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src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_0_overall_architecture.png?v=dbf9e9fa\" alt=\"architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003E수많은 비즈니스는 민첩성, 성장 및 운영 효율성을 지원할 수 있는 최신 데이터 전략이 탑재된 플랫폼을 고려하고 있습니다. Snowflake는 데이터 클라우드입니다. 이는 인프라 관리와 유지 관리 대신 데이터와 분석에 집중할 수 있도록 모든 비즈니스를 위해 데이터 파이프라인을 간소화할 수 있는 미래 지향적인 솔루션입니다.\u003C/p\u003E\n","\u003Cp\u003EApache Airflow는 데이터 파이프라인을 작성 및 관리하는 데 사용할 수 있는 오픈 소스 워크플로우 관리 플랫폼입니다. Airflow는 작업의 방향성 비순환 그래프(DAG)로 만들어진 워크플로우를 사용합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.getdbt.com/\"\u003Edbt\u003C/a\u003E는 \u003Ca href=\"https://www.getdbt.com/\"\u003Edbt Labs\u003C/a\u003E에서 유지하는 최신 데이터 엔지니어링 프레임워크입니다. Snowflake와 같은 클라우드 데이터 플랫폼을 활용하는 최신 데이터 아키텍처에서 큰 인기를 얻고 있습니다. \u003Ca href=\"https://docs.getdbt.com/dbt-cli/cli-overview\"\u003Edbt CLI\u003C/a\u003E는 dbt 프로젝트를 실행하기 위한 명령줄 인터페이스입니다. CLI는 무료로 사용할 수 있으며 오픈 소스입니다.\u003C/p\u003E\n","\u003Cp\u003E이 가상 실습 랩에서는 데이터 변환 작업 스케줄러를 생성하기 위해 Airflow와 dbt를 사용하는 단계별 가이드를 따라 하게 됩니다.\u003C/p\u003E\n","\u003Cp\u003E시작하겠습니다.\u003C/p\u003E\n","\u003Ch3\u003E사전 필요 조건 및 지식\u003C/h3\u003E\n","\u003Cp\u003E이 가이드는 사용자가 Python 및 dbt에 대한 기본적인 실무 지식을 보유하고 있다고 가정합니다.\u003C/p\u003E\n","\u003Ch3\u003E학습할 내용\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E데이터 스케줄러를 생성하기 위해 Airflow와 같은 오픈 소스 도구 사용하기\u003C/li\u003E\u003Cli\u003EDAG를 작성하고 이를 Airflow에 업로드하기\u003C/li\u003E\u003Cli\u003Edbt, Airflow 및 Snowflake를 사용하여 확장 가능한 파이프라인 구축하기\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E필요한 것\u003C/h3\u003E\n","\u003Cp\u003E시작하기 전에 다음이 필요합니다.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESnowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake 계정\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E적절한 권한을 포함하여 생성된 Snowflake 사용자\u003C/strong\u003E 이 사용자에게는 DEMO_DB 데이터베이스에서 객체를 생성하기 위한 권한이 필요합니다.\u003C/li\u003E\u003Cli\u003EGitHub\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGitHub 계정\u003C/strong\u003E GitHub 계정이 아직 없다면 무료로 생성할 수 있습니다. 시작하려면 \u003Ca href=\"https://github.com/join\"\u003EGitHub 가입\u003C/a\u003E 페이지를 방문하십시오.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGitHub 리포지토리\u003C/strong\u003E 아직 리포지토리를 생성하지 않았거나 새로운 리포지토리를 생성하고 싶다면, \u003Ca href=\"https://github.com/new\"\u003E새로운 리포지토리를 생성합니다\u003C/a\u003E. 유형으로 \u003Ccode\u003EPublic\u003C/code\u003E을 선택합니다(하지만 두 가지 모두 사용할 수 있음). 또한, 지금은 README, .gitignore 및 라이선스 추가를 건너뛸 수 있습니다.\u003C/li\u003E\u003Cli\u003E통합 개발 환경(IDE)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGit과 통합되며 선호하는 IDE\u003C/strong\u003E Git과 통합되며 선호하는 IDE가 아직 없다면, 훌륭한 무료 오픈 소스 IDE인 \u003Ca href=\"https://code.visualstudio.com/\"\u003EVisual Studio Code\u003C/a\u003E를 추천합니다.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E컴퓨터에 프로젝트 리포지토리 복제\u003C/strong\u003E Git 리포지토리에 대한 연결 세부 정보를 위해 리포지토리를 열고 페이지 상단 부근에 있는 \u003Ccode\u003EHTTPS\u003C/code\u003E 링크를 복사합니다. 최소 하나의 파일이 리포지토리에 있다면 페이지 상단 부근에 있는 녹색 \u003Ccode\u003ECode\u003C/code\u003E 아이콘을 클릭하고 \u003Ccode\u003EHTTPS\u003C/code\u003E 링크를 복사합니다. 이 링크를 VS Code 또는 선호하는 IDE에서 사용하여 리포지토리를 컴퓨터에 복제합니다.\u003C/li\u003E\u003Cli\u003EDocker\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E노트북에 Docker Desktop 설치\u003C/strong\u003E  Airflow를 컨테이너로 실행할 것입니다. \u003Ca href=\"https://docs.docker.com/desktop/\"\u003EDocker 설정 지침\u003C/a\u003E에 따라 운영 체제에 Docker Desktop을 설치하십시오.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003E구축할 것\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003Edbt 및 Snowflake와 간단히 작동하는 Airflow 파이프라인\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E환경 설정\u003C/h2\u003E\n","\u003Cp\u003E우선, 아래 명령을 실행하여 폴더를 생성하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Emkdir dbt_airflow && cd "$_"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E다음으로 Airflow의 docker-compose 파일을 가져오겠습니다. 이 작업을 수행하기 위해 로컬 노트북에 대해 파일의 curl 명령을 실행하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecurl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.3.0/docker-compose.yaml'\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E이제 docker-compose 파일을 조정하겠습니다. 2개의 폴더를 볼륨으로 추가합니다. \u003Ccode\u003Edags\u003C/code\u003E는 Airflow가 선택하여 분석할 수 있도록 Airflow DAG가 있는 폴더입니다. \u003Ccode\u003Edbt\u003C/code\u003E는 dbt 모델과 CSV 파일을 구성한 폴더입니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E  volumes:\n    - ./dags:/opt/airflow/dags\n    - ./logs:/opt/airflow/logs\n    - ./plugins:/opt/airflow/plugins\n    - ./dbt:/dbt # add this in\n    - ./dags:/dags # add this in\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E이제 추가 docker-compose 매개변수로 추가 파일을 생성해야 합니다. 이렇게 하면 컨테이너가 시작될 때 dbt가 설치됩니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003E.env\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E_PIP_ADDITIONAL_REQUIREMENTS=dbt==0.19.0\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Cp\u003E이제 \u003Ccode\u003Edbt\u003C/code\u003E 프로젝트와 \u003Ccode\u003Edags\u003C/code\u003E 폴더를 생성해야 합니다.