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data-anchor=\"why-data-lineage-tracking-matters\"\u003Eデータリネージの追跡の重要性\u003C/li\u003E\r\n\u003Cli data-anchor=\"types-of-data-lineage-tracking\"\u003Eデータリネージの追跡の種類\u003C/li\u003E\r\n\u003Cli data-anchor=\"how-automated-data-lineage-tracking-works\"\u003Eデータリネージの自動追跡の仕組み\u003C/li\u003E\r\n\u003Cli data-anchor=\"key-benefits-of-data-lineage-tracking\"\u003Eデータリネージの追跡の主なメリット\u003C/li\u003E\r\n\u003Cli data-anchor=\"common-challenges-in-data-lineage-tracking\"\u003Eデータリネージの追跡での一般的な課題\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-lineage-tracking-best-practices\"\u003Eデータリネージの追跡のベストプラクティス\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-lineage-tracking-for-ai-and-ml-governance\"\u003EAIおよびMLガバナンスのためのデータリネージの追跡\u003C/li\u003E\r\n\u003Cli data-anchor=\"when-lineage-becomes-operationally-useful\"\u003Eリネージが運用上役立つ場合\u003C/li\u003E\r\n\u003Cli data-anchor=\"data-lineage-tracking-faqs\"\u003Eデータリネージの追跡に関するよくある質問\u003C/li\u003E\r\n\u003Cli data-anchor=\"resources\"\u003E関連リソース\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"experiencefragment":{"id":"experiencefragment-44596cbb00","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/ja/site/share-icons/share-icons-no-title/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment","classNames":"aem-xf",":items":{"root":{"columnClassNames":{"container_949147658":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-7d432ef796",":type":"snowflake-site/components/container",":items":{"container_949147658":{"columnClassNames":{"markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/ja/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"]}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"section_0":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-5b34db14bc","type":"heading2","lines":["概要"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-e35728b60e","text":"\u003Cp\u003Eデータリネージの追跡とは、データがシステム、パイプライン、変換処理の間をどのように移動するかについて、利用可能な記録を継続的に取得し、維持するプロセスのことです。具体的には、上流のソース、下流の依存関係、変換ロジック、フィールドレベルの関係性、そして問題のトラブルシューティング、変更リスクの評価、ガバナンスのサポートに必要な運用コンテキストを文書化することを意味します。\u003C/p\u003E\r\n\u003Cp\u003Eデータが単一のパイプラインだけを移動することはもはや稀です。データが関与するシステム、変換、下流の依存関係が増えるほど、何がなぜ変更されたのかを理解することは困難になります。たとえば、1つのテーブルがダッシュボード、ML（機械学習）の特徴量、規制レポートで再利用されたとします。その後、上流で列の定義が変更されたとしても、3つの異なる場所で数値に食い違いが生じるまでは誰も変更に気がつきません。その時点で、データに対する信頼はすでに損なわれています。この原因の追及は現実的に困難な場合があり、コンプライアンスの応答時間や意思決定などに影響を及ぼします。\u003C/p\u003E\r\n\u003Cp\u003Eだからこそ、データリネージの追跡は現在、あれば便利なものではなく、実務上の必須要件となっています。チームには、データの出所、変更履歴、依存関係、および上流で変更が発生した際に影響を受ける可能性のあるアセットを示す、最新の記録が必要です。また、AIシステムがより多くのエンタープライズデータを使用するようになるにつれて、この記録は再現性、説明可能性、ガバナンスのためのコントロールレイヤーの一部にもなります。\u003C/p\u003E\r\n\u003Cp\u003E本ガイドでは、データリネージの追跡の概要、自動追跡の仕組み、チームが実装時に直面する問題について説明します。さらに、ガバナンス、運用、AI全体でリネージを役立てる方法も解説します。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"]},"section_1":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what-is-data-lineage-tracking",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-8c61b98a35","type":"heading2","lines":["データリネージの追跡とは"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-7d811dd9f7","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eデータリネージの追跡とは、データが時間の経過とともにシステム間でどのように移動し、変換され、変化するかを文書化するプロセスです。モダンな環境では、通常、テーブルレベル（多くの場合、列レベル）でメタデータを継続的に取得することを意味します。これにより、チームは静的な図ではなく、生きたマップを基に作業できるようになります。\u003C/p\u003E\r\n\u003Cp\u003Eなお、実務担当者は「データリネージ」と「データリネージの追跡」を同じ意味で使用することがありますが、本来データリネージとデータリネージの追跡は異なるものと考えるべきです。データリネージはより広範な概念であり、データが起点から目的地までたどるプロセス全体のことを指します。