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noreferrer\"\u003Eリアルタイム分析\u003C/a\u003Eとは、ライブデータを数秒以内に信頼できるインサイトとアクションに変える機能です。リアルタイム分析は、イベントの発生に合わせてデータを処理および分析し、人やシステムに現在何が起きているかを示します。適切なレイテンシーはワークロードによって異なります。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"image-71cc9870f3","height":"708","src":"/adobe/dynamicmedia/deliver/dm-aid--ae41b3b0-11ad-4134-9ca6-2568fedb8420/architecture-diagram-showing-data-flow-from-message-bus.png?quality=85&preferwebp=true","alt":"Architecture diagram showing data flow from message bus through processing engine to serving database and dashboards with warnings","lazyEnabled":true,"isLcpImage":true,"width":"1786","title":"Figure 1: Traditional real-time analytics architecture.",":type":"snowflake-site/components/image"},"blog_component_4":{"id":"blog-text-f5013ab1f2","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ESnowflakeは現在、リアルタイムでのエンドツーエンドのアナリティクスのために、接続性、変換、提供を統合しています。このブログ記事では、これらの機能、つまりデータを接続し、処理中に変換し、高速に提供する方法に焦点を当てます。Snowflakeは、チームがそれぞれのニーズに合わせてアーキテクチャを再構築することなく、以下のニーズを満たすように設計されています。\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003E大規模環境でのシンプルさ：\u003C/b\u003E背後にあるすべてのシステムの専門家にならなくても、リアルタイムパイプラインを構築できます。Snowflakeは、接続性、変換、提供にわたるインフラストラクチャ、スケーリング、パイプラインを管理します。\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/snowflake-coco/\"\u003ESnowflake CoCo\u003C/a\u003Eのスキルにより、パイプラインのセットアップがガイド付きの会話に変わります。必要なものを説明するだけで、CoCoがロジックを構成、構築、実装します。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003E低い総コストで高速なパフォーマンスを実現：\u003C/b\u003E完全に独立したリアルタイムスタックに費用をかけることなく、最新のインサイトを提供します。チームは、個別の提供用データベースや重複したストレージの必要性を排除し、それらの運用、監視、トラブルシューティングに必要なエンジニアリングの労力も削減できます。社内テストでは、Snowpipe Streaming、ストリーミング変換、およびSnowflakeインタラクティブアナリティクスを使用した場合、\u003Cb\u003EエンドツーエンドのレイテンシーのP50が3秒\u003C/b\u003E\u003Csup data-footnote-ref=\"true\" id=\"footnote-ref-1\"\u003E\u003Ca href=\"#footnote-1\"\u003E[1]\u003C/a\u003E\u003C/sup\u003Eであることが確認されています。\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003Eすべてのステップでのガバナンス：\u003C/b\u003E統合された環境で、データとAIアセットを検出し、セキュアにし、構築します。\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003EHorizonカタログ\u003C/a\u003Eは、Snowflake全体でアクセス制御、リネージ、データ品質、ビジネスコンテキストを統合します。データとAIアセットのデータ品質モニタリングとデータ分類により、組織全体で信頼を構築します。\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"image-1bc9579368","height":"776","src":"/adobe/dynamicmedia/deliver/dm-aid--586efccf-2f46-466e-a18a-4787da693c4e/snowflake-data-platform-architecture-diagram-showing.png?quality=85&preferwebp=true","alt":"Snowflake data platform architecture diagram showing Connect, Transform, and Serve stages with various data sources and services","lazyEnabled":true,"isLcpImage":false,"width":"1646","title":"Figure 2: Snowflake offers real-time connectivity, transformation and serving in one connected environment.",":type":"snowflake-site/components/image"},"blog_component_6":{"id":"blog-title-c6824f1eda","propertiesId":"","type":"heading2","lines":["インフラストラクチャを追加せずにリアルタイムデータを接続"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_7":{"id":"blog-text-e19e04c8da","text":"\u003Cp\u003Eリアルタイム分析は、データを迅速かつ簡単に取り込むことから始まります。