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below","lazyEnabled":true,"isLcpImage":true,"width":"1999","title":"Figure 1: Customer journey graph highlighting degradation in the Complete Order step, with step-level impact metrics.",":type":"snowflake-site/components/image"},"blog_component_5":{"id":"blog-text-45653705e3","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E「製品購入」ジャーニーを例に考えます。ホーム → 製品一覧の取得 → 製品詳細 → 商品の追加 → 注文完了。レガシーツールでは、生のログエラーや、CPU使用率やメモリ使用率などのインフラストラクチャメトリクスの急増を確認しても、購入がブロックされているかどうかや、影響がどの程度広がっているかはすぐには分かりません。ジャーニーファーストのモデルでは、顧客体験の劣化から調査を始めます。\u003C/p\u003E\n\u003Cp\u003EObserveでカスタマージャーニーから始めることで、影響の範囲をほぼ即時に把握できます。影響の測定は、あらゆるインシデントにおける最初のステップであるべきです。Observeは、レート、エラー、継続時間（RED）のメトリクスとユニークセッションを、意味のあるコンテキストに集約します。調査は、ビジネスへの影響がすでに定義された状態から始まります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_6":{"id":"blog-title-9ffe066666","propertiesId":"2","type":"heading3","lines":["ステップ 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metrics","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 2: Drill down from the Complete Order interaction to the responsible service and operation, isolating the source of latency and errors.",":type":"snowflake-site/components/image"},"blog_component_9":{"id":"blog-text-b60e22f94c","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E注文完了インタラクションの劣化が特定されたら、次の問いは担当範囲と影響範囲です。注文完了から、サービスレベルまたはオペレーションレベルの内訳にピボットします。この例では、カートサービス内のEmptyCartオペレーションがレイテンシーとエラーの原因となっています。調査の対象は、障害が発生している顧客のステップから特定のオペレーションへと絞り込まれます。経営層は影響と重大度を確認し、サイトリライアビリティエンジニア（SRE）は同じダッシュボードとデータから問題のあるサービスを特定するため、異なるツールの結果を突き合わせる無駄な時間を最小限に抑えられます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"blog-title-b8f7214a8d","propertiesId":"3","type":"heading3","lines":["ステップ 3：トレースとログによる確認"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_11":{"id":"image-03b50205a8","height":"1159","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3cc4e213-2787-446d-a3d8-3c8d92c78719/distributed-tracing-waterfall-view-showing-frontend-proxy.png?preferwebp=true&quality=85","alt":"Distributed tracing waterfall view showing frontend-proxy service spans with highlighted errors in cart service operations","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 3: Trace waterfall confirming the failure path and associated error for a checkout request.",":type":"snowflake-site/components/image"},"blog_component_12":{"id":"blog-text-82f534147b","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E根本原因となっているサービスが特定されると、SREは担当のアプリチームをピンポイントで呼び出し、関連するテレメトリの調査を継続できます。トレースにピボットして、特定のリクエストでどこに時間がかかっているか、どこでエラーが発生しているかを確認し、次にダウンストリームで失敗しているスパンに関連するログにドリルダウンします。これでインシデントの全体像が明確になります。ユーザーが注文を完了できない → ダウンストリームのサービスで障害が発生 → 関連するリクエストのトレースで失敗を確認 → 明示的なエラーメッセージ（根本原因）。この例では、カスタマージャーニーを活用することで、呼び出されたアプリチームが通話に参加するのとほぼ同時に、SREはEmptyCartオペレーションがRedisからカートの内容を取得できないために顧客がチェックアウトを完了できないことを突き止めます。\u003C/p\u003E\n\u003Cp\u003Eこの例は、探索的なインシデント分析を示しています。対応者は顧客への影響を起点に、テレメトリデータレイク内の関係性、すなわちインタラクションからサービス、サービスからオペレーション、オペレーションからトレース、トレースからログへとたどりながら、障害モードが確認されるまで調査を絞り込んでいきます。構造はデータ内で定義されていますが、調査そのものは依然として人が主導しています。エンジニアは、次にどこへピボットするか、どのシグナルが重要か、そして調査結果をその場の他のメンバーにどうまとめて伝えるかを判断します。こうした総合的な判断は不可欠ですが、プレッシャーの中では時間がかかります。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_13":{"id":"blog-title-255de47668","propertiesId":"ai-sre","type":"heading2","lines":["AI SRE：根本原因への道筋の迅速化"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"image-75b2cec918","height":"1966","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ee1a0379-fb99-46ae-81eb-a52119e28105/root-cause-analysis-report-showing-redis-valkey-connection.png?preferwebp=true&quality=85","alt":"Screenshot of a root cause analysis report showing Redis/Valkey connection failures and payment card expiration errors","lazyEnabled":true,"isLcpImage":false,"width":"1936","title":"Figure 4: AI SRE traversing the observability context graph to produce a guided root cause analysis.",":type":"snowflake-site/components/image"},"blog_component_17":{"id":"blog-text-765d3a51fe","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EAI SREは、ジャーニーフローを支えるテレメトリデータレイク内の同じオブザーバビリティコンテキストグラフ上で動作します。エンジニアは各ステップを手動でたどる代わりに、「決済失敗の急増の原因は何か」と質問できます。AI SREは、接続されたインタラクション、サービス、トレース、ログをたどり、裏付けとなるエビデンスと、より深い分析のためのインタラクティブなクエリカードを備えたガイド付きの調査結果を数秒で返します。\u003C/p\u003E\n\u003Cp\u003EAI SREは、オブザーバビリティのワークフローにおいて大幅な生産性の向上をもたらします。お客様からは、タスクを平均で約4倍速く完了でき、一部の調査では最大10倍速くなったとの報告が寄せられています。