Wrangler は、Cloud Data Fusion Studio インターフェース内のビジュアル データ準備ツールです。抽出、変換、読み込み（ETL）パイプラインで使用する前に、データをクリーニングして変換できます。Wrangler は、データセット全体でロジックを実行する前に、データのサンプルに対して 1 か所（プレビュー）で変換を適用します。このプレビューは、変換を適用し、変換がデータセット全体にどのように影響するかを把握するのに役立ちます。
[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[[["\u003cp\u003eWrangler is a visual data preparation tool in Cloud Data Fusion Studio that allows users to clean and transform data before it's used in ETL pipelines.\u003c/p\u003e\n"],["\u003cp\u003eWrangler operates on a sample of data, allowing users to preview transformations and understand their impact before applying them to the entire dataset.\u003c/p\u003e\n"],["\u003cp\u003eDirectives are single instructions within Wrangler for manipulating data, and multiple directives are organized together in a Recipe, executed sequentially as transformation steps.\u003c/p\u003e\n"],["\u003cp\u003eWrangler offers a workspace with a visual interface to add and manage transformation steps, view data quality, explore data schemas, and also offers a Power Mode (CLI) to use declarative syntax for complex directives.\u003c/p\u003e\n"],["\u003cp\u003eWrangler transformations can be converted into Cloud Data Fusion pipelines, but it is crucial to edit recipes within the Wrangler interface to ensure automatic output schema updates.\u003c/p\u003e\n"]]],[],null,["# Wrangler overview\n\nWrangler is a visual data preparation tool within the Cloud Data Fusion\nStudio interface. It lets you clean and transform data before using it in\nExtract, Transform, Load (ETL) pipelines. Wrangler applies transformations on a\nsample of your data in one place (called a *Preview*) before running the logic\non the entire dataset. This preview helps you apply transformations and gain an\nunderstanding of how they affect the entire dataset.\n\nWrangler directives\n-------------------\n\nA directive is a single instruction used within the Wrangler. Directives\nspecify how to manipulate your data, such as transforming, filtering, or\npivoting individual records.\n\nThe following concepts are related to directives:\n\nRecipe\n: A recipe is a set of directives. It consists of one or more directives.\n\nTransformation step\n: A transformation step is an implementation of a data transformation directive,\n operating on a single record or set of records. A transformation step can\n generate zero or more records from applying a directive. Wrangler\n applies the transformation steps in the order listed in the recipe.\n\nWrangler components\n-------------------\n\nThe following sections explain components of Wrangler in the\nCloud Data Fusion Studio.\n\n### Wrangler workspace\n\nThe Wrangler workspace is a page in the Cloud Data Fusion Studio interface\nwhere you parse, blend, cleanse, and transform datasets. On the **Workspace**\npage, you can do the following:\n\n- Add transformation steps to a recipe using the drop-down menu in each column.\n- View or delete steps in a recipe by selecting the **Transformation steps** tab.\n- Discover columns with blank fields and other information by checking the **Data quality** bar.\n- View the schema for the dataset by clicking **More**.\n- Create a data pipeline with a source plugin for the dataset, and the Wrangler transformation with the recipe containing the transformation steps, which are executed when the pipeline runs.\n\n| **Note:** In Wrangler, you can work on multiple datasets from the same connection or different connections. Each dataset appears on a separate tab in the Wrangler workspace.\n\n### Wrangler Power Mode (CLI)\n\nTo specify directives using declarative syntax, use the Power Mode (CLI). It's\nuseful for the following tasks:\n\n- Using directives that aren't available in the Studio interface\n- Adding user-defined directives\n- Applying a directive to multiple columns\n\nTo use Wrangler Power Mode, enter directives in the black bar at the bottom of\nthe Wrangler **Data** tab.\n\n### Wrangler Insights tab\n\nYou can use the **Insights** tab on the Wrangler page to perform data discovery\non a dataset.\n\nLimitations\n-----------\n\n- Wrangler is only supported for batch ETL pipelines.\n- Wrangler applies transformation only on the sample data. This sample data is limited to the first 1000 records.