOfMonthFinancials',\r\n    }\r\n)\r\n```\r\n\r\n아니면 연결한 다음 `ALTER SESSION SET` SQL 문을 실행하여 설정할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\"ALTER SESSION SET QUERY_TAG = 'EndOfMonthFinancials'\")\r\n```\r\n\r\n강력한 보안 방법을 사용하기 위해 Snowflake 기능의 이점을 누리고 있으셨을 수 있습니다. 그렇다면 추가적인 단계를 밟아 Snowflake Python Connector를 구성하는 것이 좋습니다.\r\n\r\n* [Single Sign-on (SSO)](https://docs.snowflake.com/ko/user-guide/admin-security-fed-auth-use.html#label-sso-with-command-line-clients)\r\n* [키 쌍 인증](https://docs.snowflake.com/ko/user-guide/python-connector-example.html#using-key-pair-authentication)\r\n  - [키 회전](https://docs.snowflake.com/ko/user-guide/python-connector-example.html#key-rotation)\r\n* [OAuth](https://docs.snowflake.com/ko/user-guide/oauth-intro.html#label-oauth-intro-clients-drivers-conns)\r\n\r\n이 시점에는 Snowflake 내에서 오브젝트 조작을 시작할 수 있습니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 웨어하우스 생성\r\n\r\n\r\nSnowflake와 상호 작용하기 위한 SQL 명령에 익숙하다면 Python Connector 내 명령에도 익숙하실 것입니다. 명령이 어떻게 구성되어 있는지 분석해 보겠습니다.\r\n\r\n`conn`은 여러분을 여러분의 Snowflake 계정과 연결하는 오브젝트임을 기억하십시오. 이를 사용하여 다음 형식을 가진 SQL 명령을 실행할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\"YOUR SQL COMMAND\")\r\n```\r\n\r\n우선 [가상 웨어하우스](https://docs.snowflake.com/ko/user-guide/warehouses-overview.html)를 생성하겠습니다. 가상 웨어하우스에는 Snowflake를 통해 쿼리와 DML 작업을 수행하기 위해 필요한 서버가 포함되어 있습니다. `CREATE WAREHOUSE` SQL 명령을 통합하여 하나의 가상 웨어하우스를 생성할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\"CREATE WAREHOUSE IF NOT EXISTS tiny_warehouse_mg\")\r\n```\r\n\r\n`CREATE WAREHOUSE` 명령은 예상대로 웨어하우스를 생성하지만 암시적으로 해당 웨어하우스를 여러분의 세션을 위한 활성 웨어하우스로 설정하기도 합니다. 이미 웨어하우스를 생성했다면 `USE WAREHOUSE` 명령을 사용하여 명시적으로 이를 활성 웨어하우스로 설정할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\"USE WAREHOUSE tiny_warehouse_mg\")\r\n```\r\n\r\n이제 웨어하우스가 있으니 데이터베이스를 다뤄 보겠습니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 데이터베이스 생성\r\n\r\n\r\n다음 단계는 데이터베이스 생성입니다. 데이터베이스는 여러분의 데이터베이스 오브젝트가 포함되어 있는 스키마를 포함합니다. 웨어하우스를 생성했을 때와 동일한 방식으로 데이터베이스를 생성할 수 있습니다. 다만 이번에는 `CREATE DATABASE` 명령을 사용하겠습니다.\r\n\r\n```\r\nconn.cursor().execute(\"CREATE DATABASE IF NOT EXISTS testdb\")\r\n```\r\n\r\n데이터베이스 생성 또한 해당 데이터베이스를 현재 세션의 활성 데이터베이스로 설정합니다. 이미 생성된 데이터베이스를 세션을 위한 활성 데이터베이스로 설정해야 한다면 `USE DATABASE` 명령을 사용합니다.\r\n\r\n```\r\nconn.cursor().execute(\"USE DATABASE testdb\")\r\n```\r\n\r\n그런 다음 스키마를 생성해야 합니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 스키마 생성\r\n\r\n\r\n스키마는 여러분의 데이터베이스 오브젝트의 그룹화입니다. 스키마에는 여러분의 테이블, 테이블에 있는 데이터 및 뷰가 포함되어 있습니다. 스키마는 데이터베이스 내에 있습니다. `CREATE SCHEMA` 명령으로 스키마를 생성할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\"CREATE SCHEMA IF NOT EXISTS testschema\")\r\n```\r\n\r\n`CREATE SCHEMA`는 또한 여러분의 세션을 위해 이를 활성 스키마로 설정하기에 명시적으로 `USE SCHEMA` 또한 호출할 필요가 없습니다. 이미 생성된 스키마를 사용하고 싶을 때만 사용하십시오.\r\n\r\n```\r\nconn.cursor().execute(\"USE SCHEMA testschema\")\r\n```\r\n\r\n기본값으로 스키마는 현재 데이터베이스에서 사용됩니다. 또 다른 데이터베이스를 지정하여 이러한 다른 데이터베이스에서 사용할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\"USE SCHEMA otherdb.testschema\")\r\n```\r\n\r\n스키마를 사용하고 있으니 테이블에 대해 알아볼 시간입니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 테이블 생성\r\n\r\n\r\n웨어하우스, 데이터베이스 및 스키마를 생성했으니 데이터에 있는 데이터 조작에 필요한 모든 것이 준비되었습니다. 