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a one-DAG Data Pipeline with Apache Airflow and dbt transformations for data analysis with Snowpark.","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003E概要\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_0_overall_architecture.png\" alt=\"アーキテクチャ\"\u003E\u003C/p\u003E\n","\u003Cp\u003E多くの企業が、アジリティ、成長、運用効率をサポートするプラットフォーム上に構築された最新のデータ戦略に注目しています。Snowflakeはデータクラウドであり、あらゆるビジネスのデータパイプラインを簡素化し、企業がインフラストラクチャの管理やメンテナンスではなく、データと分析に集中できるようにする将来を見据えたソリューションです。\u003C/p\u003E\n","\u003Cp\u003EApache Airflowは、データパイプラインの作成と管理に使用できるオープンソースのワークフロー管理プラットフォームです。Airflowは、タスクの有向非巡回グラフ（DAG）で構成されるワークフローを使用します。\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://www.getdbt.com/\"\u003Edbt\u003C/a\u003Eは、\u003Ca href=\"https://www.getdbt.com/\"\u003Edbt Labs\u003C/a\u003Eによって管理されている最新のデータエンジニアリングフレームワークであり、Snowflakeなどのクラウドデータプラットフォームを活用した最新のデータアーキテクチャで非常に人気が高まっています。\u003Ca href=\"https://docs.getdbt.com/dbt-cli/cli-overview\"\u003Edbt CLI\u003C/a\u003Eは、DBTプロジェクトを実行するためのコマンドラインインターフェイスです。このCLIは無料で使用できるオープンソースです。\u003C/p\u003E\n","\u003Cp\u003Eこのバーチャルハンズオンラボでは、Airflowとdbtを使用するためのステップバイステップのガイドに従ってデータ変換ジョブスケジューラを作成します。\u003C/p\u003E\n","\u003Cp\u003Eでは始めましょう。\u003C/p\u003E\n","\u003Ch3\u003E前提条件\u003C/h3\u003E\n","\u003Cp\u003Eこのガイドは、Pythonとdbtに関する基本的な実務知識があることを前提としています。\u003C/p\u003E\n","\u003Ch3\u003E学習する内容\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAirflowなどのオープンソースツールを使用してデータスケジューラを作成する方法\u003C/li\u003E\u003Cli\u003EDAGを作成してAirflowにアップロードする方法\u003C/li\u003E\u003Cli\u003Edbt、Airflow、Snowflakeを使用してスケーラブルなパイプラインを構築する方法\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003E必要なもの\u003C/h3\u003E\n","\u003Cp\u003E始める前に以下のものが必要です。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESnowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflakeアカウント\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E**適切な権限で作成されたSnowflakeユーザー。**このユーザーには、DEMO_DB データベースにオブジェクトを作成する権限が必要です。\u003C/li\u003E\u003Cli\u003EGitHub\u003C/li\u003E\u003Cli\u003E**GitHubアカウント。**まだGitHubアカウントを持っていない場合は、無料で作成できます。アカウントの作成を開始するには、\u003Ca href=\"https://github.com/join\"\u003EJoin GitHub\u003C/a\u003Eページにアクセスしてください。\u003C/li\u003E\u003Cli\u003E**GitHubレポジトリ。**まだレポジトリを作成していない場合、または新しく作成したい場合は、\u003Ca href=\"https://github.com/new\"\u003E新しいレポジトリを作成\u003C/a\u003Eします。タイプは「\u003Ccode\u003EPublic\u003C/code\u003E」を選択します（どちらも使用できます）。また、現時点では、README、.gitignore、ライセンスの追加をスキップできます。\u003C/li\u003E\u003Cli\u003E統合開発環境（IDE）\u003C/li\u003E\u003Cli\u003E**お気に入りのIDEとGitの統合。**お気に入りのIDEとGitをまだ統合していない場合は、\u003Ca href=\"https://code.visualstudio.com/\"\u003EVisual Studio Code\u003C/a\u003Eをお勧めします。これは無料で利用できるオープンソースの優れたIDEです。\u003C/li\u003E\u003Cli\u003E**コンピュータに複製されたプロジェクトレポジトリ。**Gitレポジトリの接続の詳細については、レポジトリを開き、ページ上部付近にある\u003Ccode\u003EHTTPS\u003C/code\u003Eリンクをコピーします。レポジトリに1つ以上のファイルがある場合は、ページ上部付近にあるの緑色の\u003Ccode\u003ECode\u003C/code\u003Eアイコンをクリックし、\u003Ccode\u003EHTTPS\u003C/code\u003Eリンクをコピーします。