\u003C/p\u003E\n","\u003Cp\u003Edbt 프로젝트에 대해 \u003Ccode\u003Edbt init dbt\u003C/code\u003E 명령을 실행합니다. 추후 4단계에서는 여기에서 dbt를 구성하게 됩니다.\u003C/p\u003E\n","\u003Cp\u003Edags 폴더에 대해 다음 작업을 수행해 폴더를 생성합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Emkdir dags\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E트리 리포지토리는 다음과 같아야 합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_1_tree_structure.png?v=dbf9e9fa\" alt=\"Folderstructure\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt 프로젝트 설정\u003C/h2\u003E\n","\u003Cp\u003E이제 리포지토리를 설정했으니 dbt 프로젝트를 구성 및 설정하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E시작하기 전에 dbt 프로젝트를 위해 어떤 작업을 수행할지 알아보겠습니다.\u003C/p\u003E\n","\u003Cp\u003E아래 다이어그램에서 확인할 수 있듯이 \u003Ccode\u003Ebookings_1\u003C/code\u003E, \u003Ccode\u003Ebookings_2\u003C/code\u003E 및 \u003Ccode\u003Ecustomers\u003C/code\u003E라는 3개의 CSV 파일이 있습니다. 이러한 CSV 파일을 테이블로 Snowflake에 시드하겠습니다. 이 부분은 추후 4단계에서 자세히 살펴볼 것입니다.\u003C/p\u003E\n","\u003Cp\u003E이에 따라 \u003Ccode\u003Ebookings_1\u003C/code\u003E 및 \u003Ccode\u003Ebookings_2\u003C/code\u003E 테이블을 \u003Ccode\u003Ecombined_bookings\u003C/code\u003E와 병합하겠습니다. 다음으로 \u003Ccode\u003Eprepped_data\u003C/code\u003E 테이블을 만들기 위해 \u003Ccode\u003Ecombined_bookings\u003C/code\u003E 및 \u003Ccode\u003Ecustomer\u003C/code\u003E 테이블을 customer_id로 결합하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E마지막으로 2개의 뷰를 생성하여 \u003Ccode\u003Eprepped_data\u003C/code\u003E에 대한 분석 및 변환 작업을 수행하겠습니다.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Ehotel_count_by_day.sql\u003C/code\u003E: 이렇게 하면 ANALYSIS 스키마에 hotel_count_by_day 뷰가 생성됩니다. 여기에서 일별로 호텔 예약 횟수를 계산하게 됩니다.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Ethirty_day_avg_cost.sql\u003C/code\u003E: 이렇게 하면 ANALYSIS 스키마에 thirty_day_avg_cost 뷰가 생성됩니다. 여기에서 지난 30일 동안의 평균 예약 비용을 계산하게 됩니다.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_0_dbt_flow.png?v=dbf9e9fa\" alt=\"dbt_structure\"\u003E\u003C/p\u003E\n","\u003Cp\u003E우선, Snowflake 콘솔로 이동하여 아래 스크립트를 실행하겠습니다. 이렇게 하면 dbt_user 및 dbt_dev_role이 생성됩니다. 그런 다음 dbt_user를 위한 데이터베이스를 설정합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE SECURITYADMIN;\n\nCREATE OR REPLACE ROLE dbt_DEV_ROLE COMMENT='dbt_DEV_ROLE';\nGRANT ROLE dbt_DEV_ROLE TO ROLE SYSADMIN;\n\nCREATE OR REPLACE USER dbt_USER PASSWORD='<PASSWORD>'\n\tDEFAULT_ROLE=dbt_DEV_ROLE\n\tDEFAULT_WAREHOUSE=dbt_WH\n\tCOMMENT='dbt User';\n    \nGRANT ROLE dbt_DEV_ROLE TO USER dbt_USER;\n\n-- Grant privileges to role\nUSE ROLE ACCOUNTADMIN;\n\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE dbt_DEV_ROLE;\n\n/*---------------------------------------------------------------------------\nNext we will create a virtual warehouse that will be used\n---------------------------------------------------------------------------*/\nUSE ROLE SYSADMIN;\n\n--Create Warehouse for dbt work\nCREATE OR REPLACE WAREHOUSE dbt_DEV_WH\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 120\n  AUTO_RESUME = true\n  INITIALLY_SUSPENDED = TRUE;\n\nGRANT ALL ON WAREHOUSE dbt_DEV_WH TO ROLE dbt_DEV_ROLE;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Edbt_user\u003C/code\u003E로 로그인하고 명령을 실행하여 \u003Ccode\u003EDEMO_dbt\u003C/code\u003E 데이터베이스를 생성하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nCREATE OR REPLACE DATABASE DEMO_dbt\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_2_snowflake_console.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E이제 앞서 1단계에서 설정한 \u003Ccode\u003Edbt_airflow\u003C/code\u003E > \u003Ccode\u003Edbt\u003C/code\u003E 프로젝트로 되돌아가겠습니다.\u003C/p\u003E\n","\u003Cp\u003E아래에 있는 각 파일의 몇 가지 구성을 설정하겠습니다. \u003Ccode\u003Edbt_project.yml\u003C/code\u003E에 대해서는 모델 섹션만 바꾸면 됩니다.\u003C/p\u003E\n","\u003Cp\u003Eprofiles.yml\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Edefault:\n  target: dev\n  outputs:\n    dev:\n      type: snowflake\n      ######## Please replace with your Snowflake account name \n      ######## for example sg_demo.ap-southeast-1\n      account: <ACCOUNT_URL>.<REGION> \n\n      user: "{{ env_var('dbt_user') }}"\n      ######## These environment variables dbt_user and dbt_password \n      ######## are read from the variabls in Airflow which we will set later\n      password: "{{ env_var('dbt_password') }}"\n\n      role: dbt_dev_role\n      database: demo_dbt\n      warehouse: dbt_dev_wh\n      schema: public\n      threads: 200\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Epackages.yml\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Epackages:\n  - package: fishtown-analytics/dbt_utils\n    version: 0.6.4\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Edbt_project.yml\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-yml\"\u003Emodels:\n  my_new_project:\n      # Applies to all files under models/example/\n      transform:\n          schema: transform\n          materialized: view\n      analysis:\n          schema: analysis\n          materialized: view\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E다음으로 \u003Ccode\u003Epackages.yml\u003C/code\u003E 내부에 배치한 \u003Ccode\u003Efishtown-analytics/dbt_utils\u003C/code\u003E를 설치하겠습니다. \u003Ccode\u003Edbt\u003C/code\u003E 폴더에서 \u003Ccode\u003Edbt deps\u003C/code\u003E 명령을 실행하여 설치할 수 있습니다.