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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});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":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"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":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":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn 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aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-80976c425e","type":"heading2","lines":["概要"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-817a91e3ca","text":"\u003Cp\u003Eデータは身の回りのあらゆる場所にあり、膨大な数のシステムにさまざまな形式で保存されています。ソーシャルメディアの投稿履歴から、企業の顧客データベース、遺伝子配列データベースまで、その種類は非常に多岐にわたります。データドリブンな意思決定の鍵となるのは、このような情報を1か所に集約してアクセスできるようにすることです。しかし、まずはそのデータを特定して取り込む必要があります。\u003C/p\u003E\n\u003Cp\u003Eデータ取り込みは、リアルタイム分析、ビッグデータプロジェクト、AIイニシアチブをサポートするモダンデータパイプラインの構築に不可欠なステップです。しかし、組織によるデータの収集とインポートの方法は、ビジネス目標とデータ戦略によって異なります。この記事では、さまざまなデータ取り込みの方法と、そのデータを使用して企業のデータイニシアチブを実現する方法について詳しく説明します。\u003Cbr\u003E\n\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn 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aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"is",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-81a6002ace","additionalClasses":"headline-decoration","type":"heading2","lines":["データ取り込みが重要な理由"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-cfc7068e02","text":"\u003Cp\u003E世界中で作成、キャプチャ、コピー、消費されるあらゆる種類のデータの量は\u003Ca href=\"https://www.statista.com/statistics/871513/worldwide-data-created/\"\u003E年率19.2%\u003C/a\u003Eで増加しており、その勢いが衰える気配はありません。現代の企業は、利用できる最もタイムリーな情報にアクセスする必要があり、さらにそのデータが正確で関連性が高く、包括的なものであることを確認しなければなりません。\u003C/p\u003E\n\u003Cp\u003Eデータ取り込みプロセスを効率的に管理している組織には、明確な競争優位性があります。このような組織は、顧客行動の変化に迅速に対応し、製品の市場投入を加速させることができます。また、新たな収益源を開拓し、規制上の制約を遵守して、状況の急変による影響を低減できます。\u003C/p\u003E\n\u003Cp\u003Eデータを適切に取り込めない企業では、盲点が生じて機会を失い、リスクが高まります。また、取り込みの方法が適切でない場合は、記録の欠落、データの重複、不整合、コンプライアンス違反、セキュリティ違反、変換エラーがエコシステム全体に伝播し、ユーザーからの信頼と意思決定の正確性が低下する可能性があります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy_":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1482435266":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_440491184":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_2006380649":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1966069952":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"use",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-3cf22db42a","additionalClasses":"headline-decoration","type":"heading2","lines":[" データ取り込みの種類とユースケース"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-01f69f253d","text":"\u003Cp\u003Eデータ取り込みプロセスには、情報の使用方法や時間的制約に応じてさまざまな形式があります。データ取り込みの主な種類は次の3つです。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-d21da67251","additionalClasses":"headline-decoration","type":"heading4","lines":["バッチ方式のデータ取り込み"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-9b34ed2c13","text":"\u003Cp\u003E最も一般的な取り込み形式では、プラットフォームがスケジュールされた間隔（毎時間、毎日、毎週）でデータを収集し、一度にまとめて処理します。バッチ取り込みはシンプルで信頼性が高く、オフピーク時にスケジュールできるため、システムパフォーマンスへの影響を最小限に抑えられます。しかし、タイムリーな情報と迅速な意思決定を必要とするアプリケーションには適していません。典型的なユースケースには、タイムシートのデータを毎週または隔週で処理する必要がある給与システムや、トランザクションデータを夜間に集約する必要がある金融機関などがあります。\u003Cbr\u003E\n\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1482435266":{"id":"title-v2-d45ad36933","additionalClasses":"headline-decoration","type":"heading4","lines":["リアルタイムのデータ取り込み"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_440491184":{"id":"text-b838b88466","text":"\u003Cp\u003Eこのシナリオでは、データはソースから宛先への連続した流れとして処理されます。