そのリンクをVS Codeまたはお好みのIDEで使用して、コンピュータにレポジトリを複製します。\u003C/li\u003E\u003Cli\u003EDocker\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Eノートパソコン上のDocker Desktop。\u003C/strong\u003E  Airflowをコンテナとして実行します。\u003Ca href=\"https://docs.docker.com/desktop/\"\u003EDockerの設定手順\u003C/a\u003Eに従って、ご希望のOSにDocker Desktopをインストールしてください。\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003E構築するもの\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EdbtとSnowflakeを使用したシンプルで実用的なAirflowパイプライン\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003E環境設定\u003C/h2\u003E\n","\u003Cp\u003Eまず、以下のコマンドを実行してフォルダを作成しましょう。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Emkdir dbt_airflow && cd "$_"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E次に、Airflowのdocker-composeファイルを取得します。これを行うため、ローカルのノートパソコンへのこのファイルのcurlを実行しましょう。\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\u003Eここで、docker-composeファイルを調整し、2つのフォルダをボリュームとして追加します。\u003Ccode\u003Edags\u003C/code\u003Eは、Airflowが取得して分析するためにAirflow DAGが配置されているフォルダです。\u003Ccode\u003Edbt\u003C/code\u003Eは、dbtモデルとCSVファイルを設定したフォルダです。\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\u003Eここで、追加のdocker-composeパラメータを含む追加ファイルを作成する必要があります。こうすることで、コンテナの起動時にdbtがインストールされます。\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\u003E次に、\u003Ccode\u003Edbt\u003C/code\u003Eプロジェクトと\u003Ccode\u003Edags\u003C/code\u003Eフォルダを作成する必要があります。\u003C/p\u003E\n","\u003Cp\u003EDBTプロジェクトの場合は、\u003Ccode\u003Edbt init dbt\u003C/code\u003Eを実行します。後のステップ4で、ここにdbtを設定します。\u003C/p\u003E\n","\u003Cp\u003Edagsフォルダの場合は、次を実行してフォルダを作成します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Emkdir dags\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eツリーレポジトリは次のようになります。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_1_tree_structure.png\" alt=\"Folderstructure\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDBTプロジェクトの設定\u003C/h2\u003E\n","\u003Cp\u003Eレポジトリを作成したので、次はDBTプロジェクトを設定します。\u003C/p\u003E\n","\u003Cp\u003E始める前に、少し時間を取ってDBTプロジェクトで何をしようとしているのかを理解しましょう。\u003C/p\u003E\n","\u003Cp\u003E下の図からわかるように、3つのcsvファイル（\u003Ccode\u003Ebookings_1\u003C/code\u003E、\u003Ccode\u003Ebookings_2\u003C/code\u003E、\u003Ccode\u003Ecustomers\u003C/code\u003E）があります。これらのcsvファイルをテーブルとしてSnowflakeにシードします。これについては、後のステップ4で詳しく説明します。\u003C/p\u003E\n","\u003Cp\u003E続いて、\u003Ccode\u003Ebookings_1\u003C/code\u003Eテーブルと\u003Ccode\u003Ebookings_2\u003C/code\u003Eテーブルを\u003Ccode\u003Ecombined_bookings\u003C/code\u003Eにマージします。次に、customer_idで\u003Ccode\u003Ecombined_bookings\u003C/code\u003Eテーブルと\u003Ccode\u003Ecustomer\u003C/code\u003Eテーブルを結合し、\u003Ccode\u003Eprepped_data\u003C/code\u003Eテーブルを作成します。\u003C/p\u003E\n","\u003Cp\u003E最後に、2つのビューを作成して、\u003Ccode\u003Eprepped_data\u003C/code\u003Eの分析と変換を実行します。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Ehotel_count_by_day.sql\u003C/code\u003E：ANALYSISスキーマにhotel_count_by_dayビューを作成し、日別のホテル予約数をカウントします。