データリネージの追跡は、パイプラインの実行やスキーマの進化に伴い、出所、変換、依存関係、変更を取得することでその流れを最新の状態に保つ運用上の規律のことを指します。しかし、現状多くの人はデータリネージの追跡の活動を指して「データリネージ」という用語を使用しています。\u003C/p\u003E\r\n\u003Cp\u003E本ガイドでは、データリネージの追跡の運用レイヤーに焦点を当てています。より広範な概念について知りたい方は、\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/data-lineage/\" target=\"_self\"\u003Eデータリネージ\u003C/a\u003Eの記事をご覧ください。\u003C/p\u003E\r\n\u003Cp\u003E具体的には、リネージの追跡には通常、次の4つの要素が含まれます。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E出所の取得：\u003C/b\u003Eデータが環境のどこに入力され、どのソースオブジェクトまたはシステムがそれを提供したか\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E変更の時系列記録：\u003C/b\u003E結合、フィルター、計算、および手続き型のステップがデータをどのように変更したか\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E依存関係のマッピング：\u003C/b\u003Eどの下流テーブル、ダッシュボード、モデル、レポートがそのデータに依存しているか\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E継続的な監視：\u003C/b\u003Eコード、スキーマ、プロセスが変化する中で、リネージをどのように最新の状態に保つか\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E役立つリネージの記録とは単なるオブジェクト名の連なりではなく、チームが実際の疑問に答えるための十分なコンテキストを提供するものでなければなりません。具体的には、「どのダッシュボードがこのフィールドに依存しているのか」、「どのタスクがこのテーブルにデータを入力したのか」、「どのモデルのバージョンがこの特徴量のビューを使用したのか」、「元のソースと現在レポートに表示されている数値との間でどんな変更があったのか」などをわかるようにしておく必要があるのです。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"]},"section_2":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why-data-lineage-tracking-matters",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-c53e506f26","type":"heading2","lines":["データリネージの追跡の重要性"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-1a96792a3b","text":"\u003Cp\u003E近年、データを扱う作業はもはや一方通行ではなくなっているため、リネージの追跡の重要性が増しています。1つのソーステーブルが、変換ジョブ、セマンティックレイヤー、ダッシュボード、リバースETLワークフロー、ML（機械学習）パイプラインに同時にデータを供給することもあります。上流での小さな変更であっても、下流に長い影響の連鎖を生み出す可能性があります。\u003C/p\u003E\r\n\u003Cp\u003Eリネージの追跡の価値は、データの移動を継続的な運用記録として読み取れるようにし、何が起こったかを後からチームが再構築する手間を省くことにあります。この記録が欠けていると、作業が思うように進みません。指標の変更の調査、計画された更新のレビュー、または結果がどのように生成されたかの把握を行うチームは、散在するコード、システムの履歴、組織の記憶から答えを組み立てる必要があります。\u003C/p\u003E\r\n\u003Cp\u003Eガバナンスが適用された環境では、単純にポリシーに記述された以上のものが求められるため、規制によるプレッシャーも新たな課題となります。こうした環境では、監査に耐えうる\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://artificialintelligenceact.eu/article/10/\"\u003E記録が必要\u003C/a\u003Eになります。実際には、特にリスク、コンプライアンス、\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.com/ja/data-governance/ai/\" target=\"_self\"\u003EAI向けガバナンス\u003C/a\u003Eに関連するワークフローにおいて、データがどのように取得、集計、変換、レポートされたかを文書化できることを意味します。\u003C/p\u003E\r\n\u003Cp\u003Eデータにまつわる作業が1つのチームの境界内に留まることはめったにないため、リネージを追跡することは運用上の強力なメリットもあります。多くの場合、エンジニア、アナリスト、スチュワード、プラットフォーム所有者は、異なる目的で同じアセットに依存しています。そのため、依存関係を可視化と関係者への共有を怠ると、一部環境での変更が他の場所での混乱や手戻りを引き起こす可能性があります。パイプラインが進化し、アセットがワークフロー間で再利用されるようになるにつれて、データリネージの追跡は何が起こったかを事後把握するだけでなく、変更が実行される前にどのような影響を与えるかをチームが予測するのにも役立ちます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"]},"section_3":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types-of-data-lineage-tracking",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e074c1e767","type":"heading2","lines":["データリネージの追跡の種類"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-4b63a248be","text":"\u003Cp\u003Eすべてのリネージの追跡が同じようなことを明らかにするのではありません。リネージはさまざまな方法で追跡でき、それに応じて多様な質問の答えを出すことができます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-8c83d3e60b","type":"heading3","lines":["粒度別"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-879b4f64d1","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eテーブルレベルのリネージ：\u003C/b\u003Eテーブルレベルのリネージは、データセットがパイプライン全体でどのように接続されているかを示します。これはほとんどのケースで、大まかな依存関係のマッピング、オンボーディング、および最初に行う影響分析には十分です。