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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"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_949147658"],":type":"snowflake-site/components/container"},"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",":type":"snowflake-site/components/experiencefragment","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-957108e6a4","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"gridClassNames":"aem-Grid 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aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-0bebc118fc","type":"heading2","lines":["概要"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-441726aebf","text":"\u003Cp\u003E企業や組織は日々膨大なデータを生成していますが、未加工の状態では実際の価値を持っているというよりは、将来の可能性を秘めているに過ぎません。レシピに従って料理を作ったり、組み立てラインで製品を製造したりするように、最終成果物は各パーツの集合体であり、すべてが組み合わさることで有用な（あるいは美味しい）商品が生まれます。同様に、企業が未整理の生データを抱えている場合、誰かにとって有用なものにする前に、そのすべてを整理して理解可能な状態にする必要があります。この一連の工程は、データ処理と呼ばれます。\u003C/p\u003E\n\u003Cp\u003Eデータ処理とは、未加工で混沌としていることが多いデータを有用な形式に構造化することです。企業は一連の業務を通じて、数字の列、アンケートの回答のページ、豊富な情報を含むスプレッドシートに隠された価値を明らかにできます。これはビジネス戦略の核心であり、ビジネスアナリティクスから機械学習（ML）に至るまで、あらゆる活用を可能にする基盤となります。\u003C/p\u003E\n\u003Cp\u003E本ガイドでは、データ処理とは何か、その重要性、データ処理のステージ、データ処理の種類、データ処理の方法、データ処理ツールとテクノロジーについて解説します。最後に、このビジネスクリティカルな運用に関してよく寄せられる質問をいくつか紹介します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"a","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-d2ada9fe51","additionalClasses":"headline-decoration","type":"heading2","lines":["データ処理とは"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-4c9160601a","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eデータ処理は、未加工の未整理データを取得して有用な情報に変換する体系的な一連の処理です。組織は、こうした処理から有意義なインサイトを引き出し、情報に基づいた意思決定を行うことができます。ビジネス戦略の基本要素であり、データ分析を実現するためには不可欠です。\u003C/p\u003E\n\u003Cp\u003Eこれまで、データ処理は非常に手間のかかる手作業のプロセスでした。\u003Ca href=\"https://www.nasa.gov/centers-and-facilities/jpl/when-computers-were-human/\"\u003E計算処理の職務\u003C/a\u003Eを与えられていた人々は、データを収集、保存、分析するために、台帳、フォーム、電卓などの物理的なツールと紙ベースのシステムに依存していました。\u003Ca href=\"https://www.rudderstack.com/learn/data-collection/data-collection-history/\"\u003E歴史的な事例\u003C/a\u003Eとして、米国では、1880年の国勢調査の結果を公表するまでに7年もの期間を必要としたことが広く知られています。手作業での集計処理が遅かったためです。この事態を背景に、当時米国国勢調査局の職員であったHerman Hollerith氏は集計機を発明しました。\u003Ci\u003E\u003C/i\u003Eこれにより、国勢調査データの処理に要する時間が数年から数か月へと劇的に短縮され、モダンデータ処理の業界基礎が築かれました。\u003C/p\u003E\n\u003Cp\u003E今日のデータ処理は、コンピューターと自動化によって管理される電子プロセスであり、通常はデータアナリスト、データプロセッサ、\u003Ca href=\"https://www.snowflake.com/ja/product/data-engineering/\"\u003Eデータエンジニア\u003C/a\u003E、データサイエンティストによって処理されます。\u003Ca href=\"https://www.snowflake.com/ja/product/ai/\"\u003EAIとML\u003C/a\u003Eは、特に大規模なデータセットの処理において重要な役割を果たします。データ処理はサイクルで実行されるとよく説明されますが、未加工の状態から分析、解釈、保存に至るまで、さまざまなステップを実行する必要があります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy__987904085":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__373061683":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1985496925":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__127842237":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_259718203":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__714116681":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy__1190438074":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"is","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-ecbb5b782b","additionalClasses":"headline-decoration","type":"heading2","lines":["データ処理が重要な理由"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8d9b994c3f","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003Eデータ処理がなければ、組織が毎秒のように生成している膨大なデータはデジタルノイズでしかありません。