\u003C/p\u003E\n","\u003Cp\u003E이제 \u003Ccode\u003Emacros\u003C/code\u003E 폴더에 \u003Ccode\u003Ecustom_demo_macros.sql\u003C/code\u003E이라는 파일을 생성하고 아래 SQL을 입력하겠습니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{% macro generate_schema_name(custom_schema_name, node) -%}\n    {%- set default_schema = target.schema -%}\n    {%- if custom_schema_name is none -%}\n        {{ default_schema }}\n    {%- else -%}\n        {{ custom_schema_name | trim }}\n    {%- endif -%}\n{%- endmacro %}\n\n\n{% macro set_query_tag() -%}\n  {% set new_query_tag = model.name %} {# always use model name #}\n  {% if new_query_tag %}\n    {% set original_query_tag = get_current_query_tag() %}\n    {{ log("Setting query_tag to '" ~ new_query_tag ~ "'. Will reset to '" ~ original_query_tag ~ "' after materialization.") }}\n    {% do run_query("alter session set query_tag = '{}'".format(new_query_tag)) %}\n    {{ return(original_query_tag)}}\n  {% endif %}\n  {{ return(none)}}\n{% endmacro %}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E모든 것이 올바르게 진행되면 폴더는 다음과 같아야 합니다. 주석이 달린 상자는 위에서 진행한 내용입니다.\u003C/p\u003E\n","\u003Cp\u003E여기에서 진행할 마지막 단계는 \u003Ccode\u003Edb_utils\u003C/code\u003E의 dbt 모듈을 설치하는 것입니다. dbt 디렉터리에서 실행합니다.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language- \"\u003Edbt deps\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E그러면 \u003Ccode\u003Edbt_modules\u003C/code\u003E 폴더에 관련 모듈이 설치되는 것을 확인할 수 있습니다.\u003C/p\u003E\n","\u003Cp\u003E지금쯤 폴더 구조는 다음과 같아야 합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_3_dbt_structure.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003Edbt 구성이 완료되었습니다. 다음 섹션에서는 CSV 파일과 DAG를 생성하겠습니다.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Edbt에서 CSV 데이터 파일 생성\u003C/h2\u003E\n","\u003Cp\u003E이 섹션에서는 샘플 CSV 데이터 파일과 관련 SQL 모델을 준비하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E우선 dbt 폴더에 있는 \u003Ccode\u003Edata\u003C/code\u003E 폴더에 3개의 Excel 파일을 생성하며 시작하겠습니다.\u003C/p\u003E\n","\u003Cp\u003Ebookings_1.csv\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-csv\"\u003Eid,booking_reference,hotel,booking_date,cost\n1,232323231,Pan Pacific,2021-03-19,100\n1,232323232,Fullerton,2021-03-20,200\n1,232323233,Fullerton,2021-04-20,300\n1,232323234,Jackson Square,2021-03-21,400\n1,232323235,Mayflower,2021-06-20,500\n1,232323236,Suncity,2021-03-19,600\n1,232323237,Fullerton,2021-08-20,700\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Ebookings_2.csv\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-csv\"\u003Eid,booking_reference,hotel,booking_date,cost\n2,332323231,Fullerton,2021-03-19,100\n2,332323232,Jackson Square,2021-03-20,300\n2,332323233,Suncity,2021-03-20,300\n2,332323234,Jackson Square,2021-03-21,300\n2,332323235,Fullerton,2021-06-20,300\n2,332323236,Suncity,2021-03-19,300\n2,332323237,Berkly,2021-05-20,200\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Ecustomers.csv\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-csv\"\u003Eid,first_name,last_name,birthdate,membership_no\n1,jim,jone,1989-03-19,12334\n2,adrian,lee,1990-03-10,12323\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E폴더 구조는 다음과 같아야 합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_4_csv_files.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Emodels 폴더에서 dbt 모델 생성\u003C/h2\u003E\n","\u003Cp\u003Emodels 폴더에서 \u003Ccode\u003Eanalysis\u003C/code\u003E 및 \u003Ccode\u003Etransform\u003C/code\u003E 폴더 2개를 생성합니다. 분석 및 변환을 위해 아래에 있는 섹션을 각각 따르십시오.\u003C/p\u003E\n","\u003Ch3\u003Etransform 폴더를 위한 dbt 모델\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Etransform\u003C/code\u003E 폴더에는 3개의 SQL 파일이 있습니다.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003Ecombined_bookings.sql\u003C/code\u003E: 이는 위에서 봤던 2개의 예약 CSV 파일을 결합하고 \u003Ccode\u003ETRANSFORM\u003C/code\u003E 스키마에 \u003Ccode\u003ECOMBINED_BOOKINGS\u003C/code\u003E 뷰를 생성합니다.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003Ecombined_bookings.sql\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E{{ dbt_utils.union_relations(\n    relations=[ref('bookings_1'), ref('bookings_2')]\n) }}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\u003Ccode\u003Ecustomer.sql\u003C/code\u003E: 이는 \u003Ccode\u003ETRANSFORM\u003C/code\u003E 스키마에 \u003Ccode\u003ECUSTOMER\u003C/code\u003E 뷰를 생성합니다.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003Ecustomer.sql\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT ID \n    , FIRST_NAME\n    , LAST_NAME\n    , birthdate\nFROM {{ ref('customers') }}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\u003Ccode\u003Eprepped_data.sql\u003C/code\u003E: 이는 \u003Ccode\u003ETRANSFORM\u003C/code\u003E 스키마에 \u003Ccode\u003EPREPPED_DATA\u003C/code\u003E 뷰를 생성합니다. 