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博士（以下、本田氏）は、当時の課題を次のように振り返る。\u003C/p\u003E\r\n\u003Cp\u003E「従来は数百億レコードが解析対象の最大規模でしたが、1兆レコードという規模が見えてきました。これを現実的な時間で処理するには、10台、20台で分散していた計算を、100台、200台へと大幅に増やす必要があります。しかし自前で確保できる計算機は50台から100台が上限で、サーバールームの制約からもそれ以上は困難です。必要なときに必要なだけ計算能力を確保できる環境を整えることが、目標の一つになりました」\u003C/p\u003E\r\n\u003Cp\u003E　もう一つの課題が、データの機密性である。企業から提供を受けたデータには、公開可能な情報よりも高い水準の管理が求められる。すべてを自組織内で管理できるオンプレミス環境と異なり、クラウドの利用は情報を外部に置くことを意味するため、活用には慎重な判断が求められた。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_c":{"id":"title-v2-36161e8036","type":"heading4","lines":["解決策：ISMAP準拠のフルマネージド基盤選定と、専門知見に基づく技術支援の融合"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy":{"id":"text-ff7af7d8d4","text":"\u003Cp\u003Eデータ基盤の選定で産総研が重視した要件は明確だった。第一は、フルマネージドであることだ。\u003C/p\u003E\r\n\u003Cp\u003E「私たち自身は情報技術を研究の対象としているわけではなく、あくまで技術を利用する立場です。管理コストを大きくかけることは避けたいと考えていました。フルマネージドであること、そして使いたい時間だけ使えることが、大きな要件でした」（本田氏）\u003C/p\u003E\r\n\u003Cp\u003E　第二は、データ基盤そのものの性能だ。複数の製品を比較した結果、解析機能よりもデータ基盤というインフラ部分に軸足を置く点で、Snowflakeが合致すると判断した。そして政府情報システムのためのセキュリティ評価制度「ISMAP」にSnowflakeが登録されていることも、クラウド活用への懸念を解消する鍵となった。\u003C/p\u003E\r\n\u003Cp\u003E　導入を支援したのは、DATUM STUDIOと、その子会社であるちゅらデータだ。両社は案件ごとに双方から人材を出し合い、共同でチームを編成する。契機は、産総研からの問い合わせだった。本田氏が着目したのは、DATUM STUDIO 執行役員 兼 ちゅらデータ CTOでSnowflake Data Superheroにも選出されている菱沼 雄太氏による、技術情報の発信である。\u003C/p\u003E\r\n\u003Cp\u003E「私たちがやりたいことの技術を、きちんと理解してくれるかどうかを重視しました。こちらが話した内容を正確に把握し、技術的な回答を返してくれること。そこが最も重要でした」（本田氏）\u003C/p\u003E\r\n\u003Cp\u003E構築期間は2カ月弱で、技術的な論点は二つあった。一つはデータのロードだ。解析はSQLで記述できるが、ロードの仕組みは産総研にとって知見の少ない領域だった。クラウドストレージへの効率的なアップロード手順や、取り込みやすいサイズへのファイル分割は、ちゅらデータ シニアデータ エンジニアの倉富 優氏（以下、倉富氏）が支援した。\u003C/p\u003E\r\n\u003Cp\u003E　もう一つは、産総研が従来利用してきた分析ツールとSnowflakeの接続だ。参照できる資料が少なく、動作を検証しながら進めた。倉富氏はこう語る。\u003C/p\u003E\r\n\u003Cp\u003E　「単純に接続するだけでは、データをクライアント側にダウンロードしてマージし、表示する形になります。それではSnowflakeの計算リソースを活かせず、単なるデータの保管場所になりかねません。設定を調整しながら検証を重ね、Snowflake側で計算処理が動く構成を見つけました」\u003C/p\u003E\r\n\u003Cp\u003E　ネットワークは、AWS PrivateLinkを介したプライベートな経路で接続し、既存のスーパーコンピューター環境の延長線上にSnowflakeを位置づけた。運用面では、オンプレミス環境になかったコストのアラート機能を設定し、権限付与を必要最小限にとどめてコードで統制している。従来オンプレミス環境のみで取得していたISO 27001およびJIS Q 15001の認証範囲も、Snowflakeを含む形へ拡張した。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_v2_copy_copy_8":{"id":"title-v2-be6042607a","type":"heading4","lines":["結果：計算リソースの動的拡張により、データ規模に左右されない解析スピードを実現"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color"},"text_copy_copy_63041":{"id":"text-16e4cd9d5c","text":"\u003Cp\u003ESnowflakeの導入により、産総研は計算リソースを柔軟に拡張できる環境を得た。本田氏が最大の効果として挙げるのが、この伸縮性だ。\u003C/p\u003E\r\n\u003Cp\u003E　処理速度も大幅に向上している。従来6時間を要していた解析が、1時間、場合によっては30分程度で完了する。この処理能力の向上は、これまで着手が難しかった大規模解析にも道を開いた。その一例が、気象衛星ひまわりの観測データを用いた全国規模の日射量解析である。全国を500メートル四方で捉えた観測データを住宅の所在地域に紐づけ、日射量や晴天の有無を特定する処理は、区分数が約50万、10年分では500億レコードに達する。従来の基盤なら1〜2週間は占有する規模だが、Snowflake上では数時間で完了した。本田氏は「基盤を長期間占有すると考えて躊躇していた処理を、まず試せるのは大きな利点です」と語る。\u003C/p\u003E\r\n\u003Cp\u003E　運用面の高度化も進む。当初はウェアハウスのサイズを利用者が判断していたが、適応型のコンピューティング機能「Adaptive Compute」により、最適なサイズが自動で選定される。\u003C/p\u003E\r\n\u003Cp\u003E　AIとの親和性の高さも、導入後の発見の一つだ。同チームは解析用のコードをAIコーディングツールで記述しているが、従来はSnowflake向けのSQLを外部の生成AIで生成して投入しても、外部ツールから直接接続しても、精度が上がりにくかった。生成されるコードは処理効率が低く、コスト面の無駄も生じやすい。状況が変わったのは、データネイティブなAIコーディングエージェント「Snowflake CoCo」の活用開始後だ。Snowflake自身が提供するエージェントであり、その仕様を前提としたコードが生成される。コストパフォーマンスの低いコードは減り、エラーによる再実行も減少したという。\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"quote-item_1790018859474_257067":{"id":"quote-item-a79d8be1a5","alignment":"left","layout":"card","quoteSourceName":"本田 智則 博士（工学）","quoteSourceTitle":"国立研究開発法人 産業技術総合研究所 ゼロエミッション国際共同研究センター データ駆動型社会システム研究チーム 研究チーム長 兼 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