\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ccode\u003Ethirty_day_avg_cost.sql\u003C/code\u003E：ANALYSISスキーマにthirty_day_avg_costビューを作成し、過去30日間の予約の平均コストを計算します。\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-ja/data_engineering_with_apache_airflow_0_dbt_flow.png\" alt=\"dbt_structure\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eまず、Snowflakeコンソールに移動し、以下のスクリプトを実行しましょう。これにより、dbt_userとdbt_dev_roleが作成されます。その後、dbt_userのデータベースを設定します。\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\u003Eでログインし、次のコマンドを実行して\u003Ccode\u003EDEMO_dbt\u003C/code\u003Eデータベースを作成しましょう。\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-ja/data_engineering_with_apache_airflow_2_snowflake_console.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eここで、ステップ1で設定したプロジェクト\u003Ccode\u003Edbt_airflow\u003C/code\u003E > \u003Ccode\u003Edbt\u003C/code\u003Eに戻りましょう。\u003C/p\u003E\n","\u003Cp\u003E以下のそれぞれのファイルに対していくつかの構成を設定します。\u003Ccode\u003Edbt_project.yml\u003C/code\u003Eについては、modelsセクションを置き換えるだけでよいことに注意してください。\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\u003E次に、\u003Ccode\u003Epackages.yml\u003C/code\u003E内に配置した\u003Ccode\u003Efishtown-analytics/dbt_utils\u003C/code\u003Eをインストールします。これを行うには、\u003Ccode\u003Edbt\u003C/code\u003Eフォルダから\u003Ccode\u003Edbt deps\u003C/code\u003Eコマンドを実行します。\u003C/p\u003E\n","\u003Cp\u003Eここで、\u003Ccode\u003Emacros\u003C/code\u003Eフォルダの下に\u003Ccode\u003Ecustom_demo_macros.sql\u003C/code\u003Eというファイルを作成し、以下のsqlを入力します。\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\u003Eすべてが正しく完了すると、フォルダは次のようになります。注釈付きのボックスは、上で説明した内容です。\u003C/p\u003E\n","\u003Cp\u003E最後のステップは、\u003Ccode\u003Edb_utils\u003C/code\u003Eのdbtモジュールをインストールすることです。dbtディレクトリから次を実行します。\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language- \"\u003Edbt deps\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E関連するモジュールが\u003Ccode\u003Edbt_modules\u003C/code\u003Eフォルダにインストールされていることがわかります。\u003C/p\u003E\n","\u003Cp\u003Eここまでで、次のようなフォルダ構造になります。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_3_dbt_structure.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eこれでdbtの設定は完了です。次のセクションでは、csvファイルとdagsの作成に進みましょう。\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EdbtでのCSVデータファイルの作成\u003C/h2\u003E\n","\u003Cp\u003Eこのセクションでは、サンプルのCSVデータファイルと関連するSQLモデルを準備します。\u003C/p\u003E\n","\u003Cp\u003Eまず、dbtフォルダ内の\u003Ccode\u003Edata\u003C/code\u003Eフォルダに3つのExcelファイルを作成しましょう。\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\u003Eフォルダ構造は次のようになります。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_4_csv_files.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Emodelsフォルダへのdbtモデルの作成\u003C/h2\u003E\n","\u003Cp\u003Emodelsフォルダに\u003Ccode\u003Eanalysis\u003C/code\u003Eと\u003Ccode\u003Etransform\u003C/code\u003Eの2つのフォルダを作成します。分析と変換については、それぞれ以下のセクションに従ってください。\u003C/p\u003E\n","\u003Ch3\u003Etransformフォルダのdbtモデル\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Etransform\u003C/code\u003Eフォルダ内には3つのSQLファイルがあります。