たとえば、顧客アナリティクステーブルが複数のステージングテーブルと選別された1つの顧客テーブルに依存している場合、テーブルレベルのリネージを使用すれば、データセットの接続状況をすばやく可視化できます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E列レベルのリネージ：\u003C/b\u003E列レベルのリネージでは、個々のフィールドがコピー、フィルタリング、結合、名前変更、または計算される過程を追跡します。これは、測定基準が少数の機密フィールドや規制対象フィールドに依存しており、レポート内の1つの値がどのように導き出されたかをチームが正確に知る必要がある場合に重要になります。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eクロスシステムのリネージ：\u003C/b\u003Eクロスシステムのリネージでは、1つのプラットフォームだけで止まることなく、ツールや環境をまたいでデータを追跡します。これは、\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/fundamentals/data-ingestion/\"\u003Eデータ取り込み（データインジェスト）\u003C/a\u003E、\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003Eデータ変換（データトランスフォーメーション）\u003C/a\u003E、\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/fundamentals/data-orchestration/\"\u003Eデータオーケストレーション\u003C/a\u003E、BI、MLが複数のシステムに分散している場合に重要になります。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-88eb02c612","type":"heading3","lines":["方向別"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-af45597b6a","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eフォワードリネージ：\u003C/b\u003Eフォワードリネージは、データの起点から目的地までデータを追跡します。チームはこれを使用して、変更を加える前に、影響範囲や内容を評価します。たとえばエンジニアがカラムの廃止やタスクの変更を計画している場合、フォワードリネージを活用することで、何が機能しなくなるか、誰が下流のアセットを所有しているか、どのレポート、アプリ、モデルに更新が必要になるかを特定できます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eバックワードリネージ：\u003C/b\u003Eバックワードリネージは、データの出力先から逆方向に追跡を始め、上流に向かって起点までたどります。チームはこれを、根本原因分析、インシデント対応、デバッグに使用できます。たとえばKPIが予期せず変動した場合、バックワードリネージを行うことで、問題の原因が遅れて到着した情報源によるものなのか、変換の変更やタスクの失敗なのか、またはさらに上流で発生したセマンティックの不一致のいずれであるかを特定できます。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-227ec89571","type":"heading3","lines":["スコープ別"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-48e66e36cd","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eテクニカルリネージ：\u003C/b\u003Eテクニカルリネージは、データがシステム間で物理的にどのように移動し、変化するかを記述します。たとえば、エンジニアがパイプライン、変換、オーケストレーションの手順、プラットフォームの関係を確認するために使用するビューがこれに該当します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eビジネスリネージ：\u003C/b\u003Eビジネスリネージはコンテキストを追加し、エンジニアリング部門以外でもリネージグラフを活用できるようにします。これには、ビジネス定義、所有者情報、用語集の用語、タグ、ポリシーのコンテキスト、認定ステータス、想定されるリフレッシュパターンなどが含まれます。このレイヤーがないと、リネージグラフは技術的に正確であっても、アナリスト、スチュワード、コンプライアンスチームにとって解釈が難しいものになる可能性があります。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3"]},"section_4":{"columnClassNames":{"text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how-automated-data-lineage-tracking-works",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-82b6f33177","type":"heading2","lines":["データリネージの自動追跡の仕組み"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-057127b98c","text":"\u003Cp\u003Eデータリネージの自動追跡は、メタデータのキャプチャから始まります。クエリの実行、パイプラインの実行、オブジェクトの変更に伴い、システムはソースの入力、変換、依存関係、出力に関するシグナルを生成します。その後、リネージツールがこれらのシグナルを組み立て、データが環境内をどのように移動したかを示す有用なリネージマップを作成します。このマップ作成にはいくつかの手法や技術があり、それぞれ異なる目的を果たします。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-95f65f18a9","type":"heading3","lines":["メタデータをキャプチャする手法"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-a5e083678e","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eクエリの解析：\u003C/b\u003E解析ではSQLを読み取り、結合、フィルター、挿入、マージ、変換ロジックからリネージを推測します。ソースコードが利用可能で標準化されている場合、解析によって、特に列レベルで詳細なリネージを生成できます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eログベースの追跡：\u003C/b\u003E一部のシステムは、クエリログ、実行履歴、またはプラットフォームのアクティビティ記録からリネージを推測します。これは、コードが一元管理されていない場合や、リポジトリ上の想定される動作ではなく、実際に実行されたことの証拠をチームが必要とする場合に役立ちます。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E組み込み型のリネージ：\u003C/b\u003E一部のオーケストレーションツールや変換ツールは、実行の一環としてリネージを出力します。