여기에서는 위 단계의 \u003Ccode\u003ECUSTOMER\u003C/code\u003E 및 \u003Ccode\u003ECOMBINED_BOOKINGS\u003C/code\u003E 뷰에 대한 내부 조인 작업이 수행됩니다.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003Eprepped_data.sql\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT A.ID \n    , FIRST_NAME\n    , LAST_NAME\n    , birthdate\n    , BOOKING_REFERENCE\n    , HOTEL\n    , BOOKING_DATE\n    , COST\nFROM {{ref('customer')}}  A\nJOIN {{ref('combined_bookings')}} B\non A.ID = B.ID\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003Eanalysis 폴더를 위한 dbt 모델\u003C/h3\u003E\n","\u003Cp\u003E이제 \u003Ccode\u003Eanalysis\u003C/code\u003E 폴더로 이동하겠습니다. \u003Ccode\u003Eanalysis\u003C/code\u003E 폴더로 바꾸고 다음과 같은 2개의 SQL 파일을 생성합니다.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003Ehotel_count_by_day.sql\u003C/code\u003E: 이렇게 하면 \u003Ccode\u003EANALYSIS\u003C/code\u003E 스키마에 hotel_count_by_day 뷰가 생성됩니다. 여기에서 일별로 호텔 예약 횟수를 계산하게 됩니다.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  BOOKING_DATE,\n  HOTEL,\n  COUNT(ID) as count_bookings\nFROM {{ ref('prepped_data') }}\nGROUP BY\n  BOOKING_DATE,\n  HOTEL\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\u003Ccode\u003Ethirty_day_avg_cost.sql\u003C/code\u003E: 이렇게 하면 \u003Ccode\u003EANALYSIS\u003C/code\u003E 스키마에 thirty_day_avg_cost 뷰가 생성됩니다. 여기에서 지난 30일 동안의 평균 예약 비용을 계산하게 됩니다.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n  BOOKING_DATE,\n  HOTEL,\n  COST,\n  AVG(COST) OVER (\n    ORDER BY BOOKING_DATE ROWS BETWEEN 29 PRECEDING AND CURRENT ROW\n  ) as "30_DAY_AVG_COST",\n  COST -   AVG(COST) OVER (\n    ORDER BY BOOKING_DATE ROWS BETWEEN 29 PRECEDING AND CURRENT ROW\n  ) as "DIFF_BTW_ACTUAL_AVG"\nFROM {{ ref('prepped_data') }}\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E파일 구조는 다음과 같아야 합니다. 이미 dbt 모델을 완료했으니 Airflow에 대한 작업을 진행할 수 있습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_dbt_models.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAirflow DAG 준비\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Edags\u003C/code\u003E 폴더에서 \u003Ccode\u003Einit.py\u003C/code\u003E 및 \u003Ccode\u003Etransform_and_analysis.py\u003C/code\u003E 파일 2개를 생성합니다. \u003Ccode\u003Einit.py\u003C/code\u003E는 초기화하며 CSV 데이터를 열람합니다. \u003Ccode\u003Etransform_and_analysis.py\u003C/code\u003E는 변환 및 분석 작업을 수행합니다.\u003C/p\u003E\n","\u003Cp\u003E그런 다음 Airflow를 사용하여 매일 \u003Ccode\u003Etransform_and_analysis\u003C/code\u003E DAG를 예약할 수 있습니다. 하지만 이 사례에서는 DAG를 수동으로 트리거하겠습니다.\u003C/p\u003E\n","\u003Cp\u003Einit.py\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom datetime import datetime\nimport os\n\nfrom airflow import DAG\nfrom airflow.operators.python import PythonOperator, BranchPythonOperator\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.dummy_operator import DummyOperator\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime(2020,8,1),\n    'retries': 0\n}\n\n\nwith DAG('1_init_once_seed_data', default_args=default_args, schedule_interval='@once') as dag:\n    task_1 = BashOperator(\n        task_id='load_seed_data_once',\n        bash_command='cd /dbt && dbt seed --profiles-dir .',\n        env={\n            'dbt_user': '{{ var.value.dbt_user }}',\n            'dbt_password': '{{ var.value.dbt_password }}',\n            **os.environ\n        },\n        dag=dag\n    )\n\ntask_1  \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Etransform_and_analysis.py\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom airflow import DAG\nfrom airflow.operators.python import PythonOperator, BranchPythonOperator\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom datetime import datetime\n\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime(2020,8,1),\n    'retries': 0\n}\n\nwith DAG('2_daily_transformation_analysis', default_args=default_args, schedule_interval='@once') as dag:\n    task_1 = BashOperator(\n        task_id='daily_transform',\n        bash_command='cd /dbt && dbt run --models transform --profiles-dir .',\n        env={\n            'dbt_user': '{{ var.value.dbt_user }}',\n            'dbt_password': '{{ var.value.dbt_password }}',\n            **os.environ\n        },\n        dag=dag\n    )\n\n    task_2 = BashOperator(\n        task_id='daily_analysis',\n        bash_command='cd /dbt && dbt run --models analysis --profiles-dir .',\n        env={\n            'dbt_user': '{{ var.value.dbt_user }}',\n            'dbt_password': '{{ var.value.dbt_password }}',\n            **os.environ\n        },\n        dag=dag\n    )\n\n    task_1 >> task_2 # Define dependencies\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAirflow를 위해 docker-compose 파일 실행\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Edocker-compose up\u003C/code\u003E을 실행하고 \u003Ca href=\"http://localhost:8080/\"\u003Ehttp://localhost:8080/\u003C/a\u003E으로 이동하겠습니다. 기본 사용자 이름은 \u003Ccode\u003Eairflow\u003C/code\u003E이고 암호는 \u003Ccode\u003Eairflow\u003C/code\u003E입니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_2_airflow_url.