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永豐銀行打破資料束縛 以MongoDB Enterprise Advanced優化客戶服務體驗

朝向華人金融第一品牌邁進 永豐銀行持續擴大服務範疇 隨著全球進入數位化金融時代，消費者已逐漸習慣透過行動銀行、網路銀行等辦理轉帳、投資等相關業務，也讓實體分行辦理業務的比例大幅降低。而長達3年的疫情衝擊，更讓消費者對金融業者的線上服務依賴度日增，所以近幾年金融產業都加快推出創新服務的速度，全力打造最佳消費者體驗，以便能在競爭日趨激烈的環境中提升競爭力。 隸屬永豐金控集團旗下的永豐銀行，在財富管理有豐富專業知識與經驗，致力為客戶提供專業、個性化的財富管理解決方案，透過基金、海外債及美股多元投資工具積極打造滿足高資產客戶的平台。此外，該銀行亦積極在不斷變化市場中，努力確保客戶的投資組合穩健增長，期盼能實現預先設定的財務目標，也因此多次榮獲銀行業界「最佳客戶滿意獎」的肯定。 永豐銀行專業副理楊文淵說：「永豐銀行以綿密的營業網路、高度創新的商品組合，致力為客戶提供最佳的金融服務，實踐『翻轉金融，共創美好生活。Together, a better life. 』企業願景，為提供客戶靈活、完整的金融整體解決方案，朝向華人金融第一品牌目標邁進。MongoDB Enterprise Advanced能讓我們釋放資料的潛力，為用戶提供更佳的使用者體驗。」 挑戰 傳統交易資料查詢限制多 仰賴NoSQL突破限制 面對競爭激烈的臺灣金融市場，致力打造友善金融服務的永豐銀行，全力運用創新科技建構貼近客戶的日常生活，如近期推出的小蜜豐平台或首創建置掌靜脈辨識系統的ATM等，主打能為用戶提供線上到線下的完整體驗。該公司以虛實整合（Smooth）、流程智能化（Smart）與服務個人化（Sweet）的3S核心為起點，加上積極響應「綠色金融3.0」及「信託2.0」等政策，期盼以多元服務協助企業減碳且逐步邁向淨零目標、提升產業鏈價值，共同推動臺灣成為永續家園。 儘管永豐銀行連續三年獲得Forbes雜誌評比「年度全球最佳銀行」（World’s Best Banks）肯定，然而並沒有因此自滿，更特別將IT部門分派到各業務單位之中，持續尋找任何能優化的系統與服務。團隊發現現行系統只能讓客戶在線上查詢6個月內的交易資料，無法滿足實務上的需求。因此，必須透過引進NoSQL資料庫方式，打破傳統關連式資料庫的限制，為客戶提供更全面的資料查詢服務。 楊文淵指出，「因應金融法規要求，無論是客戶的銀行或信用卡等交易紀錄，我們都會完整保留下來。只是受限於關聯性資料庫的限制，資料庫都是採取以年為單位進行切割。為了避免用戶查詢歷史交易的資料量過大，導致系統主機資源被大量佔用，目前幾乎每家銀行僅提供在線上查詢1年內的資料，每次最多查詢6個月。我們認為既然資料完整被保存下來，那應該要尋找可打破查詢限制的方法，而NoSQL資料庫自然是最佳方案。根據銀行法規，我們尚且不能將交易資料放在公有雲端平台上，而能提供資料加密功能的MongoDB Enterprise Advanced，自然是我們的最佳選擇。」 解決方案 將創意轉為可行方案 儘管理論上引進NoSQL資料庫可降低大量資料查詢負擔，但仍然必須經過實際驗證才可證明想法可行與否。在MongoDB 顧問團隊協助下，小規模POC驗證順利完成。在確認原本構想可行之後，MongoDB 顧問團隊也隨即安排2天教育訓練課程，讓永豐銀行團隊能在最短時間內熟悉MongoDB資料庫的運作和啟動專案。 除了完整技術支援服務之外，MongoDB Enterprise Advanced也支援身份驗證、存取控制、加密等安全性功能。使用AES256-CBC加密模式，可保護MongoDB資料庫的安全，避免發生資料外洩事件，也同時符合金管會的法規要求。 其次，產品內建功能強大的Ops Manager ，管理人員可藉此加快部署、備份MongoDB速度，同時也可完整監控資料庫的健康狀況，免去傳統手動處理的繁瑣步驟，大幅降低維運的工作負擔。 楊文淵指出，由於銀行業是受到高度監管的產業，因此相關法令規範非常嚴謹。資料庫本身若沒有完整的安全功能，如身份驗證、加密等，基本上絕對不能使用在對外服務之中。綜合技術支援服務、安全功能等因素，也促成永豐銀行引進MongoDB Enterprise Advanced。尤其具備完整管理功能的Ops Manager，則讓團隊大幅降低維運的工作負擔，對永豐銀行來說幫助非常大。 在MongoDB顧問團隊協助下，永豐銀行大約用了6個月的時間進行開發，並在沒有更改動系統架構狀況下，加裝一台負責資料提取、轉換、載入的ETL（extract、transform、load）主機 ，可自動將銀行核心系統、信用卡系統上的客戶交易資料，傳送到MongoDB主機上大幅降低資料轉移的時間，以及系統維運人員的負擔。 另外專案負責人楊文淵提也到，此次計畫不止透過MongoDB彈性的資料格式以及擴充功能來降低原來ODS (Operational Data Store) 系統的負擔，更透過一系列資料運算價值的功能來提升行動銀行的使用者體驗，做到獲客之外還有留客，加深客戶與永豐銀行的互動經驗，於8月正式推出永豐銀行智慧收支帳本服務。總共包括六大功能: 歷史交易一目瞭然、創新業界整合帳戶卡戶明細、搜尋與篩選功能、交易明細編輯功能、交易明細自動分類、特定交易明細排除功能，後續也將推出更多個人化等智慧理財服務，透過服務創新與資料加值深度經營與客戶間的關係。 展望未來 以 MongoDB Enterprise Advanced為核心 持續提升資料價值 目前永豐銀行是臺灣金融市場中，唯一可在線上提供客戶查詢超過10年帳戶卡戶交易紀錄的業者。此項獨步市場的服務，有助永豐銀行提升優勢，以爭取更多新客戶。 為提供更優質的服務，永豐銀行計劃擴大MongoDB Enterprise Advanced的應用範疇，如透過收集與分析用戶的消費行為，作為後續推動行銷服務的參考等，全力朝向華人第一品牌銀行的目標邁進。 「MongoDB Enterprise Advanced的強大資料整合與分析功能，讓我們順利打破傳統關聯資料庫的限制，為客戶提供全新的服務項目，更為公司的長遠發展奠定穩健基礎。未來，我們會繼續與 MongoDB緊密合作，讓永豐銀行的服務能量持續攀升。 」 永豐銀行專業副理楊文淵