これにより、切断されたメタデータソースから後で再構築されるのではなく、パイプラインの実行時に同時にリネージが作成されるため、リネージの最新性（鮮度）が向上します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAPIベースのデータ取得：\u003C/b\u003EプラットフォームがAPIや関数を通じてリネージを公開している場合、チームはデータの依存関係を直接問い合わせして確認できます。たとえば\u003Ca href=\"https://www.snowflake.com/ja/\" target=\"_blank\"\u003ESnowflake\u003C/a\u003Eでは、\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/ja/sql-reference/functions/get_lineage-snowflake-core\"\u003EGET\u003Cb\u003E_\u003C/b\u003ELINEAGE\u003C/a\u003E関数を使用することで、データの流れる方向や距離を含む、上流または下流のリネージ情報を取得できます。これにより、画面上のマップを目視するだけでなく、プログラムを使ってリネージを自動的に検査することが可能になります。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-a4428c4b4a","type":"heading3","lines":["リネージの組み立て手法"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-d99830e279","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003Eパターンベースの組み立て：\u003C/b\u003E完全な変換ロジックが利用できない場合、一部のシステムはメタデータのヒューリスティクス（必ずしも正しい答えよりも経験や感覚に基づく答えを導き出す手法）を使用して、正解の可能性が高い関係性を推測します。これはカバレッジの向上に役立ちますが、通常、解析や組み込み型よりも信頼性が低くなります。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E解析ベースの組み立て：\u003C/b\u003Eこのアプローチでは、SQL、Python、Spark、または同様のロジックをリバースエンジニアリングして、より正確な依存関係マップを構築します。コードが一貫しており、一元的にアクセスできる場合に最も効果を発揮します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eタグベースの組み立て：\u003C/b\u003Eチームによっては、開発者のアノテーションやメタデータタグを付与して、データソースの起点、変換段階、またはガバナンスのコンテキストを示します。これにより解釈が容易になりますが、規律ある維持管理が必要です。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E自己完結型の組み立て：\u003C/b\u003E最も強力なリネージ環境は通常、プラットフォーム内での通常の実行に付随してリネージを生成します。リネージは実際に作業が行われる場所で生成されるため、コネクタの乱立、メタデータの遅延、および照合作業が軽減されます。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-1bc1edff9b","type":"heading3","lines":["プラットフォーム組み込み型の追跡"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-c56a36133f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eプラットフォーム組み込み型の追跡は、他のものとは明確に異なります。このモデルはリネージがデータプラットフォームに組み込まれているため、外部スキャンや同期ジョブを通じて後からつなぎ合わせるのではなく、通常のオブジェクト作成、クエリ実行、プロセスアクティビティを通じて記録が生成されます。\u003C/p\u003E\r\n\u003Cp\u003Eこれにより、運用モデルがいくつかの点で他のものと異なります。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E維持すべきコネクタの減少\u003C/li\u003E\r\n\u003Cli\u003Eメタデータ取り込みの遅延の減少\u003C/li\u003E\r\n\u003Cli\u003E視覚化されたリネージと実際のプラットフォームの状態の照合の減少\u003C/li\u003E\r\n\u003Cli\u003E同じ環境内でのリネージ、ガバナンス、アクセス制御の連携の強化\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESnowflakeのネイティブなリネージ機能は、このアプローチの良い例です。\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/product/features/horizon/\"\u003EHorizonカタログ\u003C/a\u003Eを使用すると、プラットフォームは起点からターゲットオブジェクトへのデータの流れを追跡し、データがどこから来たのか、またはどこへ行くのかを\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/ja/user-guide/ui-snowsight\"\u003ESnowsight\u003C/a\u003Eで表示できます。また、自動化された列レベル（サポートされている場合）、タスクレベル、および\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/ja/user-guide/external-lineage\"\u003E外部リネージ\u003C/a\u003Eも提供します。\u003C/p\u003E\r\n\u003Cp\u003E実装アプローチをより広く評価している読者にとって、ここはツールの比較検討議論が関連してくる部分でもあります。コネクタを多用するアーキテクチャも機能はしますが、メタデータを最新の状態に保ち、システム間のギャップを照合するためにはより多くのメンテナンスが必要になることがよくあります。プラットフォーム組み込み型の追跡は、設計上その負担の一部を軽減します。\u003C/p\u003E\r\n\u003Cp\u003Eデータリネージツールの比較検討に関しては、\u003Ca rel=\"noopener noreferrer\" target=\"_self\" href=\"https://www.snowflake.com/ja/data-governance/data-lineage/tools/\"\u003Eデータリネージツール：機能と最適なツールの選択方法\u003C/a\u003E（評価基準とプラットフォームのカテゴリに焦点を当てた別のガイド）を参考にしてみてください。