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003Ecombined_bookings.sql\u003C/code\u003E：上記の2つの予約のCSVファイルを結合し、\u003Ccode\u003ETRANSFORM\u003C/code\u003Eスキーマに\u003Ccode\u003ECOMBINED_BOOKINGS\u003C/code\u003Eビューを作成します。\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：\u003Ccode\u003ETRANSFORM\u003C/code\u003Eスキーマに\u003Ccode\u003ECUSTOMER\u003C/code\u003Eビューを作成します。\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：\u003Ccode\u003ETRANSFORM\u003C/code\u003Eスキーマに\u003Ccode\u003EPREPPED_DATA\u003C/code\u003Eビューを作成し、上記の手順で作成した\u003Ccode\u003ECUSTOMER\u003C/code\u003Eビューと\u003Ccode\u003ECOMBINED_BOOKINGS\u003C/code\u003Eビューの内部結合を実行します。\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フォルダのdbtモデル\u003C/h3\u003E\n","\u003Cp\u003E次に、\u003Ccode\u003Eanalysis\u003C/code\u003Eフォルダに進みましょう。\u003Ccode\u003Eanalysis\u003C/code\u003Eフォルダに移動し、以下の2つのSQLファイルを作成します。\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Ccode\u003Ehotel_count_by_day.sql\u003C/code\u003E：\u003Ccode\u003EANALYSIS\u003C/code\u003Eスキーマにhotel_count_by_dayビューを作成し、日別のホテル予約数をカウントします。\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：\u003Ccode\u003EANALYSIS\u003C/code\u003Eスキーマにthirty_day_avg_costビューを作成し、過去30日間の予約の平均コストを計算します。\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\u003Eファイル構造は次のようになります。dbtモデルはすでに完了しているので、Airflowの作業に進むことができます。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_dbt_models.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAirflow DAGの準備\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Edags\u003C/code\u003Eフォルダに、\u003Ccode\u003Einit.py\u003C/code\u003Eと\u003Ccode\u003Etransform_and_analysis.py\u003C/code\u003Eの2つのファイルを作成します。\u003Ccode\u003Einit.py\u003C/code\u003Eは初期化を行い、CSVデータを参照します。\u003Ccode\u003Etransform_and_analysis.py\u003C/code\u003Eは変換と分析を実行します。\u003C/p\u003E\n","\u003Cp\u003EAirflowを使用すると、\u003Ccode\u003Etransform_and_analysis\u003C/code\u003E DAGを毎日スケジュールできます。ただし、この例では手動でDAGをトリガーします。\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\u003EAirflowのdocker-composeファイルの実行\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003Edocker-compose up\u003C/code\u003Eを実行し、\u003Ca href=\"http://localhost:8080/\"\u003Ehttp://localhost:8080/\u003C/a\u003Eに移動しましょう。デフォルトのユーザー名は\u003Ccode\u003Eairflow\u003C/code\u003E、パスワードは\u003Ccode\u003Eairflow\u003C/code\u003Eです。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_2_airflow_url.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eここでは2つの変数を作成します。\u003Ccode\u003Eadmin > Variables\u003C/code\u003Eに移動し、\u003Ccode\u003E+\u003C/code\u003Eアイコンをクリックします。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_airflow_variables.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003Eまず、\u003Ccode\u003Edbt_user\u003C/code\u003Eのキーと値\u003Ccode\u003Edbt_user\u003C/code\u003Eを作成しましょう。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_airflow_username.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E次に、\u003Ccode\u003Edbt_password\u003C/code\u003Eの2番目のキーと値\u003Ccode\u003E<ADD IN YOUR PASSWORD>\u003C/code\u003Eを作成しましょう。