\n- Wrangler requires connections to be created with the source. For more information, see [Create and manage connections](/data-fusion/docs/how-to/managing-connections).\n- Wrangler always requires at least one Wrangler workspace to be open.\n- Clicking the Wrangle button in the Wrangler transformation isn't supported.\n\nNavigate to Wrangler in Cloud Data Fusion\n-----------------------------------------\n\nYou can access Wrangler in two ways from the Cloud Data Fusion Studio\ninterface:\n\n- To open the Cloud Data Fusion Wrangler workspace, [go to the Cloud Data Fusion Studio](/data-fusion/docs/create-data-pipeline#navigate-web-interface) and click **Wrangler**.\n- To configure Wrangler properties, go to the Cloud Data Fusion Studio, and click **Studio** \\\u003e expand_more **Transformations** \\\u003e **Wrangler**.\n\n### Connect to a data source\n\nWrangler supports various data sources, such as BigQuery,\nCloud Storage, and external databases (with additional configuration). To use\nWrangler, you must create a connection with the source.\n\nTo create the connection, go to the **Connections** list and select the\nconnection to your data source. For more information, see\n[Create and manage connections](/data-fusion/docs/how-to/managing-connections).\n| **Note:** If you access Wrangler from the plugin palette, to open the Wrangler workspace from the plugin pallet, open the Wrangler plugin properties and click **Wrangle**.\n\n### Explore and preview data\n\nWrangler displays a sample of your data (typically 1000 rows) for inspection.\nYou can get an overview of the data schema, including data types and basic\nstatistics.\n\n### Apply directives\n\nWrangler offers a variety of built-in directives for common data wrangling\ntasks.\n\n- Drag the chosen directive onto a specific column or the data preview window.\n- Each directive has configuration options to customize its behavior.\n\nFor more information, see [Wrangler command-line directives](/data-fusion/docs/concepts/wrangler-cli-directives).\n\n### Preview transformation results\n\nAs you apply directives, the data preview window dynamically updates to reflect\nthe changes. This lets you see the immediate impact of each transformation\non your data.\n\n### Refine and iterate\n\nTo refine your data wrangling process, continue adding directives, modifying\nconfigurations, and reviewing the preview.\n\nWrangler's visual interface helps you experiment and ensure that your\ntransformations produce the expected outcome.\n\n### Add transformations to a pipeline\n\nWhile Wrangler itself isn't a persistent storage solution,\nCloud Data Fusion offers ways to capture your wrangling logic:\n\n- **Create a pipeline**. From the Wrangler workspace, convert your Wrangler\n transformations into a Cloud Data Fusion pipeline by following these\n steps:\n\n 1. Click **Create pipeline**.\n 2. Select **Batch pipeline** . The **Pipeline Studio** page opens with a pipeline that has a source and a Wrangler transformation.\n- **Apply transformations** . If you're using the Wrangler plugin on the\n **Studio** page, convert your Wrangler transformations into a\n Cloud Data Fusion pipeline by clicking **Apply**.\n\n| **Note:** For complex transformations requiring persistent storage, consider creating custom User Defined Directives (UDDs) in Python or Scala. These UDDs can be saved and reused across Wrangler sessions.\n\n### Edit Recipes\n\nWhen you use the Wrangler workspace to create a Wrangler transformation, after\nyou add the Wrangler transformation to a pipeline, it's recommended that you use\nthe Wrangler interface to add or edit recipes.\n\nIn the Wrangler transformation, if you manually edit the recipe or add new steps\nto the recipe and the changes affect the output schema, you must manually update\nthe output schema in the Wrangler transformation to match the changes in the\nrecipe. Only recipes created or edited in the Wrangler workspace will\nauto-create and auto-update the output schema in the Wrangler transformation.\n\nTo edit a recipe in the Wrangler transformation that was created in the Wrangler\nweb interface, follow these steps:\n\n1. Go to the Wrangler node in your pipeline and click **Properties**.\n2. Click **Wrangle**.\n3. Edit or add a new recipe.\n4. Click **Apply**.\n\nWhat's next\n-----------\n\n- Learn more about [Wrangler CLI directives](/data-fusion/docs/concepts/wrangler-cli-directives)."]]