未処理の情報がそのままの状態で役立つことはほとんどありませんが、データ処理は、ビジネス上の意思決定に役立つ重要なインサイトや組織の競争優位性につながる情報との間の溝を解消します。\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E意思決定の向上：\u003C/b\u003E企業が競争と成長を望むならば、前提や推測に頼ることはできません。データ処理を通じて取得された明確なインサイトは、以下のような多くの点で意思決定を改善できます。\u003Cbr\u003E\r\n\u003Cbr\u003E\r\n\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E市場トレンドの特定：\u003C/b\u003E販売データにより、どの製品がよく売れているか、どのデモグラフィックが購入しているのか、1年のうちどの時期によく売れているのかなどについての情報を得られます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E運用効率の改善\u003C/b\u003E：サプライチェーン、ロジスティクス、生産データを分析することで、企業は無駄やボトルネックを特定し、プロセスを最適化できます。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003Eデータに裏付けられた予測：\u003C/b\u003E予測分析は、履歴データを使用して結果を予測することで、企業が顧客ニーズを予測し、在庫を管理し、リスクを軽減するのに役立ちます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003E精度と信頼性の向上：\u003C/b\u003E未処理のデータには、エラー、重複、不整合が含まれていることがよくあります。また、\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/governance-risk-compliance/\"\u003Eガバナンス、リスク、コンプライアンス（GRC）\u003C/a\u003E、不正検知、金融などの多くの業界では、1つのエラーや不整合が、さらなる大きな問題へと連鎖的に発展します。データ処理のデータクレンジングステップ（これについては後ほど説明します）では、こうした問題を特定して修正することで、分析時のデータの正確性と信頼性が向上します。\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E競争優位性：\u003C/b\u003Eデータを効果的に処理して活用することは、企業にとって競合他社の先手を打つための重要な差別化要素です。以下のようなメリットが挙げられます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E顧客体験のパーソナライゼーション：\u003C/b\u003E企業は顧客データを処理することで、顧客が関心のあるパーソナライズされたレコメンデーション、ターゲットマーケティング、サービスを提供できるようになり、ブランドロイヤルティと顧客維持率が向上します。\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E市場の変化への対応：\u003C/b\u003E企業はリアルタイムのデータ処理により、競合他社による新製品の投入や顧客需要の変化など、市場の変化に迅速に対応できます。\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Cb\u003Eデータセキュリティとコンプライアンスの強化：\u003C/b\u003Eデータ処理は、データを有用にするだけでなく、安全性も高めます。データのクレンジングと編成では、\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Eデータマスキング\u003C/a\u003E、\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-anonymization/\"\u003E匿名化\u003C/a\u003E、暗号化、トークン化などの\u003Ca href=\"https://www.acceldata.io/blog/data-compliance\"\u003E特定の保護対策\u003C/a\u003Eが組み込まれています。また、データ処理システムは、データへのアクセス、変更、削除を行えるユーザーに関する規則も適用します。また、GDPRやHIPAAなどの多くのデータ規制では、収集を許可するデータや使用方法などについて厳しい要件が定められています。データ処理システムは、収集から削除までのデータライフサイクルのあらゆるステップを慎重に記録します。これにより、組織が規制に準拠していることを証明する監査証跡が作成されます。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","text"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"use","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-cbca1f6c02","additionalClasses":"headline-decoration","type":"heading2","lines":["データ処理の各ステージ"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-a747427c99","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003E本ガイドではすでに、データ処理を工場の組み立てラインを例に挙げました。シャーシの打ち出しから最終的な塗装、仕上げに至る自動車の製造工程と同様に、データ処理も構造化されたマルチステップのワークフローに従って進められます。煩雑な生データをクリーンで信頼できるデータに変換するには、各ステップが不可欠です。こうしたデータは、組織が情報に基づく意思決定を行い、確実な戦略を構築する際に活用されます。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1.