この方法は、クレジットカード取引中の不正検知や、\u003Ca href=\"https://www.snowflake.com/en/blog/manufacturing-data-ingestion-into-snowflake/\"\u003E製造\u003C/a\u003E環境内の機器故障センサーのモニタリングなど、リアルタイムで意思決定を行う必要がある場合に不可欠です。しかし、インフラストラクチャへのより多大な投資が必要となり、運用コストも増加します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1966069952":{"id":"title-v2-711070e365","additionalClasses":"headline-decoration","type":"heading4","lines":["マイクロバッチ方式のデータ取り込み"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_2006380649":{"id":"text-a08a39e7df","text":"\u003Cp\u003E3つ目のデータ取り込みの形式は、上記の2つのハイブリッドです。データの収集は継続的に行われますが、処理は数分または数秒間隔など、定期的に少量ずつ実行されます。このアプローチはリアルタイム処理よりもシステムインフラストラクチャへの要求を抑えられるうえ、データ収集からアクション実行までの遅延を短縮することができます。マイクロバッチ取り込み機能を使用する例としては、Eコマースサイトで買い物かごの内容に基づいて訪問者に商品を勧める、地域の医療センターが公衆衛生の危機発生時に病床の空き状況を判断するといったことがあります。\u003C/p\u003E\n\u003Cp\u003E単一の組織が、日報作成にはバッチ処理、不正検知にはリアルタイム方式、ウェブやモバイル顧客とのコミュニケーションにはハイブリッドアプローチなど、アプリケーションごとに異なる形式のデータ取り込みを採用している場合もあります。どの取り込み方法を選択するかは、データ量、レイテンシーの要件、インフラストラクチャのコスト、技術的な複雑さ、ビジネスにとってのデータの重要度などの要因によって異なります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy_1241045613","title_v2_copy_copy_c_1482435266","text_copy_1241045613_440491184","title_v2_copy_copy_c_1966069952","text_copy_1241045613_2006380649"]},"container_copy_copy_259718203":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"and",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a08381c9fb","additionalClasses":"headline-decoration","type":"heading2","lines":[" データ取り込みとETLの比較"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-90631e9929","text":"\u003Cp\u003Eデータ取り込みでは、データウェアハウスやデータレイクの構築プロセスにおける不可欠なステップを実行するために、ステージング領域に情報を送ります。ステージング領域では、その情報を抽出、変換、ロード（ETL）し、ウェアハウスまたはレイクに取り込むことができます。したがって、データ取り込みは、ETLまたは\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/understanding-extract-load-transform-elt/\"\u003E抽出、ロード、変換（ELT）\u003C/a\u003Eプロセスを補完するものです。\u003C/p\u003E\n\u003Cp\u003Eこのプロセスを、レストランの仕組みにたとえて説明します。データ取り込みプラットフォームは、生の食材（データ）を厨房に届けるトラックのようなものです。ETL/ELTオペレーションでは、これらの食材を洗って切り、味付けし、データウェアハウスやデータレイクのシェフに渡します。顧客はクエリ（注文）をシェフに送り、シェフは、顧客が求めている回答（食事）に合うように材料を混ぜ合わせ、対応します。\u003C/p\u003E\n\u003Cp\u003E場合によっては、ETLやELTを使用せずに、データウェアハウスやレイクでデータを直接受け入れることができます。この設定では、データ取り込みプラットフォームは、ファストフードレストランのドライブスルー窓口のような役割を果たします。たとえば、販売管理（POS）システムや取引システム（フォーマットに変化がなく、データの変換が不要なシステム）や、機器センサー（データに一貫性があり、迅速な対応が必要なシステム）などです。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"]},"container_copy_copy__987904085":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"how",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-942fdc4bcb","additionalClasses":"headline-decoration","type":"heading2","lines":["データ取り込みパイプラインについて"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-ff91944ad9","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/modernizing-data-pipelines/\"\u003Eデータパイプライン\u003C/a\u003Eは、適切な情報源の特定で始まり、クリーンで一貫性のある分析可能な大量のデータで終わる、一連の連続したプロセスで構成されています。主なステージは以下のとおりです。 \u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003Eディスカバリー：\u003C/b\u003Eパイプラインは、データベース、ストリーミングプラットフォーム、IoTデバイス、APIなど、信頼できるデータソースに対する接続の確立から始まります。 \u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E抽出：\u003C/b\u003Eパイプラインは、ソースごとに適切なプロトコルを使用してデータを引き出すか、リアルタイムフィードへの永続的な接続を確立します。パイプラインは、幅広いデータフォーマット、フレームワーク、プロトコルをサポートできなければなりません。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E検証：\u003C/b\u003Eパイプラインは、アルゴリズムによって生データを検査および検証し、正確性と一貫性について期待される基準を満たしていることを確認します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E変換：\u003C/b\u003Eパイプラインは、検証済みのデータをターゲットシステム内で使用するための一貫した形式に変換し、エラーを修正して重複を削除し、欠落しているデータがあれば、フラグを立ててさらにレビューします。このステージでは、データリネージと品質を説明するメタデータを追加することもできます。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003Eロード：\u003C/b\u003E最後のステップでは、変換されたデータをターゲットシステム（通常はデータウェアハウスまたはデータレイク）に移動し、分析とレポート作成の準備を整えます。