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E이제 2개의 변수를 생성하겠습니다. \u003Ccode\u003Eadmin > Variables\u003C/code\u003E로 이동하여 \u003Ccode\u003E+\u003C/code\u003E 아이콘을 클릭합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_airflow_variables.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E우선 \u003Ccode\u003Edbt_user\u003C/code\u003E 키와 \u003Ccode\u003Edbt_user\u003C/code\u003E 값을 생성하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_airflow_username.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E이제 두 번째 \u003Ccode\u003Edbt_password\u003C/code\u003E 키와 \u003Ccode\u003E<ADD IN YOUR PASSWORD>\u003C/code\u003E 값을 생성하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_airflow_password.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDAG 활성화 및 실행\u003C/h2\u003E\n","\u003Cp\u003E이제 DAG를 활성화하겠습니다. \u003Ccode\u003E1_init_once_seed_data\u003C/code\u003E 및 \u003Ccode\u003E2_daily_transformation_analysis\u003C/code\u003E의 파란색 버튼을 클릭합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_6_runnig_our_dags.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E1_init_once_seed_data 실행\u003C/h3\u003E\n","\u003Cp\u003E이제 데이터를 시드하기 위해 \u003Ccode\u003E1_init_once_seed_data\u003C/code\u003E를 실행하겠습니다. 실행하려면 DAG 오른쪽에 있는 \u003Ccode\u003EActions\u003C/code\u003E 아래의 재생 아이콘을 클릭합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_7_dag_init_successful.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPUBLIC 스키마에 생성된 테이블에서 시드 데이터 보기\u003C/h3\u003E\n","\u003Cp\u003E모든 것이 올바르게 진행되면 Snowflake 인스턴스로 되돌아갔을 때 \u003Ccode\u003EPUBLIC\u003C/code\u003E 스키마에 성공적으로 생성된 트리 테이블이 보여야 합니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_8_snowflake_successful_seed.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E2_daily_transformation_analysis 실행\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Etransform\u003C/code\u003E 및 \u003Ccode\u003Eanalysis\u003C/code\u003E 모델을 실행할 두 번째 \u003Ccode\u003E2_daily_transformation_analysis\u003C/code\u003E DAG를 실행하겠습니다.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_9_dag_transform_analysis_successful.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003ETransform\u003C/code\u003E 및 \u003Ccode\u003EAnalysis\u003C/code\u003E 뷰가 성공적으로 생성되었습니다!\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_10_snowflake_successful_transform_analysis.png?v=dbf9e9fa\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E결론\u003C/h2\u003E\n","\u003Cp\u003E축하합니다! dbt 및 Snowflake로 첫 번째 Apache Airflow를 생성하셨습니다! 자체 샘플이나 프로덕션 데이터를 로드하고 이 랩에서 다루지 않은 더 고급 수준의 Airflow 및 Snowflake 기능을 사용하여 무료 평가판을 계속 사용해 보십시오.\u003C/p\u003E\n","\u003Ch3\u003E추가 리소스:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E18,000명 이상의 데이터 실무자가 참여하고 있는 \u003Ca href=\"https://www.getdbt.com/community/\"\u003Edbt 커뮤니티 Slack\u003C/a\u003E에 오늘 참여해 보십시오. Snowflake 관련 콘텐츠 전용 Slack 채널(#db-snowflake)을 운영하고 있습니다.\u003C/li\u003E\u003Cli\u003E단순한 \u003Ca href=\"https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html\"\u003EAirflow DAG\u003C/a\u003E 작성 방법에 대한 빠른 자습서\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E다룬 내용:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAirflow, dbt 및 Snowflake 설정하기\u003C/li\u003E\u003Cli\u003EDAG 생성하기 및 DAG에서 dbt 실행하기\u003C/li\u003E\u003C/ul\u003E"],":type":"snowflake-site/components/contentfragment","isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"value":"\u003C!-- ------------------------ --\u003E\n## 개요\n\n\n![architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_0_overall_architecture.png?v=dbf9e9fa)\n\n수많은 비즈니스는 민첩성, 성장 및 운영 효율성을 지원할 수 있는 최신 데이터 전략이 탑재된 플랫폼을 고려하고 있습니다. Snowflake는 데이터 클라우드입니다. 이는 인프라 관리와 유지 관리 대신 데이터와 분석에 집중할 수 있도록 모든 비즈니스를 위해 데이터 파이프라인을 간소화할 수 있는 미래 지향적인 솔루션입니다.\n\nApache Airflow는 데이터 파이프라인을 작성 및 관리하는 데 사용할 수 있는 오픈 소스 워크플로우 관리 플랫폼입니다. Airflow는 작업의 방향성 비순환 그래프(DAG)로 만들어진 워크플로우를 사용합니다.\n\n[dbt](https://www.getdbt.com/)는 [dbt Labs](https://www.getdbt.com/)에서 유지하는 최신 데이터 엔지니어링 프레임워크입니다. Snowflake와 같은 클라우드 데이터 플랫폼을 활용하는 최신 데이터 아키텍처에서 큰 인기를 얻고 있습니다. [dbt CLI](https://docs.getdbt.com/dbt-cli/cli-overview)는 dbt 프로젝트를 실행하기 위한 명령줄 인터페이스입니다. CLI는 무료로 사용할 수 있으며 오픈 소스입니다.\n\n이 가상 실습 랩에서는 데이터 변환 작업 스케줄러를 생성하기 위해 Airflow와 dbt를 사용하는 단계별 가이드를 따라 하게 됩니다.\n\n시작하겠습니다.\n\n### 사전 필요 조건 및 지식\n\n이 가이드는 사용자가 Python 및 dbt에 대한 기본적인 실무 지식을 보유하고 있다고 가정합니다.\n\n### 학습할 내용\n\n- 데이터 스케줄러를 생성하기 위해 Airflow와 같은 오픈 소스 도구 사용하기\n- DAG를 작성하고 이를 Airflow에 업로드하기\n- dbt, Airflow 및 Snowflake를 사용하여 확장 가능한 파이프라인 구축하기\n\n### 필요한 것\n\n시작하기 전에 다음이 필요합니다.\n\n1. Snowflake\n2. **Snowflake 계정**\n3. **적절한 권한을 포함하여 생성된 Snowflake 사용자** 이 사용자에게는 DEMO_DB 데이터베이스에서 객체를 생성하기 위한 권한이 필요합니다.\n4. GitHub\n5. **GitHub 계정** GitHub 계정이 아직 없다면 무료로 생성할 수 있습니다. 시작하려면 [GitHub 가입](https://github.com/join) 페이지를 방문하십시오.\n6. **GitHub 리포지토리** 아직 리포지토리를 생성하지 않았거나 새로운 리포지토리를 생성하고 싶다면, [새로운 리포지토리를 생성합니다](https://github.com/new). 유형으로 `Public`을 선택합니다(하지만 두 가지 모두 사용할 수 있음). 또한, 지금은 README, .gitignore 및 라이선스 추가를 건너뛸 수 있습니다.\n7. 