우선 테이블을 생성해야 합니다. `CREATE TABLE` 명령을 통해 생성합니다.\r\n\r\n```\r\nconn.cursor().execute(\r\n    \"CREATE OR REPLACE TABLE \"\r\n    \"test_table(col1 integer, col2 string)\")\r\n```\r\n\r\n`conn.cursor().execute` 내에서 이 예는 2개의 열을 가진 `test_table`라는 테이블을 생성합니다. `col1`이라는 열에는 정수가 포함되며 `col2`이라는 열에는 문자열이 포함됩니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 데이터 삽입\r\n\r\n\r\n`test_table` 테이블을 생성했으니 여기에 데이터를 추가할 수 있습니다. `INSERT` 명령으로 추가할 수 있습니다.\r\n\r\n```\r\nconn.cursor().execute(\r\n    \"INSERT INTO test_table(col1, col2) \"\r\n    \"VALUES(123, 'test string1'),(456, 'test string2')\")\r\n```\r\n\r\n이 명령은 행별로 데이터를 `test_table`에 삽입합니다. 첫 열과 첫 행에서 찾은 값은 '123' 등과 같습니다.\r\n\r\n그러나 행별로 데이터를 수동으로 삽입하고 싶지 않다면 대신 데이터를 로드할 수 있습니다. 이는 `PUT`과 `COPY INTO` 명령의 조합으로 가능합니다.\r\n\r\n```\r\nconn.cursor().execute(\"PUT file:///tmp/data/file* @%test_table\")\r\nconn.cursor().execute(\"COPY INTO test_table\")\r\n```\r\n\r\n여기에서 `PUT` 명령은 파일을 스테이징하고, `COPY INTO` 명령은 해당 데이터를 파일에서 복사하여 지정된 테이블로 복사합니다. 또한 `COPY INTO` 명령을 사용하여 데이터를 외부 위치에서 복사할 수 있습니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 데이터 쿼리\r\n\r\n\r\n당연히 언젠가 여러분의 데이터를 쿼리하고 싶을 것입니다. Python Connector 내에서 이를 쉽게 진행할 수 있습니다. `print` 명령으로 쉽게 테이블에서 가져온 값을 확인할 수 있습니다.\r\n\r\n```\r\ncol1, col2 = conn.cursor().execute(\"SELECT col1, col2 FROM test_table\").fetchone()\r\nprint('{0}, {1}'.format(col1, col2))\r\n```\r\n\r\n이 코드 조각은 `SELECT col1, col2 FROM test_table` SQL 명령을 사용하여 특정 열을 선택한 다음 각각의 행 또는 첫 행에 있는 첫 값을 출력합니다.\r\n\r\n비슷한 방식으로 전체 열을 출력할 수 있습니다.\r\n\r\n```\r\nfor (col1, col2) in conn.cursor().execute(\"SELECT col1, col2 FROM test_table\"):\r\n print('{0}, {1}'.format(col1, col2))\r\n```\r\n\r\n여러분의 리소스를 효율적으로 사용하려면 쿼리를 수행한 후 명시적으로 Snowflake와의 연결을 종료하는 것이 좋습니다.\r\n\r\n```\r\nconnection.close()\r\n```\r\n\r\n보십시오! 이제 Snowflake 내에서 데이터를 조작하기 위해 필요한 모든 단계를 구현했습니다.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## 다음 단계: Python으로 고급 조작\r\n\r\n\r\n이 단계에서는 Python Connector 사용의 기본 사항을 파악하셨을 것입니다. 알아차리셨을 수도 있지만 이는 이미 확립된 Snowflake SQL 명령에 상당히 의존합니다. 당연히 Connector로 수행할 수 있는 작업은 더 많습니다. 여러분의 사용 사례에 따라 관심을 가지실 만한 잠재적인 다음 단계는 다음과 같습니다.\r\n\r\n* [데이터 바인딩](https://docs.snowflake.com/ko/user-guide/python-connector-example.html#binding-data)\r\n* [Pandas Dataframes 사용](https://docs.snowflake.com/ko/user-guide/python-connector-pandas.html)\r\n* [Snowflake SQLAlchemy 도구 키트](https://docs.snowflake.com/ko/user-guide/sqlalchemy.html)\r\n\r\n이는 Snowflake로 수행할 수 있는 작업의 극히 일부입니다. 더 자세한 내용은 [Snowflake 설명서](https://docs.snowflake.com/ko/)를 확인하십시오.","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d409e98bd2","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-b38e2a6f0b",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-7f798ab72c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2023-02-25",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-d70d22fde3","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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