\u003Cstrong\u003ESnowpipe Streaming\u003C/strong\u003Eは、アプリケーション、デバイス、サービスからのデータをSnowflakeネイティブテーブルまたはIcebergテーブルに直接取り込みます。チームは、自社のアーキテクチャに合ったテーブルフォーマットを柔軟に選択できます。ネイティブテーブルは、運用アナリティクスやアプリケーション向けのシンプルでフルマネージドなパスを提供します。Icebergテーブルは、他のツールがアクセスできるオープンな形式でデータを保持します。データは数秒でクエリ可能になり、取り込みベースの定額料金モデルで\u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/snowpipe-streaming/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eテーブルあたり最大20GB/秒\u003C/a\u003E\u003C/strong\u003Eのスループットを実現します。\u003C/p\u003E\u003Cp\u003E取り込みは、多くの場合、リアルタイムプロジェクトが出荷されるかどうかを決定する項目となります。新しいソースを追加するたびにコストが上昇すると、チームは希望するよりも少ないデータしかライブ状態に保てなくなります。同等のワークロードにおいて、Snowpipe StreamingはZerobusよりも\u003Cstrong\u003E5倍安く\u003C/strong\u003E\u003Csup id=\"footnote-ref-2\" data-footnote-ref=\"true\"\u003E\u003Ca href=\"#footnote-2\"\u003E[2]\u003C/a\u003E\u003C/sup\u003E、ClickPipesよりも\u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/engineering/efficient-snowflake-ingestion-query-ready/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E80%安価\u003C/a\u003E\u003C/strong\u003Eです\u003Csup id=\"footnote-ref-3\" data-footnote-ref=\"true\"\u003E\u003Ca href=\"#footnote-3\"\u003E[3]\u003C/a\u003E\u003C/sup\u003E。取り込むデータに基づいた定額料金を支払うため、ソースを追加してもコストは予測可能なままです。\u003C/p\u003E\u003Cp\u003E金融サービスチームにとって、1秒1秒が重要です。取引活動、トランザクション記録、運用イベントを数秒以内に分析できるようにすることで、チームは最新の情報に基づいて行動できるようになります。\u003Ca href=\"https://youtu.be/sHi8ZMS2JJw?si=7xzxX-KjG_UUeR9C&t=1002\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECboe\u003C/a\u003E \u003Ca href=\"https://youtu.be/sHi8ZMS2JJw?si=7xzxX-KjG_UUeR9C&t=1002\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EGlobal Markets\u003C/a\u003Eは、このアプローチの規模を実証しており、Snowpipe Streamingを使用して\u003Cstrong\u003E1日あたり1,900億\u003C/strong\u003E\u003Cstrong\u003E行を30秒未満で\u003C/strong\u003E格納し、そのデータをインタラクティブウェアハウスを通じてアナリストに直接提供しています。\u003C/p\u003E\u003Cp\u003Eイベントの量が増えても、チームは取り込みの仕組みの管理に多くの時間を費やすべきではありません。Snowpipe Streamingの\u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/snowpipe-streaming-elastic-channels-ga/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EElastic Channels\u003C/a\u003E\u003C/strong\u003E（一般提供）を使用すると、アプリやIoTデバイスを含む何百ものプロデューサーが、単一の弾力性のあるチャネルを通じて1つの宛先に書き込むことができます。追加のチャネルを作成または管理することなく、より多くのソースを接続できます。Snowflakeは、ボリュームの増加に合わせて取り込みパスを自動的にスケーリングします。配信は正確に1回だけ順序付けられるため、チームはダウンストリームで重複排除ロジックを記述する代わりに、分析に時間を費やすことができます。社内テストでは、スループットをさらに押し上げ、\u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/snowpipe-streaming-elastic-channels-benchmark/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E単一のテーブルへの書き込みで41GB/秒\u003C/a\u003E\u003C/strong\u003Eに達しました。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_8":{"id":"image-7c85934fcd","height":"736","src":"/adobe/dynamicmedia/deliver/dm-aid--ff80b31d-6480-4fd4-acc9-d01b0fe87e49/multi-hop-data-pipeline-architecture-with-snowpipe.png?quality=85&preferwebp=true","alt":"Diagram comparing multi-hop data pipeline architecture with Snowpipe Streaming Elastic Channels simplified approach to Snowflake","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 3: Elastic Channels let you stream directly into Snowflake without introducing another intermediary solely for ingestion.",":type":"snowflake-site/components/image"},"blog_component_9":{"id":"blog-text-6ff243e2e7","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E多くのチームはApache Kafka®のプロデューサーとコンシューマーを導入していますが、ストリーミングインフラストラクチャは多くの場合、インフラストラクチャエンジニア向けに設計されています。