収集\u003C/h4\u003E\n\u003Cp\u003Eデータの収集を開始する場合には、そのソースはトランザクションログ、社内データベース、ソーシャルメディアのエンゲージメント統計、顧客アンケートなど多岐にわたります。多くの場合、\u003Ca href=\"https://www.snowflake.com/ja/product/analytics/\"\u003Eデータレイクやデータウェアハウス\u003C/a\u003Eに格納されます。この最初のステップで抽出されるデータは、関連性が高く、正確で、信頼できるソースから抽出されていることが重要です。さもなければ、最終結果を歪めてしまい、プロジェクトの根幹を最初から台無しにしてしまうリスクがあります。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2.準備\u003C/h4\u003E\n\u003Cp\u003E多くの場合、これは前処理と呼ばれ、品質と一貫性を確保するためにデータをクリーニングして編成する、最も重要で時間のかかるステージです。以下のステップが含まれます。\u003Cbr\u003E\n\u003Cbr\u003E\n\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003Eデータクレンジング：\u003C/b\u003Eエラーの修正、欠損値の入力、重複データや無関係なデータを削除します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003Eデータ変換\u003C/a\u003E：\u003C/b\u003Eデータを一貫した形式に変換します（日付形式の標準化、テキストの数値コード化など）。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003Eデータ検証：\u003C/b\u003Eデータをルールに照らしてチェックし、正確性を確保します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.fullstory.com/blog/data-enrichment/\"\u003Eデータエンリッチメント\u003C/a\u003E：\u003C/b\u003E外部ソースからの関連情報を追加してデータセットを強化します。\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch4\u003E\u003Cbr\u003E\n3.入力\u003C/h4\u003E\n\u003Cp\u003Eこれは、準備されたデータが処理システムに投入される段階であり、生データが使用可能なデータに形成されていく最初の段階です。処理システムの例としては、特定のデータタイプや分析目標のために設計されたソフトウェアやアルゴリズム（大規模データセット用の\u003Ca href=\"https://www.ibm.com/think/topics/apache-spark\"\u003EApache Spark\u003C/a\u003Eなど）が挙げられます。この段階では、手動入力（小規模なデータセットの場合）、外部ソースからのデータのインポート、自動データキャプチャのすべてが、こうしたシステムにデータを入力する方法です。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E4.処理\u003C/h4\u003E\n\u003Cp\u003E名前のとおりですが、このステップはデータ処理サイクルの中核となります。データから必要な成果やインサイトに応じて、データを意味のある情報に変換するには、いくつかの異なる手法を使用します。たとえば、次のような処理があります。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003Eソート：\u003C/b\u003Eデータを特定の順序で配置します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003Eフィルタリング：\u003C/b\u003Eデータの特定のサブセットを選択します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E計算：\u003C/b\u003E合計や平均の計算などの数学演算を実行します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E集約：\u003C/b\u003E複数のレコードのデータを要約します。\u003Cbr\u003E\n \u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch4\u003E5.アウトプットと解釈\u003C/h4\u003E\n\u003Cp\u003E処理後、データは理解しやすく、活用しやすい形式で提示されます。最終的な成果物は、グラフ、ダッシュボード、その他の視覚的な表現です。解釈フェーズは、アウトプットを分析して結論を導き出し、トレンドを特定し、情報に基づいた意思決定を行うフェーズです。そして、ここで処理されたデータの価値が最終的に実現します。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E6.ストレージ\u003C/h4\u003E\n\u003Cp\u003E最後のステップでは、処理されたデータをデータベースやデータウェアハウスにセキュアに保存し、将来の使用や検索に備えます。このステップは、以下の理由から極めて重要です。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E監査とコンプライアンス：\u003C/b\u003E法律や規制の目的で記録が作成されます。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E今後の分析：\u003C/b\u003Eさらに複雑な分析のための基盤としてデータを使用できます。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003Eリファレンス：\u003C/b\u003E意思決定のための信頼できる履歴情報源を提供します。\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"],":type":"snowflake-site/components/container"},"container_copy_copy_259718203":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"and","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-c5ec6be37a","additionalClasses":"headline-decoration","type":"heading2","lines":["データ処理の種類"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-8b2b6b714a","text":"\u003Cp\u003E生データを意味のある有用な情報に変換するには、さまざまな方法が使用されます。