\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613"]},"container_copy_copy__1985496925":{"columnClassNames":{"text_copy_1241045613_1455632553":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1002905779":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1779735486":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1337643433":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1644233054":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_705732595":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1240732416":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,"layout":"RESPONSIVE_GRID","id":"cus",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-b32bba7e77","additionalClasses":"headline-decoration","type":"heading2","lines":["データ取り込みプロセスを最適化するメリット"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-afd32fdb78","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-ingestion-framework/\"\u003Eデータ取り込みプロセス\u003C/a\u003Eの最適化と合理化は、将来的な利益につながります。データの迅速かつ正確な取り込みは、企業にさまざまな競争優位性をもたらします。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-ece6ae7200","additionalClasses":"headline-decoration","type":"heading4","lines":["意思決定の正確性の向上"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1779735486":{"id":"text-d4214dabc3","text":"\u003Cp\u003E適切に設計されたパイプラインは、一貫性の欠如を解消し、エラーを低減することでデータ品質を改善し、結果として、そのデータに基づく意思決定を向上させます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1337643433":{"id":"title-v2-f905b7171c","additionalClasses":"headline-decoration","type":"heading4","lines":["新しいインサイトへの迅速なアクセス"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1455632553":{"id":"text-b74ffbc0a7","text":"\u003Cp\u003E取り込みプロセスの合理化により、データを収集してから実際に活用するまでの遅延を大幅に短縮することができます。処理時間が数時間から数分以下に短縮されることで、企業は変化する市況により迅速に対応できるようになります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_705732595":{"id":"title-v2-8f83262ca0","additionalClasses":"headline-decoration","type":"heading4","lines":["運用上のボトルネックの軽減"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1240732416":{"id":"text-35fac7597a","text":"\u003Cp\u003E適切に設計されたパイプラインは、機能停止が少なく復旧も迅速なため、トラブルシューティングの必要性が減り、コストの削減にもつながります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1002905779":{"id":"title-v2-8874488578","additionalClasses":"headline-decoration","type":"heading4","lines":["スケーラビリティと柔軟性の向上"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1644233054":{"id":"text-b1d9680eb5","text":"\u003Cp\u003E合理化されたデータ取り込みプロセスは、データ量の増加に合わせてスケーリングでき、大規模なアップグレードや再構築が必要ありません。\u003C/p\u003E\n\u003Cp\u003E自動化により、データ取り込み時のオーケストレーション、データ検証後の下流プロセスのトリガー、需要の増加に応じたリソースの動的なスケーリングが可能になり、データ取り込みワークフローが強化されます。一貫したデータフォーマットの採用や取り込みパイプラインの個別のコンポーネントへの分割といった\u003Ca href=\"https://www.snowflake.com/ja/blog/best-practices-for-data-ingestion/\"\u003Eベストプラクティス\u003C/a\u003Eに従うと、人間による介入を最小限に抑えた自己管理システムを構築できます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text_copy_1241045613_1779735486","title_v2_copy_copy_c_1337643433","text_copy_1241045613_1455632553","title_v2_copy_copy_c_705732595","text_copy_1241045613_1240732416","title_v2_copy_copy_c_1002905779","text_copy_1241045613_1644233054"]},"container_copy_copy__2023633504":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_978857420":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_2144930113":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1840708743":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1393802456":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_657429347":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1764704265":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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