\u003Cb\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/datastream/\"\u003EDatastream\u003C/a\u003E\u003C/b\u003E（まもなくパブリックプレビュー、現在はプライベートプレビュー）は、データ中心のアプローチを採用しています。これはSnowflakeネイティブのKafka互換サービスであり、データチームはブローカーを運用したり、データをテーブルに移動するための個別のコネクタレイヤーを維持したりすることなく、マネージドエンドポイントで既存のKafkaプロデューサーとコンシューマーを使用できます。データ中心のストリーミングは、ガバナンスの効いたデータアセットを中心にチームを再編成するため、パーティション、レプリケーション、クラスター容量ではなく、トピック、鮮度、パイプラインを管理するようになります。MintedやZywaveなどのお客様は、このアプローチを使用して、個別のKafkaとSnowflakeの認証、ロール、監視を削減しています。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"image-e1660b40bc","height":"666","src":"/adobe/dynamicmedia/deliver/dm-aid--031714ee-f064-44bf-9fd3-0f0e6475b75a/comparison-diagram-showing-infra-centric-versus-data.png?quality=85&preferwebp=true","alt":"Comparison diagram showing infra-centric versus data-centric approaches across starting point, mental model, data landing, and scale model","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 4: Data-centric streaming changes where you start, how you think and what you spend time on.",":type":"snowflake-site/components/image"},"blog_component_11":{"id":"blog-title-846cd6e68d","propertiesId":"","type":"heading2","lines":["ビジネスに必要な鮮度でデータを変換"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_12":{"id":"blog-text-d82f643e0c","text":"\u003Cp\u003E最新のデータは、ビジネスが必要とする形になって初めて意味を持ちます。\u003Cstrong\u003Eストリーミング変換\u003C/strong\u003E（プライベートプレビュー）は、Snowpipe Streamingでの取り込み中にデータをフィルタリング、強化、再形成、集計することで、受信データをリアルタイムでアナリティクスに使用できるようにします。チームはSQLパイプ定義でロジックを1度定義するだけで、レコードはテーブルに格納される前に変換されます。\u003C/p\u003E\u003Cp\u003Eチームは、無関係なイベントを除外したり、受信レコードをリファレンスデータで強化したり、カスタムロジックを適用したり、ネストされた配列を個々の行に展開したりできます。変換を取り込みパスに組み込むことで、ストリーミングスタックが簡素化され、最終的にデータがより早く有用になります。これは、すでにいくつかのSnowflakeのお客様が実感していることです。ある産業用機器の製造企業は、アナリティクスワークフローが一貫した名前のレコードを受け取れるように、お客様とアセットの識別子を標準化しています。一方、あるサイバーセキュリティ企業は、ストリーミング変換を使用してセキュリティイベントをリアルタイムで処理し、監視とレポートの処理コストとレイテンシーを削減しています。\u003C/p\u003E\u003Cp\u003Eサポートされている他のワークロードの場合、ストリーミング変換は、外部のストリーム処理レイヤーの必要性や、データが格納された後のダウンストリーム変換の構築、スケジュール設定、維持の必要性を排除するのに役立ちます。これは、運用するインフラストラクチャが減り、生のイベントから実用的なインサイトまでのパスが短くなることを意味します。ストリーミング変換は、標準のSnowflakeテーブル、インタラクティブテーブル、およびSnowflakeマネージドIcebergテーブルをサポートしています。現在の取り込みから提供までの鮮度目標は、インタラクティブの場合は3秒未満、標準およびIcebergシンクの場合は10秒未満です。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_13":{"id":"image-3751d01117","height":"725","src":"/adobe/dynamicmedia/deliver/dm-aid--5a7e143b-ef62-4da8-890b-224e06655580/stream-processing-features-alongside-sql-code-screenshot.png?quality=85&preferwebp=true","alt":"Diagram showing stream processing features alongside SQL code screenshot for creating an enriched events data pipe in Snowflake","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 5: Join the incoming stream to reference dimension tables with a left outer join, so enriched rows land directly in the target table.",":type":"snowflake-site/components/image"},"blog_component_14":{"id":"blog-title-c299f2a6ca","propertiesId":"1","type":"heading2","lines":["インタラクティブアナリティクスにより、高い同時実行性で1秒未満の応答を提供"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"blog-text-951128cc38","text":"\u003Cp\u003Eラストマイルは、多くのリアルタイムプロジェクトが失敗する場所です。