その方法は非常に多く存在し、シナリオや要件によって最適な方法は異なりますが、バッチ処理、リアルタイム処理、オンライン処理の3つは最も一般的なものです。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1.バッチ処理\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ja/product/use-cases/streaming-data-pipelines/\"\u003Eバッチ処理\u003C/a\u003Eとは、大量のデータを一定期間にわたって収集し、一度にまとめて処理する方法です。このアプローチは、時間的制約のないタスクに最適であり、コンピューティングリソースを節約してピーク時間外にスケジュールできます。理想的なユースケースは、給与システム、月次請求、期末レポート、銀行取引明細書の生成などです。たとえば、クレジットカード会社が一日すべてのトランザクションを収集し、顧客アカウントを更新するために一晩で単一のバッチで処理する場合などです。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2.リアルタイム処理\u003C/h4\u003E\n\u003Cp\u003Eリアルタイム処理では、データは生成された時点で処理されるため、すぐに結果を得られます。この方法は、データの入力から出力へのターンアラウンドを瞬時に行う必要がある場合、遅延が深刻な結果をもたらす可能性があるシステムでは特に不可欠となります。\u003Ca href=\"https://www.snowflake.com/ja/solutions/industries/financial-services/fraud-detection-and-financial-crimes/\"\u003E金融取引の不正検知\u003C/a\u003E、GPSシステム、航空交通管制システムなどは、このタイプのデータ処理が使用されている例です。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3.オンライン処理\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/online-analytical-processing/\"\u003Eオンライン処理\u003C/a\u003Eは、インタラクティブなリアルタイム処理の一種です。ユーザーが開始したトランザクションが発生すると、すぐに応答します。これは、ウェブサイトやアプリとのやり取りでユーザーが日々経験していることです。簡単に言うと、ユーザーがリクエストを開始したりデータを入力したりすると、システムはすぐにリクエストを処理してフィードバックします。これらのシステムは常にオンラインであり、いつでもユーザーリクエストを処理できます。Eコマース、オンラインバンキング、航空会社の予約、オンラインゲームはすべてオンライン処理を利用しています。オンラインでコンサートや映画館のチケットを購入したことはあるでしょうか。このような方法で支払いが処理され、システムが即時に更新され、他の誰も同じ座席のチケットを購入できなくなります。\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"],":type":"snowflake-site/components/container"},"container_copy_copy__987904085":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"how","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-ae3d6f5de0","additionalClasses":"headline-decoration","type":"heading2","lines":["データ処理方法"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-2719fe8431","text":"\u003Cp\u003Eデータ処理にはさまざまな方式があり、すべての方式がすべての処理の種類に対応しているわけではありません。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1.手動のデータ処理\u003C/h4\u003E\n\u003Cp\u003Eこれは、機械を使用せずにすべて手作業でデータを収集、編成、分析する、最も旧式かつ伝統的なデータ処理方法です。処理速度が遅く、手間がかかり、エラーが発生しやすく、大量のデータを扱うには理想的ではありません。しかし、小規模な事業やビジネス、あるいは選挙中に投票用紙を手作業で再集計するなど、人間による判断が不可欠な場合は、この選択肢が適しています。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2.機械的なデータ処理\u003C/h4\u003E\n\u003Cp\u003E電卓、タイプライター、パンチカードマシンなどのシンプルな機械やデバイスを使用してデータを処理する場合は、機械的なデータ処理方法を使用します。本ガイドですでに取り上げたHollerith氏の集計機械も、この手法の一例です。機械的なデータ処理は単純なデータ処理ジョブに最適であり、手動のデータ処理よりもエラーの発生は減りますが、それでも巨大なデータセットには適していません。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3.電子データ処理\u003C/h4\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.lenovo.com/us/en/glossary/what-is-edp/?orgRef=https%253A%252F%252Fwww.google.com%252F&srsltid=AfmBOooBYFjcgNsJVG7eEUuzaIdrjkhzOJ_4Z3st5mhFK4tMT58CPgpa\"\u003E電子データ処理（EDP）\u003C/a\u003Eは、コンピューター、サーバー、自動化などの電子ソリューションを使用してデータを処理する、汎用的に使用されている最新の方法です。膨大な量のデータをリアルタイムで処理できる、効率的で正確、スケーラブルなアプローチです。EDPは、入力から出力までのデータ処理サイクル全体を自動化し、シンプルな給与システムからビッグデータアプリケーションまで、今日のほぼすべての業界で使用されています。\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"],":type":"snowflake-site/components/container"},"container_copy_copy__714116681":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"why","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-3222a04710","additionalClasses":"headline-decoration","type":"heading2","lines":["データ処理ツールとテクノロジー"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text_copy_1241045613":{"id":"text-14a99b53af","text":"\u003Cp\u003Eモダンデータ処理は、未加工の生データから価値あるインサイトを抽出するために、強力なツールと新興テクノロジーの組み合わせを利用しています。