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3"]},"section_5":{"columnClassNames":{"h3_title_3":"aem-GridColumn aem-GridColumn--default--12","h3_title_4":"aem-GridColumn aem-GridColumn--default--12","text_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_5":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_6":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","text_5":"aem-GridColumn aem-GridColumn--default--12","text_4":"aem-GridColumn aem-GridColumn--default--12","text_7":"aem-GridColumn aem-GridColumn--default--12","text_6":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"key-benefits-of-data-lineage-tracking",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-02cb8d076d","type":"heading2","lines":["データリネージの追跡の主なメリット"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-9cd6a0a714","text":"\u003Cp\u003E今までのデータリネージの追跡に関する説明を踏まえて、これを具体的な作業例と結びつけると、さらにそのメリットが明確に見えてきます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-ad609f49ac","type":"heading3","lines":["より迅速な根本原因分析"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-6810fa23cb","text":"\u003Cp\u003Eレポートが破損したり、メトリクス（指標）が変動したりした場合、バックワードリネージを使用することで、チームはパイプラインを手動で再構築することなく、発生した事象からその起源へとたどることができます。個人の知識で確認を進めていくのではなく、実際の依存関係から始まるため、問題検出と解決にかかる平均時間を短縮できます。たとえば、あるダッシュボードで売上予測が突然低下し、別のダッシュボードでは低下していない場合、バックワードリネージを使用すると、各依存関係を手動で確認する代わりに、変更された変換、失敗したタスク、または古い上流テーブルに不一致の原因をたどることができます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-e62418b966","type":"heading3","lines":["より安全な変更管理"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-db1520be28","text":"\u003Cp\u003Eフォワードリネージを使用すると、列の名前変更、テーブルの廃止、タスクの変更などを行う前に起こりうる下流への影響を評価できるため、上流の小さな変更が数日後にダッシュボード、抽出、モデルの特徴量を破損させる可能性を減らすことができます。たとえばフォワードリネージを使用すれば、上流にある顧客テーブルの列を廃止する前に、そのフィールドが先に更新する必要がある下流のダッシュボード、抽出、またはML特徴量にデータを提供しているかどうかを確認できます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-26e9e7283a","type":"heading3","lines":["コンプライアンスサポートの強化"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-499a0e9086","text":"\u003Cp\u003Eリネージは、データがどのように取得、変換、使用されたかを示す監査可能な記録を提供します。これにより、プロビナンス、管理、保持、および適切なデータ取り扱い証拠を重視したフレームワーク全体での文書化と対応が容易になります。たとえば、監査担当者から「規制対象のフィールドがソースの取り込みからレポートワークフローにどのように移動したか」を尋ねられた場合、リネージの追跡は、関連システム、変換、および下流での使用状況を文書化して証明できます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-09c0e4506c","type":"heading3","lines":["コストとアセットの合理化の向上"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-8b64c3e9b8","text":"\u003Cp\u003Eリネージが可視化されると、重要でないパイプライン、有意義でないテーブル、コストを増幅させている重複など、コスト削減や業務効率化につながるポイントをチームが発見できるようになります。たとえば、2つのパイプラインが別々のダッシュボード用にほぼ同一の派生テーブルを生成していることを発見したら、処理を統合して冗長なストレージやコンピュートを削減できるでしょう。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_4":{"id":"title-v2-35e66ff599","type":"heading3","lines":["データダウンタイムの削減"],":type":"snowflake-site/components/title-v2"},"text_5":{"id":"text-810079c852","text":"\u003Cp\u003Eリネージはすべてのインシデントを防ぐことはできませんが、適切に運用することで起きうるインシデントの規模や期間を短縮することができます。データ品質モニタリングと組み合わせることで、リネージは、問題がフローのどこで発生したか、および下流において何が影響を受けているかをチームが特定するのに役立ちます。たとえばビジネスに不可欠なレポートでデータ鮮度の問題が発生した場合、リネージは、上流のどの依存関係が遅延を引き起こしたか、およびどの下流データアセットを最初にトリアージすべきかを特定します。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_5":{"id":"title-v2-2ac880ed07","type":"heading3","lines":["AIおよびMLガバナンスの強化"],":type":"snowflake-site/components/title-v2"},"text_6":{"id":"text-8548d088c2","text":"\u003Cp\u003Eこの点は、データリネージの追跡の最も重要なメリットの1つになりつつあります。ML（機械学習）リネージは、ソースデータ、特徴量エンジニアリング、データセット、モデル、および予測を接続し、結果の再現、プロビナンスの文書化、およびモデルアーティファクトがどのように生成されたかの説明が容易になります。MLリネージでは、モデルが予期しない結果を生成した場合、その出力結果をトレーニングまたは推論中に使用されたデータセットのバージョン、特徴量パイプライン、およびソースデータにまでさかのぼって追跡できます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_6":{"id":"title-v2-52080381bc","type":"heading3","lines":["部門横断的な信頼の向上"],":type":"snowflake-site/components/title-v2"},"text_7":{"id":"text-215ec5980c","text":"\u003Cp\u003Eエンジニア、アナリスト、スチュワード、監査担当者が同じパスを検査し、同じ依存関係を確認できるようになると、部門横断型で信頼性が向上します。