ダッシュボード、お客様向けアプリ、AIエージェントはすべて同時に同じデータにアクセスするため、チームは対応するために別の提供用データベースに手を伸ばすことがよくあります。\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/analytics/interactive-analytics/\"\u003Eインタラクティブアナリティクス\u003C/a\u003Eは、これらのワークロードをSnowflakeで直接提供します。\u003C/p\u003E\n\u003Cp\u003E社内テストでは、\u003Cb\u003Eインタラクティブウェアハウスは\u003C/b\u003E、標準のウェアハウスと比較して\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/introducing-interactive-analytics/\"\u003E9倍高い同時実行性と3倍低いレイテンシー\u003C/a\u003Eで1秒未満のクエリを提供しました。\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/zero-copy-interactive-analytics/\"\u003Eゼロコピーインタラクティブ\u003C/a\u003E（一般提供）を使用すると、既存の標準テーブル、動的テーブル、Icebergテーブルで直接実行されます。低レイテンシーの提供のために専用のコピーは必要ありません。ストリーミング取り込みの場合、テーブルあたり最大41GB/秒をサポートし、データは数秒以内にクエリ可能になります。\u003C/p\u003E\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/\"\u003EMcKnight Consulting Group\u003C/a\u003Eが実施した2つのベンチマークテストでは、同等の時間あたりのコンピュートコストで、Snowflakeは最大32の同時ユーザーまで、テストされたすべての同時実行性レベルで\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/when-everyone-asks-at-once-designing-snowflake-for-interactive-analytics/\"\u003E100%のクエリ\u003C/a\u003Eを完了しました。また、ダッシュボードクエリにおいて、主要なリアルタイム分析データベースの\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/counting-distincts-and-high-cardinality-aggregations-designing-snowflake-for-interactive-analytics/\"\u003E5倍以上のスループット\u003C/a\u003Eを提供した一方で、競合他社は高カーディナリティの集計の73%で失敗しました。突然の5倍のトラフィックスパイクをシミュレートした\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/auto-scaling-for-spiky-workloads-designing-snowflake-for-interactive-analytics/\"\u003E3番目のテスト\u003C/a\u003Eでは、Snowflakeは自動的にスケールアウトし、99.997%のクエリをSLA内に維持し、p99レイテンシーはピークに合わせてサイズ設定された競合他社よりも40%低くなりました。Snowflakeの結果に匹敵するためには、競合他社はピークサイズの容量を24時間体制で実行する必要があり、年間コストはほぼ2倍（31,273ドル対15,959ドル）になります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_16":{"id":"image-0e75a9c1c0","height":"235","src":"/adobe/dynamicmedia/deliver/dm-aid--a6779496-363a-41eb-a282-fc289b68dc05/horizontal-bar-chart-comparing-one-year-compute-costs.png?quality=85&preferwebp=true","alt":"Horizontal bar chart comparing one-year compute costs between ClickHouse at $31,278 and Snowflake Interactive XS at $15,959","lazyEnabled":true,"isLcpImage":false,"width":"765","title":"Figure 6. Modelled one-year compute cost with weekly (52) spikes, $3.00 per Snowflake credit, $1.19 per ClickHouse replica-hour. Compute only; storage is excluded on both sides. Source: https://mcknightcg.com/auto-scaling-for-spiky-workloads-designing-snowflake-for-interactive-analytics/",":type":"snowflake-site/components/image"},"blog_component_17":{"id":"blog-text-2e3b040b03","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E提供はビジネスで使用するのと同じガバナンスの効いたデータで行われるため、維持する同期ジョブやセキュアにする2番目のコピーはありません。チームはデータを1回保存し、そのコピーから提供するため、ボリュームと同時実行性が増加してもコストは予測可能なままです。