これらのソリューションは、基本的なデータストレージから複雑な自動分析まで、あらゆる処理を可能にしています。 \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E1.データベースとデータウェアハウス\u003C/h4\u003E\n\u003Cp\u003Eどちらもデータストレージとデータ管理のための基盤ツールですが、\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/modernizing-data-pipelines/\"\u003E処理パイプライン\u003C/a\u003Eでは\u003Ca href=\"https://www.coursera.org/articles/data-warehouse-vs-database\"\u003E目的がそれぞれ異なります\u003C/a\u003E。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003Eデータベース\u003C/b\u003Eは、ビジネスの特定の機能のために単一のデータソースから情報を保存および編成するためのものです。1つの目的のために整然と編成されたファイリングキャビネットと考えることができます。迅速で頻繁なタスクや小規模なクエリに対応するように設計されています。人気の高いデータベースには、MySQL、PostgreSQL、Microsoft SQL ServerなどのSQLベースのシステムがあります。\u003C/p\u003E\n\u003Cp\u003E一方、\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Eデータウェアハウス\u003C/a\u003Eは、複数のソースからの膨大な履歴データを保存するための、一元化された大規模なリポジトリです。これらは分析用に設計されており、本質的にはデータアナリストが複雑なビジネストレンドに関する質問に答えるために情報を見つけるためのライブラリです。大規模データセットに対して複雑なクエリを実行してレポートやビジネスインテリジェンスを生成するために構築されています。\u003Ca href=\"https://www.snowflake.com/ja/product/platform/\"\u003Eデータウェアハウスで\u003C/a\u003Eは、多くの場合、Snowflake、Hadoop、Apache Spark、\u003Ca href=\"https://www.snowflake.com/ja/fundamentals/data-lakes-and-data-lake-architecture/\"\u003Eデータレイク\u003C/a\u003Eなどのビッグデータテクノロジーが使用されます。 \u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E2.人工知能と機械学習\u003C/h4\u003E\n\u003Cp\u003EAIとMLは、データ処理のすべての段階を自動化して強化する強力なテクノロジーです。単純な計算だけでなく、パターンを明らかにして予測を立てます。AIは、データのクレンジングと準備を自動化し、エラーの自動検出と修正、欠損値の入力、データ形式の標準化を可能にします。MLモデルを履歴データでトレーニングすると、予測、異常の発見、データのセグメント化が可能になります。\u003Cbr\u003E\n \u003C/p\u003E\n\u003Ch4\u003E3.クラウドテクノロジーとデータアナリティクスのプラットフォーム\u003C/h4\u003E\n\u003Cp\u003EAmazon Web Services（AWS）、Google Cloud Platform（GCP）、Microsoft Azureなどのクラウドプロバイダーは、高価なオンプレミスハードウェアの購入やメンテナンスなしに、データ処理リソースを必要に応じてスケールアップまたはスケールダウンできるようにします。また、ビッグデータを大規模に処理することも可能になります。これは、ほとんどの企業にとって自社では不可能な処理です。\u003C/p\u003E\n\u003Cp\u003Eデータアナリティクスプラットフォームは、多くの場合クラウドベースで提供され、データ処理のための包括的な環境を実現するソフトウェアソリューションです。SnowflakeとTableauは、データの保存、分析クエリの実行、可視化の構築、複雑なワークフローの簡素化のための統合プラットフォームを提供しています。たとえば、\u003Ca href=\"https://www.snowflake.com/ja/product/platform/\"\u003ESnowflakeのAIデータクラウド\u003C/a\u003Eでは、データをプラットフォームにロードした後、主要なパブリッククラウド上で実行することで、高パフォーマンスのデータ運用に最適化されています。\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"],":type":"snowflake-site/components/container"},"container_copy_copy__1985496925":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"customer","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-257f83ae4b","additionalClasses":"headline-decoration","type":"heading2","lines":["結論"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"text":{"id":"text-08671d4707","text":"\u003Cp\u003Eデータ処理は、未整理の生データを、組織が情報に基づいた意思決定を行うために必要なビジネスクリティカルなインサイトに変換するうえで欠かせないエンジンです。データのロギングと分析は手作業で行われていた時代から大きく進化し、現在では、企業が生成している膨大な量のデータを処理するためにAIとMLを活用した強力な自動化ソリューションが提供されています。そして、このソリューションは飛躍的に増加し続けています。組織が将来の成長と成功を実現するためには、日々生成されるデータの海を理解するための効率的でインテリジェントなデータ処理がますます重要になっています。