これにより、定義に関する議論がなくなるわけではありませんが、データの出所や途中の変更内容についての不確実性が軽減されます。アナリスト、エンジニア、スチュワード全員が共有指標の同じリネージパスを検査できることで、その数値がどこから来たのか、どのチームが次の修正を担当するのかなど、部門関係なくシームレスに認識を合わせて作業ができるようになります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3","h3_title_3","text_4","h3_title_4","text_5","h3_title_5","text_6","h3_title_6","text_7"]},"section_6":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"common-challenges-in-data-lineage-tracking",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-75da46175e","type":"heading2","lines":["データリネージの追跡での一般的な課題"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-d1a18128a6","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eリネージに関する問題のほとんどは、チームが記録を完全かつ最新で使いやすい状態に維持しようしつつも、環境が煩雑である場合に発生します。\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E量と速度：\u003C/b\u003E大量のデータを扱う環境では、手動プロセスでは追いつけないほど多くのオブジェクト、更新、実行イベントが生成されます。\u003Ca href=\"https://docs.snowflake.com/ja/user-guide/snowpipe-streaming/data-load-snowpipe-streaming-overview\" target=\"_blank\"\u003Eストリーミング\u003C/a\u003Eシステムでは、継続的なフローのためにタイミングが重要になるため、リネージがさらに困難になります。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E断片化されたツールエコシステム：\u003C/b\u003E取り込み、変換、オーケストレーション、BI、MLのつながりが断片的である場合、より多くのコンテキストが必要な時点でも部分的なビューしか得られなくなります。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eレガシーシステム：\u003C/b\u003E古い環境では、多くの場合リネージがクリーンに出力されません。そのため、欠落した情報を補うためにログ、経験則、あるいは手動でのタグ付けに頼らざるを得なくなり、結果としてデータの信頼性が低下し、メンテナンスの負担が増大します。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003Eスキーマとパイプラインの絶え間ない変更：\u003C/b\u003E正確なリネージであっても、環境から遅れをとると価値が失われます。新しい列、名前が変更されたフィールド、変更された結合、および作り直されたタスクにより、思っているよりも早くリネージグラフが時代遅れになる可能性があります。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E変換と単純な移動の区別：\u003C/b\u003Eすべての下流の依存関係が同じ意味を持つわけではありません。コピーされたフィールド、フィルタリングされたフィールド、および派生した指標は、ガバナンスやデバッグに関する異なる質問に答えるものであるため、同等に扱うべきではありません。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E完全性とオーバーヘッドのバランス：\u003C/b\u003Eチームは包括的なリネージを求めていますが、同時に過度な運用上の負担を生じさせない追跡方法も必要としています。これが、プラットフォームネイティブ（組み込み型）のリネージモデルが魅力的である理由の1つです。\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E技術的なリネージとビジネスコンテキストの橋渡し：\u003C/b\u003Eオブジェクト名で埋め尽くされたリネージグラフは使いにくい場合があります。記録は、所有者、用語集のコンテキスト、機密性タグ、ポリシーの関係、および鮮度の期待値なども明確化されることで、よりグラフの価値が高くなります。\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"]},"section_7":{"columnClassNames":{"h3_title_7":"aem-GridColumn aem-GridColumn--default--12","h3_title_3":"aem-GridColumn aem-GridColumn--default--12","h3_title_4":"aem-GridColumn aem-GridColumn--default--12","text_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_5":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","youtube_636907173":"aem-GridColumn aem-GridColumn--default--12","h3_title_6":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","text_5":"aem-GridColumn aem-GridColumn--default--12","text_4":"aem-GridColumn aem-GridColumn--default--12","text_7":"aem-GridColumn aem-GridColumn--default--12","text_6":"aem-GridColumn aem-GridColumn--default--12","text_8":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"data-lineage-tracking-best-practices",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-14119b6fd3","type":"heading2","lines":["データリネージの追跡のベストプラクティス"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-40006f634a","text":"\u003Cp\u003Eリネージグラフの有用性は、チームの意思決定にどれだけ役立つかによって決まります。以下の例は、リネージを最新かつ解釈可能な状態にし、データの依存関係を可視化することで最も運用しやすいワークフローを作るためのヒントです。