\u003C/p\u003E\n\u003Cp\u003Eある例では、個別の脅威検出プラットフォームを立ち上げるのではなく、\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/customers/all-customers/snapshot/canva/\"\u003ECanvaはデータに検出機能をもたらしました\u003C/a\u003E。インタラクティブアナリティクスは、ライブ監査ログとCanvaがすでにSnowflakeに保持している信頼できるリファレンスデータに対して、1分間に数千の検出クエリを実行し、維持する2番目のコピーはありません。本番環境レベルの評価では、インタラクティブウェアハウスは、同等の標準ウェアハウスの10倍以上の同時負荷を、大幅に低いモデル化コストで処理しました。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_20":{"id":"quote-item-d938fe6c97","alignment":"left","layout":"card","quoteSourceName":"Calvin Wang","quoteSourceTitle":"Staff Analytics Engineer, Security Group, Canva","showQuoteIcon":true,"quote":"私たちは、別のプラットフォームを新たに構築したり、そのための専任スタッフを配置したりすることは望んでいませんでした。自社のプロダクトやコミュニティに関する最も価値あるデータはすでにSnowflake上に集約されていたため、私たちは「検知ロジックをデータのある場所へ持ち込む」という選択をしました。その結果、アラートの精度（忠実度）も自ずと向上したのです。","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_18":{"id":"blog-title-83d0def058","propertiesId":"","type":"heading2","lines":["今すぐ始める"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_19":{"id":"blog-text-9875d4051a","text":"\u003Cp\u003E企業は、最新のデータを意思決定に変えるためのよりシンプルな方法を必要としています。Snowflakeは、運用がシンプルで、提供が高速で、コスト効率よく拡張でき、ネイティブテーブルとIcebergテーブル全体でオープンであり、最初のイベントから最終的な意思決定までガバナンスが効いた、リアルタイム分析のための統合環境を提供します。そして、Snowflake CoCoにより、すべてのステップの構築が容易になります。必要なものを自然言語で説明するだけで、CoCoが接続性のセットアップ、変換の構築、インタラクティブアナリティクスの構成を支援します。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-15c8877361","youtubeVideoId":"wdmXRp8Cq2w","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":false,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"blog_text":{"id":"blog-text-53ff2ceed4","text":"\u003Cp\u003Eオンデマンドウェビナー「\u003Ca href=\"https://www.snowflake.com/en/webinars/thought-leadership/realtime-data-the-foundation-of-agentic-ai-2026-09-30/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eリアルタイムデータ:エージェント型AIの基盤\u003C/a\u003E」で、各要素がどのように組み合わさるかをご覧ください。このウェビナーでは、ダッシュボード、アプリケーション、AIエージェントをサポートするリアルタイム分析を構築するためのベストプラクティスとリファレンスアーキテクチャを共有します。\u003C/p\u003E\n\u003Chr\u003E\n\n\u003Col data-footnotes=\"true\"\u003E\n\u003Cli id=\"footnote-1\"\u003E データ準備のレイテンシーの中央値（P50）である3秒は、2026年10月20日に実施されたSnowflakeの社内テストで測定されたものであり、Snowpipe Streaming、ストリーミング変換、およびインタラクティブアナリティクスを通るフルパスをカバーしています。\u003Ca href=\"#footnote-ref-1\" aria-label=\"脚注[1]に戻る\"\u003E↩\u003C/a\u003E\u003C/li\u003E\n\u003Cli id=\"footnote-2\"\u003E コスト比較は、Snowpipe Streamingの0.0037クレジット/GB × 2.35ドル/クレジット（Snowflakeの平均クレジット価格）に基づいており、約0.0087ドル/GBに相当します。Databricks Zerobusの定価0.05ドル/GBと比較すると、これは約5.75倍になり、平均して約5倍に丸められます。\u003Ca href=\"#footnote-ref-2\" aria-label=\"脚注[2]に戻る\"\u003E↩\u003C/a\u003E\u003C/li\u003E\n\u003Cli id=\"footnote-3\"\u003E ClickPipesの公開されているストリーミング取り込み価格0.04ドル/GBと比較すると、コストは約78%低くなり、80%に丸められます。ClickPipesはコンピュートに対しても別途課金しますが、これらの料金はこの比較から除外されています。\u003Ca href=\"#footnote-ref-3\" aria-label=\"脚注[3]に戻る\"\u003E↩\u003C/a\u003E\u003C/li\u003E\n\u003C/ol\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_1","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_component_8","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17","blog_component_20","blog_component_18","blog_component_19","youtube","blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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