통합 개발 환경(IDE)\n8. **Git과 통합되며 선호하는 IDE** Git과 통합되며 선호하는 IDE가 아직 없다면, 훌륭한 무료 오픈 소스 IDE인 [Visual Studio Code](https://code.visualstudio.com/)를 추천합니다.\n9. **컴퓨터에 프로젝트 리포지토리 복제** Git 리포지토리에 대한 연결 세부 정보를 위해 리포지토리를 열고 페이지 상단 부근에 있는 `HTTPS` 링크를 복사합니다. 최소 하나의 파일이 리포지토리에 있다면 페이지 상단 부근에 있는 녹색 `Code` 아이콘을 클릭하고 `HTTPS` 링크를 복사합니다. 이 링크를 VS Code 또는 선호하는 IDE에서 사용하여 리포지토리를 컴퓨터에 복제합니다.\n10. Docker\n11. **노트북에 Docker Desktop 설치**  Airflow를 컨테이너로 실행할 것입니다. [Docker 설정 지침](https://docs.docker.com/desktop/)에 따라 운영 체제에 Docker Desktop을 설치하십시오.\n\n### 구축할 것\n\n- dbt 및 Snowflake와 간단히 작동하는 Airflow 파이프라인\n\n\u003C!-- ------------------------ --\u003E\n## 환경 설정\n\n\n우선, 아래 명령을 실행하여 폴더를 생성하겠습니다.\n\n```\nmkdir dbt_airflow && cd \"$_\"\n```\n\n다음으로 Airflow의 docker-compose 파일을 가져오겠습니다. 이 작업을 수행하기 위해 로컬 노트북에 대해 파일의 curl 명령을 실행하겠습니다.\n\n```bash\ncurl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.3.0/docker-compose.yaml'\n```\n\n이제 docker-compose 파일을 조정하겠습니다. 2개의 폴더를 볼륨으로 추가합니다. `dags`는 Airflow가 선택하여 분석할 수 있도록 Airflow DAG가 있는 폴더입니다. `dbt`는 dbt 모델과 CSV 파일을 구성한 폴더입니다.\n\n```bash\n  volumes:\n    - ./dags:/opt/airflow/dags\n    - ./logs:/opt/airflow/logs\n    - ./plugins:/opt/airflow/plugins\n    - ./dbt:/dbt # add this in\n    - ./dags:/dags # add this in\n\n```\n\n이제 추가 docker-compose 매개변수로 추가 파일을 생성해야 합니다. 이렇게 하면 컨테이너가 시작될 때 dbt가 설치됩니다.\n\n`.env`\n\n```bash\n_PIP_ADDITIONAL_REQUIREMENTS=dbt==0.19.0\n```\n\n---\n\n이제 `dbt` 프로젝트와 `dags` 폴더를 생성해야 합니다.\n\ndbt 프로젝트에 대해 `dbt init dbt` 명령을 실행합니다. 추후 4단계에서는 여기에서 dbt를 구성하게 됩니다.\n\ndags 폴더에 대해 다음 작업을 수행해 폴더를 생성합니다.\n\n```\nmkdir dags\n```\n\n트리 리포지토리는 다음과 같아야 합니다.\n\n![Folderstructure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_1_tree_structure.png?v=dbf9e9fa)\n\n\u003C!-- ------------------------ --\u003E\n## dbt 프로젝트 설정\n\n\n이제 리포지토리를 설정했으니 dbt 프로젝트를 구성 및 설정하겠습니다.\n\n시작하기 전에 dbt 프로젝트를 위해 어떤 작업을 수행할지 알아보겠습니다.\n\n아래 다이어그램에서 확인할 수 있듯이 `bookings_1`, `bookings_2` 및 `customers `라는 3개의 CSV 파일이 있습니다. 이러한 CSV 파일을 테이블로 Snowflake에 시드하겠습니다. 이 부분은 추후 4단계에서 자세히 살펴볼 것입니다.\n\n이에 따라 `bookings_1` 및 `bookings_2` 테이블을 `combined_bookings`와 병합하겠습니다. 다음으로 `prepped_data` 테이블을 만들기 위해 `combined_bookings` 및 `customer` 테이블을 customer_id로 결합하겠습니다.\n\n마지막으로 2개의 뷰를 생성하여 `prepped_data`에 대한 분석 및 변환 작업을 수행하겠습니다.\n\n1) `hotel_count_by_day.sql`: 이렇게 하면 ANALYSIS 스키마에 hotel_count_by_day 뷰가 생성됩니다. 여기에서 일별로 호텔 예약 횟수를 계산하게 됩니다.\n\n2) `thirty_day_avg_cost.sql`: 이렇게 하면 ANALYSIS 스키마에 thirty_day_avg_cost 뷰가 생성됩니다. 여기에서 지난 30일 동안의 평균 예약 비용을 계산하게 됩니다.\n\n![dbt_structure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_0_dbt_flow.png?v=dbf9e9fa)\n\n우선, Snowflake 콘솔로 이동하여 아래 스크립트를 실행하겠습니다. 이렇게 하면 dbt_user 및 dbt_dev_role이 생성됩니다. 그런 다음 dbt_user를 위한 데이터베이스를 설정합니다.\n\n```sql\nUSE ROLE SECURITYADMIN;\n\nCREATE OR REPLACE ROLE dbt_DEV_ROLE COMMENT='dbt_DEV_ROLE';\nGRANT ROLE dbt_DEV_ROLE TO ROLE SYSADMIN;\n\nCREATE OR REPLACE USER dbt_USER PASSWORD='\u003CPASSWORD\u003E'\n\tDEFAULT_ROLE=dbt_DEV_ROLE\n\tDEFAULT_WAREHOUSE=dbt_WH\n\tCOMMENT='dbt User';\n    \nGRANT ROLE dbt_DEV_ROLE TO USER dbt_USER;\n\n-- Grant privileges to role\nUSE ROLE ACCOUNTADMIN;\n\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE dbt_DEV_ROLE;\n\n/*---------------------------------------------------------------------------\nNext we will create a virtual warehouse that will be used\n---------------------------------------------------------------------------*/\nUSE ROLE SYSADMIN;\n\n--Create Warehouse for dbt work\nCREATE OR REPLACE WAREHOUSE dbt_DEV_WH\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n  AUTO_SUSPEND = 120\n  AUTO_RESUME = true\n  INITIALLY_SUSPENDED = TRUE;\n\nGRANT ALL ON WAREHOUSE dbt_DEV_WH TO ROLE dbt_DEV_ROLE;\n\n```\n\n`dbt_user`로 로그인하고 명령을 실행하여 `DEMO_dbt` 데이터베이스를 생성하겠습니다.\n\n```sql\n\nCREATE OR REPLACE DATABASE DEMO_dbt\n\n```\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_2_snowflake_console.png?v=dbf9e9fa)\n\n이제 앞서 1단계에서 설정한 `dbt_airflow` \u003E `dbt` 프로젝트로 되돌아가겠습니다.\n\n아래에 있는 각 파일의 몇 가지 구성을 설정하겠습니다. `dbt_project.yml`에 대해서는 모델 섹션만 바꾸면 됩니다.\n\nprofiles.yml\n\n```yml\ndefault:\n  target: dev\n  outputs:\n    dev:\n      type: snowflake\n      ######## Please replace with your Snowflake account name \n      ######## for example sg_demo.ap-southeast-1\n      account: \u003CACCOUNT_URL\u003E.\u003CREGION\u003E \n\n      user: \"{{ env_var('dbt_user') }}\"\n      ######## These environment variables dbt_user and dbt_password \n      ######## are read from the variabls in Airflow which we will set later\n      password: \"{{ env_var('dbt_password') }}\"\n\n      role: dbt_dev_role\n      database: demo_dbt\n      warehouse: dbt_dev_wh\n      schema: public\n      threads: 200\n```\n\npackages.yml\n\n```yml\npackages:\n  - package: fishtown-analytics/dbt_utils\n    version: 0.6.4\n```\n\ndbt_project.yml\n\n```yml\nmodels:\n  my_new_project:\n      # Applies to all files under models/example/\n      transform:\n          schema: transform\n          materialized: view\n      analysis:\n          schema: analysis\n          materialized: view\n```\n\n다음으로 `packages.yml` 내부에 배치한 `fishtown-analytics/dbt_utils`를 설치하겠습니다. `dbt` 폴더에서 `dbt deps` 명령을 실행하여 설치할 수 있습니다.