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-8c70c758c8","type":"heading3","lines":["影響度の高いアセットからの開始"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-84f7e9a094","text":"\u003Cp\u003Eリネージの追跡は、運用、顧客体験、財務報告、または規制対象のワークフローに重大な影響を与えるテーブル、ビュー、レポート、およびMLアセットなど、全体に与える影響が大きいものから開始することで、最も即効性のある価値を生み出します。これにより、チームは、不明確な依存関係が最大のリスクを生み出す領域に集中できるようになります。\u003C/p\u003E\r\n\u003Cp\u003Eまた、開始時のスコープを絞ることで、導入がより現実的になります。環境全体を一度にマッピングしようとするのではなく、影響分析、監査可能性、またはトラブルシューティングが最も重要となる領域でまず有用なリネージを確立し、運用モデルが成熟するにつれて対象範囲を拡大できます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-b0f2ae6870","type":"heading3","lines":["導入初日からのキャプチャの自動化"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-e3891842e5","text":"\u003Cp\u003E手作業による図解は、プロジェクトの初期段階や現状把握の段階では役立ちますが、スキーマ、ジョブ、依存関係が頻繁に変更される環境では信頼性を維持できません。リネージを手動で更新しなければならないとなると、記述対象のシステムから遅れをとることがよくあります。\u003C/p\u003E\r\n\u003Cp\u003Eキャプチャを自動化すれば、リネージは実際の実行に近い状態に保たれます。クエリが実行され、パイプラインが実行され、アセットが変更されると、リネージ記録は環境に合わせて更新されるため、個別でドキュメントを作る負担はなくなります。\u003C/p\u003E\r\n\u003Cp\u003ESnowsightの組み込みデータリネージ機能については、こちらの動画をご覧ください。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube_636907173":{"id":"embed-76f5cd3805","youtubeVideoId":"uu7dS0tiJbw","accessibilityLabel":"View Snowsight's built-in data lineage features","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"h3_title_2":{"id":"title-v2-564768c00a","type":"heading3","lines":["重要な箇所での列レベルの追跡"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-59cdcec44a","text":"\u003Cp\u003E列レベルのリネージはすべてのワークフローに必要というわけではありませんが、個々のフィールドがどのように派生し、再利用され、下流で公開されたかをチームが理解したい場合は必要です。これは、規制対象のデータ、主要なビジネス指標、および重要なレポートロジックを形成する変換において特に当てはまります。\u003C/p\u003E\r\n\u003Cp\u003Eテーブルレベルのビューでは2つのアセットが接続されていることがわかるだけですが、列レベルのビューでは、途中でどの特定のフィールドがコピー、フィルタリング、名前変更、または計算されたかを確認できます。この違いは、チームが指標ロジックをレビューしたり、機密データを追跡したり、報告された値の不一致を調査したりする際に重要になります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-50bd3105c0","type":"heading3","lines":["リネージとガバナンスアーティファクトの接続"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-f221540c8b","text":"\u003Cp\u003Eリネージパスは、技術的な関係性とともにビジネスコンテキストを伴うことで、はるかに有用になります。所有者、用語集の定義、タグ、ポリシー、認定ステータス、および想定される更新パターンはすべて、チームが確認している内容を理解し、下流のアセットをどの程度信頼すべきかを判断するのに役立ちます。\u003C/p\u003E\r\n\u003Cp\u003Eこのコンテキストがないと、リネージグラフは技術的には正確であっても、エンジニアリング部門以外では使用が困難になる可能性があります。リネージがガバナンスアーティファクトと結びついているほど、スチュワードシップ、アクセスレビュー、および責任ある再利用をサポートしやすくなります。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_4":{"id":"title-v2-b969d54140","type":"heading3","lines":["ビジネスステークホルダーによるリネージのレビュー"],":type":"snowflake-site/components/title-v2"},"text_5":{"id":"text-9ec2a9ad10","text":"\u003Cp\u003E自動化されたキャプチャはデータがどのように移動したかを示すことができますが、その結果得られた記録が、ビジネス側のデータ理解をそのまま反映しているというわけでもありません。ステークホルダーとのレビューでは、純粋に技術的な視点では見落とす可能性のある、欠落したコンテキスト、時代遅れの前提条件、およびセマンティックドリフトを特定するのに役立ちます。\u003C/p\u003E\r\n\u003Cp\u003Eこれは共有レポート環境において最も重要です。共有レポート環境では、依存関係マップがオブジェクトレベルでは正確であっても、メトリクスの定義が変更された理由や、下流チームがアセットを異なる方法で解釈する理由を説明できない場合があります。レビューを行うことで、混乱が広がる前にそのギャップを埋めることができます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_5":{"id":"title-v2-9b5d68952d","type":"heading3","lines":["リネージとデータ品質モニタリングの組み合わせ"],":type":"snowflake-site/components/title-v2"},"text_6":{"id":"text-340db482c7","text":"\u003Cp\u003Eリネージは、データ品質シグナルと併用することでより強力になります。依存関係パスはそれ自体でも有用ですが、鮮度が低下した場所、スキーマドリフトが発生した場所、または検証ルールが失敗した場所もチームが確認できるようになれば、より実用的なものになります。\u003C/p\u003E\r\n\u003Cp\u003E品質モニタリングとリネージを組み合わせることで、チームはインシデント対応時の検索範囲を絞り込むことができます。