\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"],":type":"snowflake-site/components/container"},"container_copy_copy__127842237":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn 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Storm\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003Eビジネスインテリジェンス（BI）および可視化ツール：\u003C/b\u003ETableau、Microsoft Power BI\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E統合データプラットフォーム：\u003C/b\u003ESnowflake\u003C/p\u003E\n"},{"title":"ビッグデータによく使用されるデータソースには、どのようなものがありますか？","richText":"\u003Cp\u003Eビッグデータは、さまざまなソースから収集されます。このソースは、\u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003E構造化データ、非構造化データ、半構造化データ\u003C/a\u003Eの3つの種類に大別されます。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E構造化データ：\u003C/b\u003E高度に編成されており、あらかじめ決められた形式に従います。通常はテーブルに保存されるため、従来のツールを使用しての検索、管理、分析が最も簡単なデータの種類です。例：金融取引、販売管理（POS）データ、医療記録。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E非構造化データ：\u003C/b\u003E事前に定義されたフォーマットがありません。最も一般的なタイプのビッグデータですが、分析には最大の課題があります。それは、テキスト、画像、音声、動画が含まれることです。例：ソーシャルメディアデータ、PDFやEメール、スマートサーモスタットやウェアラブルデバイスのセンサーデータ。\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/semi-structured-data/\"\u003E半構造化データ\u003C/a\u003E：\u003C/b\u003E他の2つの種類を組み合わせたデータです。構造化データのような硬直した構造はありませんが、非構造化データよりも分類や分析が容易な組織的な性質を備えています。例：XMLとJSONファイル、ログファイル、ウェブページ。\u003C/p\u003E\n"},{"title":"データ処理の例としては、どのようなものがありますか？","richText":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E給与処理：\u003C/b\u003E勤務時間、控除、給与、税金の情報を含む従業員データを使用して、スケジュールどおりに給与を計算し、発行します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EEコマースのレコメンデーション：\u003C/b\u003Eオンラインストアを閲覧すると、その企業のシステムが検索履歴や過去の購入履歴などを処理して、気に入った商品をレコメンドします。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E天気予報：\u003C/b\u003E気象学者は、人工衛星、地上センサー、気象観測所からの膨大なデータを処理して複雑なモデルを作成し、気象パターンを予測して予報を発行します（ただし、お住まいの地域の予報がどの程度正確に感じられるかは、また別の話です）。\u003C/li\u003E\n\u003C/ul\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"],":type":"snowflake-site/components/container"},"container_copy_copy__1190438074":{"gridClassNames":"aem-Grid 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"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"},"flexible_column_cont":{"id":"flexible-column-container-78d2bf91a9","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-be45bc464c","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2":{"id":"card-v2-4f407cadc7","configurationStatus":{"configured":true,"message":""},"image":{"id":"image","alt":"luminate logo","lazyEnabled":true,":type":"snowflake-site/components/image"},"title":{"id":"title","type":"heading4","lines":["Luminate Amps Entertainment Analytics with 300% Faster Data Processing and Richer Insights"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/luminate/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"ストーリーを読む"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003Eビルボードの象徴的な音楽チャートを支え、エンターテイメント業界で最も信頼されるデータパートナーであるLuminateは、Snowflakeを中核に据え、より革新的な製品開発ときわめて精度の高いデータ処理を実現するためにデータを統合しました。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2"}},":itemsOrder":["card_v2"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-191e7f9a7f","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"card_v2_copy":{"id":"card-v2-c8950d6cc5","configurationStatus":{"configured":true,"message":""},"image":{"id":"image","alt":"power 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