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_airflow_password.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDAGのアクティブ化と実行\u003C/h2\u003E\n","\u003Cp\u003EDAGをアクティブ化します。\u003Ccode\u003E1_init_once_seed_data\u003C/code\u003Eと\u003Ccode\u003E2_daily_transformation_analysis\u003C/code\u003Eの青いボタンをクリックします。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_6_runnig_our_dags.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E1_init_once_seed_dataの実行\u003C/h3\u003E\n","\u003Cp\u003E次に、\u003Ccode\u003E1_init_once_seed_data\u003C/code\u003Eを実行してデータをシードしましょう。実行するには、DAGの右側の\u003Ccode\u003EActions\u003C/code\u003Eの下にある再生アイコンをクリックします。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_7_dag_init_successful.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003Eパブリックスキーマに作成されたテーブルのシードデータの表示\u003C/h3\u003E\n","\u003Cp\u003ESnowflakeインスタンスに戻ったときにすべてがうまくいっている場合は、\u003Ccode\u003EPUBLIC\u003C/code\u003Eスキーマに正常に作成されたツリーテーブルが表示されます。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_8_snowflake_successful_seed.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003E2_daily_transformation_analysisの実行\u003C/h3\u003E\n","\u003Cp\u003E次に、2番目のDAG \u003Ccode\u003E2_daily_transformation_analysis\u003C/code\u003Eを実行して、\u003Ccode\u003Etransform\u003C/code\u003Eモデルと\u003Ccode\u003Eanalysis\u003C/code\u003Eモデルを実行します。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_9_dag_transform_analysis_successful.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ccode\u003ETransform\u003C/code\u003Eビューと\u003Ccode\u003EAnalysis\u003C/code\u003Eビューが正常に作成されました。\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_10_snowflake_successful_transform_analysis.png\" alt=\"airflow\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003Eまとめ\u003C/h2\u003E\n","\u003Cp\u003Eおめでとうございます。dbtとSnowflakeを使用して最初のApache Airflowを作成しました。無料トライアルを続行し、自分のサンプルデータまたは本番データをロードして、このラボで扱っていないAirflowとSnowflakeのより高度な機能を試してみることをお勧めします。\u003C/p\u003E\n","\u003Ch3\u003E関連リソース：\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E18,000人を超えるデータ実務者が参加する\u003Ca href=\"https://www.getdbt.com/community/\"\u003EdbtコミュニティのSlack\u003C/a\u003Eに今すぐ参加してください。専用のSlackチャネル#db-snowflakeでは、Snowflake関連コンテンツを配信しています。\u003C/li\u003E\u003Cli\u003E簡単な\u003Ca href=\"https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html\"\u003EAirflow DAG\u003C/a\u003Eの作成方法に関するクイックチュートリアル\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003Eここまで学んだ内容：\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAirflow、dbt、Snowflakeの設定方法\u003C/li\u003E\u003Cli\u003EDAGを作成し、DAGからdbtを実行する方法\u003C/li\u003E\u003C/ul\u003E"],"title":"Apache Airflow、Snowflake、dbtによるデータエンジニアリング","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## 概要\n\n\n![アーキテクチャ](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_0_overall_architecture.png)\n\n多くの企業が、アジリティ、成長、運用効率をサポートするプラットフォーム上に構築された最新のデータ戦略に注目しています。