\n\n이제 `macros` 폴더에 `custom_demo_macros.sql`이라는 파일을 생성하고 아래 SQL을 입력하겠습니다.\n\n```sql\n{% macro generate_schema_name(custom_schema_name, node) -%}\n    {%- set default_schema = target.schema -%}\n    {%- if custom_schema_name is none -%}\n        {{ default_schema }}\n    {%- else -%}\n        {{ custom_schema_name | trim }}\n    {%- endif -%}\n{%- endmacro %}\n\n\n{% macro set_query_tag() -%}\n  {% set new_query_tag = model.name %} {# always use model name #}\n  {% if new_query_tag %}\n    {% set original_query_tag = get_current_query_tag() %}\n    {{ log(\"Setting query_tag to '\" ~ new_query_tag ~ \"'. Will reset to '\" ~ original_query_tag ~ \"' after materialization.\") }}\n    {% do run_query(\"alter session set query_tag = '{}'\".format(new_query_tag)) %}\n    {{ return(original_query_tag)}}\n  {% endif %}\n  {{ return(none)}}\n{% endmacro %}\n```\n\n모든 것이 올바르게 진행되면 폴더는 다음과 같아야 합니다. 주석이 달린 상자는 위에서 진행한 내용입니다.\n\n여기에서 진행할 마지막 단계는 `db_utils`의 dbt 모듈을 설치하는 것입니다. dbt 디렉터리에서 실행합니다.\n\n``` \ndbt deps\n```\n\n그러면 `dbt_modules` 폴더에 관련 모듈이 설치되는 것을 확인할 수 있습니다.\n\n지금쯤 폴더 구조는 다음과 같아야 합니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_3_dbt_structure.png?v=dbf9e9fa)\n\ndbt 구성이 완료되었습니다. 다음 섹션에서는 CSV 파일과 DAG를 생성하겠습니다.\n\n\u003C!-- ------------------------ --\u003E\n## dbt에서 CSV 데이터 파일 생성\n\n\n이 섹션에서는 샘플 CSV 데이터 파일과 관련 SQL 모델을 준비하겠습니다.\n\n우선 dbt 폴더에 있는 `data` 폴더에 3개의 Excel 파일을 생성하며 시작하겠습니다.\n\nbookings_1.csv\n\n```csv\nid,booking_reference,hotel,booking_date,cost\n1,232323231,Pan Pacific,2021-03-19,100\n1,232323232,Fullerton,2021-03-20,200\n1,232323233,Fullerton,2021-04-20,300\n1,232323234,Jackson Square,2021-03-21,400\n1,232323235,Mayflower,2021-06-20,500\n1,232323236,Suncity,2021-03-19,600\n1,232323237,Fullerton,2021-08-20,700\n```\n\nbookings_2.csv\n\n```csv\nid,booking_reference,hotel,booking_date,cost\n2,332323231,Fullerton,2021-03-19,100\n2,332323232,Jackson Square,2021-03-20,300\n2,332323233,Suncity,2021-03-20,300\n2,332323234,Jackson Square,2021-03-21,300\n2,332323235,Fullerton,2021-06-20,300\n2,332323236,Suncity,2021-03-19,300\n2,332323237,Berkly,2021-05-20,200\n```\n\ncustomers.csv\n\n```csv\nid,first_name,last_name,birthdate,membership_no\n1,jim,jone,1989-03-19,12334\n2,adrian,lee,1990-03-10,12323\n```\n\n폴더 구조는 다음과 같아야 합니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_4_csv_files.png?v=dbf9e9fa)\n\n\u003C!-- ------------------------ --\u003E\n## models 폴더에서 dbt 모델 생성\n\n\nmodels 폴더에서 `analysis` 및 `transform` 폴더 2개를 생성합니다. 분석 및 변환을 위해 아래에 있는 섹션을 각각 따르십시오.\n\n### transform 폴더를 위한 dbt 모델\n\n`transform` 폴더에는 3개의 SQL 파일이 있습니다.\n\n1) `combined_bookings.sql`: 이는 위에서 봤던 2개의 예약 CSV 파일을 결합하고 `TRANSFORM` 스키마에 `COMBINED_BOOKINGS` 뷰를 생성합니다.\n\ncombined_bookings.sql\n\n```sql\n{{ dbt_utils.union_relations(\n    relations=[ref('bookings_1'), ref('bookings_2')]\n) }}\n```\n\n2) `customer.sql`: 이는 `TRANSFORM` 스키마에 `CUSTOMER` 뷰를 생성합니다.\n\ncustomer.sql\n\n```sql\nSELECT ID \n    , FIRST_NAME\n    , LAST_NAME\n    , birthdate\nFROM {{ ref('customers') }}\n```\n\n3) `prepped_data.sql`: 이는 `TRANSFORM` 스키마에 `PREPPED_DATA` 뷰를 생성합니다. 여기에서는 위 단계의 `CUSTOMER` 및 `COMBINED_BOOKINGS` 뷰에 대한 내부 조인 작업이 수행됩니다.\n\nprepped_data.sql\n\n```sql\nSELECT A.ID \n    , FIRST_NAME\n    , LAST_NAME\n    , birthdate\n    , BOOKING_REFERENCE\n    , HOTEL\n    , BOOKING_DATE\n    , COST\nFROM {{ref('customer')}}  A\nJOIN {{ref('combined_bookings')}} B\non A.ID = B.ID\n```\n\n### analysis 폴더를 위한 dbt 모델\n\n이제 `analysis` 폴더로 이동하겠습니다. `analysis` 폴더로 바꾸고 다음과 같은 2개의 SQL 파일을 생성합니다.\n\n1) `hotel_count_by_day.sql`: 이렇게 하면 `ANALYSIS` 스키마에 hotel_count_by_day 뷰가 생성됩니다. 여기에서 일별로 호텔 예약 횟수를 계산하게 됩니다.\n\n```sql\nSELECT\n  BOOKING_DATE,\n  HOTEL,\n  COUNT(ID) as count_bookings\nFROM {{ ref('prepped_data') }}\nGROUP BY\n  BOOKING_DATE,\n  HOTEL\n```\n\n2) `thirty_day_avg_cost.sql`: 이렇게 하면 `ANALYSIS` 스키마에 thirty_day_avg_cost 뷰가 생성됩니다. 여기에서 지난 30일 동안의 평균 예약 비용을 계산하게 됩니다.\n\n```sql\nSELECT\n  BOOKING_DATE,\n  HOTEL,\n  COST,\n  AVG(COST) OVER (\n    ORDER BY BOOKING_DATE ROWS BETWEEN 29 PRECEDING AND CURRENT ROW\n  ) as \"30_DAY_AVG_COST\",\n  COST -   AVG(COST) OVER (\n    ORDER BY BOOKING_DATE ROWS BETWEEN 29 PRECEDING AND CURRENT ROW\n  ) as \"DIFF_BTW_ACTUAL_AVG\"\nFROM {{ ref('prepped_data') }}\n```\n\n파일 구조는 다음과 같아야 합니다. 이미 dbt 모델을 완료했으니 Airflow에 대한 작업을 진행할 수 있습니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_dbt_models.png?v=dbf9e9fa)\n\n\u003C!-- ------------------------ --\u003E\n## Airflow DAG 준비\n\n\n`dags` 폴더에서 `init.py` 및 `transform_and_analysis.py` 파일 2개를 생성합니다. `init.py`는 초기화하며 CSV 데이터를 열람합니다. `transform_and_analysis.py`는 변환 및 분석 작업을 수행합니다.\n\n그런 다음 Airflow를 사용하여 매일 `transform_and_analysis` DAG를 예약할 수 있습니다. 하지만 이 사례에서는 DAG를 수동으로 트리거하겠습니다.