（AI SREが調査時間を短縮するために\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/ai-sre-unified-telemetry-context-graph-incident-investigation/\"\u003E統合されたテレメトリとコンテキストグラフをどのように活用しているか\u003C/a\u003Eについて、詳しくはこちらをご確認ください。）\u003C/p\u003E\n\u003Cp\u003EAI SREも、データ内で定義された関係性に従います。ビジネスへの影響と実装を結び付ける同じ接続関係が調査を導くため、エビデンスの証跡を保持しながら、質問から回答までの時間を短縮できます。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_18":{"id":"blog-title-2b62348e93","propertiesId":"","type":"heading2","lines":["より大きなオブザーバビリティコンテキストグラフにおけるカスタマージャーニー"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_19":{"id":"image-a6dff272d2","height":"1265","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--08d60680-c9f9-4193-bf2b-c4cf5e95012d/network-graph-visualization-showing-clusters-of-blue-and.png?preferwebp=true&quality=85","alt":"Screenshot of a network graph visualization showing clusters of blue and purple nodes labeled with terms like Metrics, AWS, Tracing, and Reference Tables","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 5: The observability context graph in Observe. Customer journeys are one domain within a broader graph connecting services, infrastructure and external systems in the telemetry data lake.",":type":"snowflake-site/components/image"},"blog_text":{"id":"blog-text-1075a85a5b","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003E上記のジャーニーフローは、Observeにおけるより広範なオブザーバビリティコンテキストグラフ内の1つのドメインです。テレメトリデータレイクに取り込まれたすべてのテレメトリは、サービス、インフラストラクチャ、ビジネスエンティティ間の相互作用を反映したモデル化された関係性によって接続されています。\u003C/p\u003E\n\u003Cp\u003Eカスタマージャーニーは、そのグラフの一部をビジネス上のインタラクションを軸に整理したものです。その他のドメインでは、クラウドリソース、変更データ、AWSやGitHubなどの外部システムを、それらが影響を与えるサービスに接続します。各ノードとエッジは、事前定義されたダッシュボードではなく、データモデル内で定義された関係性を反映しています。\u003C/p\u003E\n\u003Cp\u003Eジャーニーのステップからサービスへ、あるいはサービスからトレースへとピボットするとき、そのより大きなグラフをたどっていることになります。AI SREも同じ構造上で動作します。ジャーニーは、システムとビジネスコンテキストの接続関係を表す共有モデルへの、ビジネスに即したエントリーポイントの1つです。\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_24":{"id":"blog-title-ea0396c50b","propertiesId":"","type":"heading2","lines":["ビジネスへの影響から根本原因に至るオブザーバビリティ"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_25":{"id":"blog-text-f00c08aa06","text":"\u003Cp\u003EObserveのカスタマージャーニーは、ビジネスへの影響を起点とすることで、調査が混沌とした状態から体系的なプロシージャへとどのように変わるかを示しています。Observeはテレメトリデータレイク上に構築されたコンテキストグラフ内の関係性を軸に構築されているため、システムとビジネスエンティティが実際にどのように接続されているかに基づいて、さまざまな種類のデータを横断的にピボットできます。\u003C/p\u003E\u003Cp\u003Eジャーニーグラフはそうした関係性を整理する方法の1つであり、Observeのコア機能の一部として包括的にサポートされています。同じ仕組みは、顧客、トランザクション、出荷、デバイス、通話、請求など、データ内でモデル化するあらゆるエンティティに適用できます。こうした関係性が定義されれば、AI SREによってインシデント発生中にダッシュボードやランブックを習得することなく、分析のためのコンテキストとして活用できるようになります。\u003C/p\u003E\u003Cp\u003EこれこそがCapital Oneの説明する変化です。顧客が体験していることから始め、コンテキストを保ったまま関連するデータだけをたどっていきます。Observeにおいて、カスタマージャーニーはテレメトリデータレイク上に構築されたより広範なオブザーバビリティコンテキストグラフ内の1つのドメインです。そのグラフはシステムとビジネスエンティティの接続関係を反映しているため、ビジネスへの影響から根本原因への道筋はすでに定義されており、より迅速かつ効率的な調査につながります。\u003C/p\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/observe/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EObserve by Snowflakeの詳細\u003C/a\u003Eと、テレメトリをすばやくインサイトに変換する仕組みについては、こちらをご確認ください。\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_1","blog_component_2","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_15","blog_component_17","blog_component_18","blog_component_19","blog_text","blog_component_24","blog_component_25"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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1：顧客とのインタラクションから開始","level":"h3","anchorId":"#1","hierarchicalChildrenStructure":[]},{"headingText":"ステップ 2：障害が発生しているオペレーションへのドリルダウン","level":"h3","anchorId":"#2","hierarchicalChildrenStructure":[]},{"headingText":"ステップ 3：トレースとログによる確認","level":"h3","anchorId":"#3","hierarchicalChildrenStructure":[]}]},{"headingText":"AI SRE：根本原因への道筋の迅速化","level":"h2","anchorId":"#ai-sre","hierarchicalChildrenStructure":[]},{"headingText":"より大きなオブザーバビリティコンテキストグラフにおけるカスタマージャーニー","level":"h2","anchorId":"#","hierarchicalChildrenStructure":[]},{"headingText":"ビジネスへの影響から根本原因に至るオブザーバビリティ","level":"h2","anchorId":"#","hierarchicalChildrenStructure":[]}]}},":itemsOrder":["blog_table_of_content"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":true,"isActiveTOC":true},"related_content":{"id":"related-content-8dbc9b9a92","relatedContent":[{"configurationStatus":{"configured":true,"message":""},"tags":[{"tagText":"製品 & 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