Snowflakeはデータクラウドであり、あらゆるビジネスのデータパイプラインを簡素化し、企業がインフラストラクチャの管理やメンテナンスではなく、データと分析に集中できるようにする将来を見据えたソリューションです。\n\nApache Airflowは、データパイプラインの作成と管理に使用できるオープンソースのワークフロー管理プラットフォームです。Airflowは、タスクの有向非巡回グラフ（DAG）で構成されるワークフローを使用します。\n\n[dbt](https://www.getdbt.com/)は、[dbt Labs](https://www.getdbt.com/)によって管理されている最新のデータエンジニアリングフレームワークであり、Snowflakeなどのクラウドデータプラットフォームを活用した最新のデータアーキテクチャで非常に人気が高まっています。[dbt CLI](https://docs.getdbt.com/dbt-cli/cli-overview)は、DBTプロジェクトを実行するためのコマンドラインインターフェイスです。このCLIは無料で使用できるオープンソースです。\n\nこのバーチャルハンズオンラボでは、Airflowとdbtを使用するためのステップバイステップのガイドに従ってデータ変換ジョブスケジューラを作成します。\n\nでは始めましょう。\n\n### 前提条件\n\nこのガイドは、Pythonとdbtに関する基本的な実務知識があることを前提としています。\n\n### 学習する内容\n\n- Airflowなどのオープンソースツールを使用してデータスケジューラを作成する方法\n- DAGを作成してAirflowにアップロードする方法\n- dbt、Airflow、Snowflakeを使用してスケーラブルなパイプラインを構築する方法\n\n### 必要なもの\n\n始める前に以下のものが必要です。\n\n1. Snowflake\n2. **Snowflakeアカウント**\n3. **適切な権限で作成されたSnowflakeユーザー。**このユーザーには、DEMO_DB データベースにオブジェクトを作成する権限が必要です。\n4. GitHub\n5. **GitHubアカウント。**まだGitHubアカウントを持っていない場合は、無料で作成できます。アカウントの作成を開始するには、[Join GitHub](https://github.com/join)ページにアクセスしてください。\n6. **GitHubレポジトリ。**まだレポジトリを作成していない場合、または新しく作成したい場合は、[新しいレポジトリを作成](https://github.com/new)します。タイプは「`Public`」を選択します（どちらも使用できます）。また、現時点では、README、.gitignore、ライセンスの追加をスキップできます。\n7. 統合開発環境（IDE）\n8. **お気に入りのIDEとGitの統合。**お気に入りのIDEとGitをまだ統合していない場合は、[Visual Studio Code](https://code.visualstudio.com/)をお勧めします。これは無料で利用できるオープンソースの優れたIDEです。\n9. **コンピュータに複製されたプロジェクトレポジトリ。**Gitレポジトリの接続の詳細については、レポジトリを開き、ページ上部付近にある`HTTPS`リンクをコピーします。レポジトリに1つ以上のファイルがある場合は、ページ上部付近にあるの緑色の`Code`アイコンをクリックし、`HTTPS`リンクをコピーします。そのリンクをVS Codeまたはお好みのIDEで使用して、コンピュータにレポジトリを複製します。\n10. Docker\n11. **ノートパソコン上のDocker Desktop。**  Airflowをコンテナとして実行します。[Dockerの設定手順](https://docs.docker.com/desktop/)に従って、ご希望のOSにDocker Desktopをインストールしてください。\n\n### 構築するもの\n\n- dbtとSnowflakeを使用したシンプルで実用的なAirflowパイプライン\n\n\u003C!-- ------------------------ --\u003E\n## 環境設定\n\n\nまず、以下のコマンドを実行してフォルダを作成しましょう。\n\n```\nmkdir dbt_airflow && cd \"$_\"\n```\n\n次に、Airflowのdocker-composeファイルを取得します。これを行うため、ローカルのノートパソコンへのこのファイルのcurlを実行しましょう。\n\n```bash\ncurl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.3.0/docker-compose.yaml'\n```\n\nここで、docker-composeファイルを調整し、2つのフォルダをボリュームとして追加します。`dags`は、Airflowが取得して分析するためにAirflow DAGが配置されているフォルダです。`dbt`は、dbtモデルとCSVファイルを設定したフォルダです。\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\nここで、追加のdocker-composeパラメータを含む追加ファイルを作成する必要があります。こうすることで、コンテナの起動時にdbtがインストールされます。\n\n`.env`\n\n```bash\n_PIP_ADDITIONAL_REQUIREMENTS=dbt==0.19.0\n```\n\n---\n\n次に、`dbt`プロジェクトと`dags`フォルダを作成する必要があります。\n\nDBTプロジェクトの場合は、`dbt init dbt`を実行します。後のステップ4で、ここにdbtを設定します。\n\ndagsフォルダの場合は、次を実行してフォルダを作成します。\n\n```\nmkdir dags\n```\n\nツリーレポジトリは次のようになります。\n\n![Folderstructure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_1_tree_structure.png)\n\n\u003C!-- ------------------------ --\u003E\n## DBTプロジェクトの設定\n\n\nレポジトリを作成したので、次はDBTプロジェクトを設定します。\n\n始める前に、少し時間を取ってDBTプロジェクトで何をしようとしているのかを理解しましょう。