データがどこに移動したかを確認するだけでなく、信頼性が低下した場所や、現在影響を受けている可能性のある下流のアセットも確認できます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_6":{"id":"title-v2-73e0f5c191","type":"heading3","lines":["エンジニアリング部門以外でのリネージの活用"],":type":"snowflake-site/components/title-v2"},"text_7":{"id":"text-bbfe405198","text":"\u003Cp\u003Eリネージは、データに依存する人々がグラフをリバースエンジニアリングすることなく解釈できる場合に最も効果的です。ビジネスに適したラベル、役割に応じたビュー、および明確なコンテキストメタデータはすべて、アナリスト、スチュワード、およびコンプライアンスチームにおけるリネージの使用を簡単にします。\u003C/p\u003E\r\n\u003Cp\u003Eこれは、技術的な詳細を取り去るという意味ではありません。パイプラインのデバッグ、再利用のためのデータセットの評価、または今後の変更による影響のレビューなど、目的に応じてさまざまな関係者が活用できる方法でリネージを提示することを意味します。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_7":{"id":"title-v2-951fa70e51","type":"heading3","lines":["環境の変化に応じたカバレッジのレビュー"],":type":"snowflake-site/components/title-v2"},"text_8":{"id":"text-eb060c312a","text":"\u003Cp\u003E強力なリネージ実装であっても、現在の環境がしっかり反映されているかどうかを誰も確認しなければ、不完全なものになる可能性があります。新しいパイプライン、スキーマの変更、進化するオーケストレーションパターン、および拡大するAIワークフローはすべて、時間の経過とともに死角を生み出す可能性があります。\u003C/p\u003E\r\n\u003Cp\u003E定期的なレビューは、リネージの同期が取れていない箇所、粒度が十分ではなくなった箇所、およびビジネスに不可欠な新しいアセットをとりいれる箇所などを特定するのに役立ちます。目標は、その時点における完全性ではなく、環境の進化に合わせて有用であり続けるリネージ記録を作成することです。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","youtube_636907173","h3_title_2","text_3","h3_title_3","text_4","h3_title_4","text_5","h3_title_5","text_6","h3_title_6","text_7","h3_title_7","text_8"]},"section_8":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"data-lineage-tracking-for-ai-and-ml-governance",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-687ccd9427","type":"heading2","lines":["AIおよびMLガバナンスのためのデータリネージの追跡"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-f12ca6c867","text":"\u003Cp\u003EAIにより、リネージの追跡の必要性はより広範かつ厳格になります。チームは、モデルトレーニングにどのデータスナップショットが使われたか、どの変換が特徴量を生成したか、どのバージョンのデータセットがレビューで使用されたか、そしてどの下流の予測がそれらのアーティファクトに依存しているかを把握する必要があります。\u003C/p\u003E\r\n\u003Cp\u003Eモデルのプロビナンスと特徴量のリネージは、実用的なコントロールポイントです。プロビナンス記録は、モデルのバージョンを、その作成に使用されたトレーニングデータおよびサポートデータセットにリンクさせます。特徴量のリネージは、生の運用データがどのようにしてモデルを形成する特徴量ビューまたはデータセットになったかを追跡します。ここではデータのバージョン管理も重要です。どのスナップショットが特定の結果をもたらしたかを特定できなければ、再現性は低下し、インシデントのレビューは推測に頼ることになります。\u003C/p\u003E\r\n\u003Cp\u003Eこれには規制上の理由もあります。\u003Ca href=\"https://artificialintelligenceact.eu/\" target=\"_blank\"\u003EEU AI法\u003C/a\u003Eの第10条では、高リスクシステムでのトレーニング、レビュー、テストデータに対するガバナンスが求められており、これには妥当性、代表性、エラー、完全性、文書化への配慮が含まれます。また、同法のより広範なコンプライアンスフレームワークでは、適合性を証明するのに十分な技術文書も義務付けられています。これは、すべてのリネージグラフがそれ単体で規制を満たすという意味ではありません。しかし、高リスクのAI環境において、文書化されたデータの出所、変換履歴、アセットの関係性がますます重要になっていることを意味します。\u003C/p\u003E\r\n\u003Cp\u003EAIガバナンスにおいて、リネージの追跡は5つの具体的な成果をサポートします。\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EAIガバナンスのニーズ\u003C/th\u003E\r\n\u003Cth\u003Eリネージが確立を支援するもの\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003Eモデルのプロビナンス\u003C/td\u003E\r\n\u003Ctd\u003Eどのデータ、特徴量、データセットが特定のモデルバージョンを生成したか\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E再現性\u003C/td\u003E\r\n\u003Ctd\u003Eどのスナップショットと変換パスが結果をもたらしたか\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E説明可能性のサポート\u003C/td\u003E\r\n\u003Ctd\u003Eどの上流のデータと特徴量が下流のアーティファクトに影響を与えたか\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003Eコンプライアンスの証拠\u003C/td\u003E\r\n\u003Ctd\u003Eトレーニングデータと検証データがどのように調達され、ガバナンスが適用されたか\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003Eより安全な更新\u003C/td\u003E\r\n\u003Ctd\u003E変更によってどの特徴量、モデル、または下流のアセットが影響を受ける可能性があるか\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"]},"section_9":{"columnClassNames":{"text_0":"aem-GridColumn 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