\n\ninit.py\n\n```python\nfrom datetime import datetime\nimport os\n\nfrom airflow import DAG\nfrom airflow.operators.python import PythonOperator, BranchPythonOperator\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.dummy_operator import DummyOperator\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime(2020,8,1),\n    'retries': 0\n}\n\n\nwith DAG('1_init_once_seed_data', default_args=default_args, schedule_interval='@once') as dag:\n    task_1 = BashOperator(\n        task_id='load_seed_data_once',\n        bash_command='cd /dbt && dbt seed --profiles-dir .',\n        env={\n            'dbt_user': '{{ var.value.dbt_user }}',\n            'dbt_password': '{{ var.value.dbt_password }}',\n            **os.environ\n        },\n        dag=dag\n    )\n\ntask_1  \n```\n\ntransform_and_analysis.py\n\n```python\nfrom airflow import DAG\nfrom airflow.operators.python import PythonOperator, BranchPythonOperator\nfrom airflow.operators.bash import BashOperator\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom datetime import datetime\n\n\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime(2020,8,1),\n    'retries': 0\n}\n\nwith DAG('2_daily_transformation_analysis', default_args=default_args, schedule_interval='@once') as dag:\n    task_1 = BashOperator(\n        task_id='daily_transform',\n        bash_command='cd /dbt && dbt run --models transform --profiles-dir .',\n        env={\n            'dbt_user': '{{ var.value.dbt_user }}',\n            'dbt_password': '{{ var.value.dbt_password }}',\n            **os.environ\n        },\n        dag=dag\n    )\n\n    task_2 = BashOperator(\n        task_id='daily_analysis',\n        bash_command='cd /dbt && dbt run --models analysis --profiles-dir .',\n        env={\n            'dbt_user': '{{ var.value.dbt_user }}',\n            'dbt_password': '{{ var.value.dbt_password }}',\n            **os.environ\n        },\n        dag=dag\n    )\n\n    task_1 \u003E\u003E task_2 # Define dependencies\n```\n\n\u003C!-- ------------------------ --\u003E\n## Airflow를 위해 docker-compose 파일 실행\n\n\n`docker-compose up`을 실행하고 [http://localhost:8080/](http://localhost:8080/)으로 이동하겠습니다. 기본 사용자 이름은 `airflow`이고 암호는 `airflow`입니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_2_airflow_url.png?v=dbf9e9fa)\n\n이제 2개의 변수를 생성하겠습니다. `admin \u003E Variables`로 이동하여 `+` 아이콘을 클릭합니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_airflow_variables.png?v=dbf9e9fa)\n\n우선 `dbt_user` 키와 `dbt_user` 값을 생성하겠습니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_airflow_username.png?v=dbf9e9fa)\n\n이제 두 번째 `dbt_password` 키와 `\u003CADD IN YOUR PASSWORD\u003E` 값을 생성하겠습니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_5_airflow_password.png?v=dbf9e9fa)\n\n\u003C!-- ------------------------ --\u003E\n## DAG 활성화 및 실행\n\n이제 DAG를 활성화하겠습니다. `1_init_once_seed_data` 및 `2_daily_transformation_analysis`의 파란색 버튼을 클릭합니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_6_runnig_our_dags.png?v=dbf9e9fa)\n\n### 1_init_once_seed_data 실행\n\n이제 데이터를 시드하기 위해 `1_init_once_seed_data`를 실행하겠습니다. 실행하려면 DAG 오른쪽에 있는 `Actions` 아래의 재생 아이콘을 클릭합니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_7_dag_init_successful.png?v=dbf9e9fa)\n\n### PUBLIC 스키마에 생성된 테이블에서 시드 데이터 보기\n\n모든 것이 올바르게 진행되면 Snowflake 인스턴스로 되돌아갔을 때 `PUBLIC` 스키마에 성공적으로 생성된 트리 테이블이 보여야 합니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_8_snowflake_successful_seed.png?v=dbf9e9fa)\n\n### 2_daily_transformation_analysis 실행\n\n`transform` 및 `analysis` 모델을 실행할 두 번째 `2_daily_transformation_analysis` DAG를 실행하겠습니다.\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_9_dag_transform_analysis_successful.png?v=dbf9e9fa)\n\n`Transform` 및 `Analysis` 뷰가 성공적으로 생성되었습니다!\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-kr/data_engineering_with_apache_airflow_10_snowflake_successful_transform_analysis.png?v=dbf9e9fa)\n\n\u003C!-- ------------------------ --\u003E\n## 결론\n\n\n축하합니다! dbt 및 Snowflake로 첫 번째 Apache Airflow를 생성하셨습니다! 자체 샘플이나 프로덕션 데이터를 로드하고 이 랩에서 다루지 않은 더 고급 수준의 Airflow 및 Snowflake 기능을 사용하여 무료 평가판을 계속 사용해 보십시오.\n\n### 추가 리소스:\n\n- 18,000명 이상의 데이터 실무자가 참여하고 있는 [dbt 커뮤니티 Slack](https://www.getdbt.com/community/)에 오늘 참여해 보십시오. Snowflake 관련 콘텐츠 전용 Slack 채널(#db-snowflake)을 운영하고 있습니다.\n- 단순한 [Airflow DAG](https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html) 작성 방법에 대한 빠른 자습서\n\n### 다룬 내용:\n\n- Airflow, dbt 및 Snowflake 설정하기\n- DAG 생성하기 및 DAG에서 dbt 실행하기\n","title":"Quickstart Article Body","dataType":"string","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"title":"Quickstart Article Logo Image","dataType":"string","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c64e18d1e8","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":{"id":"container-373da21325","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0a8ae8d8c4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-04-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-faa4fc35c5","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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