\n\n下の図からわかるように、3つのcsvファイル（`bookings_1`、`bookings_2`、`customers `）があります。これらのcsvファイルをテーブルとしてSnowflakeにシードします。これについては、後のステップ4で詳しく説明します。\n\n続いて、`bookings_1`テーブルと`bookings_2`テーブルを`combined_bookings`にマージします。次に、customer_idで`combined_bookings`テーブルと`customer`テーブルを結合し、`prepped_data`テーブルを作成します。\n\n最後に、2つのビューを作成して、`prepped_data`の分析と変換を実行します。\n\n1) `hotel_count_by_day.sql`：ANALYSISスキーマにhotel_count_by_dayビューを作成し、日別のホテル予約数をカウントします。\n\n2) `thirty_day_avg_cost.sql`：ANALYSISスキーマにthirty_day_avg_costビューを作成し、過去30日間の予約の平均コストを計算します。\n\n![dbt_structure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_0_dbt_flow.png)\n\nまず、Snowflakeコンソールに移動し、以下のスクリプトを実行しましょう。これにより、dbt_userとdbt_dev_roleが作成されます。その後、dbt_userのデータベースを設定します。\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`でログインし、次のコマンドを実行して`DEMO_dbt`データベースを作成しましょう。\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-ja/data_engineering_with_apache_airflow_2_snowflake_console.png)\n\nここで、ステップ1で設定したプロジェクト`dbt_airflow` \u003E `dbt`に戻りましょう。\n\n以下のそれぞれのファイルに対していくつかの構成を設定します。`dbt_project.yml`については、modelsセクションを置き換えるだけでよいことに注意してください。\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\n次に、`packages.yml`内に配置した`fishtown-analytics/dbt_utils`をインストールします。これを行うには、`dbt`フォルダから`dbt deps`コマンドを実行します。\n\nここで、`macros`フォルダの下に`custom_demo_macros.sql`というファイルを作成し、以下のsqlを入力します。\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\nすべてが正しく完了すると、フォルダは次のようになります。注釈付きのボックスは、上で説明した内容です。\n\n最後のステップは、`db_utils`のdbtモジュールをインストールすることです。dbtディレクトリから次を実行します。\n\n``` \ndbt deps\n```\n\n関連するモジュールが`dbt_modules`フォルダにインストールされていることがわかります。\n\nここまでで、次のようなフォルダ構造になります。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_3_dbt_structure.png)\n\nこれでdbtの設定は完了です。次のセクションでは、csvファイルとdagsの作成に進みましょう。\n\n\u003C!-- ------------------------ --\u003E\n## dbtでのCSVデータファイルの作成\n\n\nこのセクションでは、サンプルのCSVデータファイルと関連するSQLモデルを準備します。\n\nまず、dbtフォルダ内の`data`フォルダに3つのExcelファイルを作成しましょう。\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\nフォルダ構造は次のようになります。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_4_csv_files.png)\n\n\u003C!-- ------------------------ --\u003E\n## modelsフォルダへのdbtモデルの作成\n\n\nmodelsフォルダに`analysis`と`transform`の2つのフォルダを作成します。分析と変換については、それぞれ以下のセクションに従ってください。\n\n### transformフォルダのdbtモデル\n\n`transform`フォルダ内には3つのSQLファイルがあります。\n\n1) `combined_bookings.sql`：上記の2つの予約のCSVファイルを結合し、`TRANSFORM`スキーマに`COMBINED_BOOKINGS`ビューを作成します。\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`：`TRANSFORM`スキーマに`CUSTOMER`ビューを作成します。\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`：`TRANSFORM`スキーマに`PREPPED_DATA`ビューを作成し、上記の手順で作成した`CUSTOMER`ビューと`COMBINED_BOOKINGS`ビューの内部結合を実行します。\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フォルダのdbtモデル\n\n次に、`analysis`フォルダに進みましょう。`analysis`フォルダに移動し、以下の2つのSQLファイルを作成します。\n\n1) `hotel_count_by_day.sql`：`ANALYSIS`スキーマにhotel_count_by_dayビューを作成し、日別のホテル予約数をカウントします。\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`：`ANALYSIS`スキーマにthirty_day_avg_costビューを作成し、過去30日間の予約の平均コストを計算します。\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\nファイル構造は次のようになります。dbtモデルはすでに完了しているので、Airflowの作業に進むことができます。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_dbt_models.png)\n\n\u003C!-- ------------------------ --\u003E\n## Airflow DAGの準備\n\n\n`dags`フォルダに、`init.py`と`transform_and_analysis.py`の2つのファイルを作成します。`init.py`は初期化を行い、CSVデータを参照します。`transform_and_analysis.py`は変換と分析を実行します。\n\nAirflowを使用すると、`transform_and_analysis` DAGを毎日スケジュールできます。ただし、この例では手動でDAGをトリガーします。\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## Airflowのdocker-composeファイルの実行\n\n\n`docker-compose up`を実行し、[http://localhost:8080/](http://localhost:8080/)に移動しましょう。デフォルトのユーザー名は`airflow`、パスワードは`airflow`です。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_2_airflow_url.png)\n\nここでは2つの変数を作成します。`admin \u003E Variables`に移動し、`+`アイコンをクリックします。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_airflow_variables.png)\n\nまず、`dbt_user`のキーと値`dbt_user`を作成しましょう。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_airflow_username.png)\n\n次に、`dbt_password`の2番目のキーと値`\u003CADD IN YOUR PASSWORD\u003E`を作成しましょう。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_5_airflow_password.png)\n\n\u003C!-- ------------------------ --\u003E\n## DAGのアクティブ化と実行\n\nDAGをアクティブ化します。`1_init_once_seed_data`と`2_daily_transformation_analysis`の青いボタンをクリックします。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_6_runnig_our_dags.png)\n\n### 1_init_once_seed_dataの実行\n\n次に、`1_init_once_seed_data`を実行してデータをシードしましょう。実行するには、DAGの右側の`Actions`の下にある再生アイコンをクリックします。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_7_dag_init_successful.png)\n\n### パブリックスキーマに作成されたテーブルのシードデータの表示\n\nSnowflakeインスタンスに戻ったときにすべてがうまくいっている場合は、`PUBLIC`スキーマに正常に作成されたツリーテーブルが表示されます。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_8_snowflake_successful_seed.png)\n\n### 2_daily_transformation_analysisの実行\n\n次に、2番目のDAG `2_daily_transformation_analysis`を実行して、`transform`モデルと`analysis`モデルを実行します。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_9_dag_transform_analysis_successful.png)\n\n`Transform`ビューと`Analysis`ビューが正常に作成されました。\n\n![airflow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/data-engineering-with-apache-airflow-ja/data_engineering_with_apache_airflow_10_snowflake_successful_transform_analysis.png)\n\n\u003C!-- ------------------------ --\u003E\n## まとめ\n\n\nおめでとうございます。dbtとSnowflakeを使用して最初のApache Airflowを作成しました。無料トライアルを続行し、自分のサンプルデータまたは本番データをロードして、このラボで扱っていないAirflowとSnowflakeのより高度な機能を試してみることをお勧めします。\n\n### 関連リソース：\n\n- 18,000人を超えるデータ実務者が参加する[dbtコミュニティのSlack](https://www.getdbt.com/community/)に今すぐ参加してください。専用のSlackチャネル#db-snowflakeでは、Snowflake関連コンテンツを配信しています。\n- 簡単な[Airflow DAG](https://airflow.apache.org/docs/apache-airflow/stable/tutorial.html)の作成方法に関するクイックチュートリアル\n\n### ここまで学んだ内容：\n\n- Airflow、dbt、Snowflakeの設定方法\n- DAGを作成し、DAGからdbtを実行する方法\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo 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