December 7, 2023

Zomato manages high-volume data and delivers high-speed success

Zomato is one of India’s largest consumer technology companies. The restaurant aggregator and food delivery operator provides restaurant information, menus and user reviews, and food delivery options from partner restaurants in over 1,000 Indian cities and towns. With over 17.5 million customers, 220,000 restaurant partners and 350,000 delivery partners, Zomato deals with data in huge quantities. However, in 2017 Zomato became increasingly aware of the pain points of database management. “Zomato was expanding at a remarkable pace,” says Abhishek Jain, senior software engineer, Zomato at MongoDB.local New Delhi earlier this year. “We realized that it would be prudent to migrate all our self-hosted clusters to a managed platform, which is when we integrated with MongoDB Atlas. As Zomato and its portfolios kept on expanding, so did our use cases for MongoDB.” Using features such as index suggestions, geospatial queries and analytics nodes, MongoDB is now the driving force behind many of Zomato’s key operational systems: The Order Tracking system covers live order statuses as well as live locations of delivery partners. The Order Assignment system is one of the most critical components, and one of the most complex. The Order Details system displays items and quantities for each order to ensure smooth fulfillment. The Delivery Partner Expected Earnings feature combines key information such as a rider’s location, their distance from the partner restaurant and customer, and the type of transport used. The Featured Restaurants system is what most people see when using Zomato app. A key marketing tool, it creates bespoke campaigns and offers, and suggests local restaurants to users. The Delivery Partner Onboarding system simplifies the onboarding process by collecting and storing drivers’ names, addresses, driving licenses, vehicle type and more. Another critical tool, the Delivery Partner Location system, is central to Zomato. With thousands of riders on the streets of India continuously publishing their locations at very high rates, flexible and responsive persistent storage is vital. “We get hundreds of thousands of requests per minute,” he explains. “We need a database capable of handling this load without any issues or latency, even during extremely high-volume periods such as New Year’s Eve. Again, MongoDB was the answer.” A final core element is Chat SDK, a central messaging hub that enables customers and delivery partners to stay in contact via email or text message and can include images and video. “All this data is non-relational, so we need a NoSQL data store that provides strong consistency under heavy read and write workloads,” says Soumil Kanwal, Software Engineer at Zomato. “MongoDB's document-oriented database fits our requirements perfectly.” He concludes: “We’re in the process of migrating features from other databases to MongoDB and building new features on top of those. MongoDB manages them all very comfortably.” Check out the full presentation now

December 6, 2023

阿里云数据库MongoDB版助力掌阅平滑上云，撬动数据红利

掌阅科技：深耕优质内容 优化数字阅读体验 掌阅科技股份有限公司成立于2008年9月，专注于数字阅读，是全球领先的数字阅读平台之一。 掌阅主营业务为互联网数字阅读服务及增值服务，同时从事网络原创文学版权运营，以及基于自有互联网平台的流量增值服务，服务覆盖自研产品、阅读服务、原创矩阵、网文出海、终端产品等五大方面。 掌阅的自研产品包括掌阅App、掌阅精选App、掌阅课外书App、得间小说App、阅爱聊等。其中，掌阅App是掌阅科技自研的数字阅读平台，拥有国学经典、严肃出版、原创文学、有声读物、漫画杂志等海量阅读内容，在业内率先实现了3D仿真翻页、护眼模式等技术的创新与引用，并在文档识别、转化、续读技术以及精装排版等方面形成了核心技术优势，处于行业领先水平。 业务挑战 数字阅读渐成主流 数据拓展需要数据管理同步升级 潜心数字阅读领域十余年，掌阅积累了海量阅读数据，如何做到这些数据的平滑升级和扩展、如何将这些数据充分利用起来并指导企业工作？是掌阅最大的痛点，也是掌阅多年来一直在研究和探索的问题。 依据阅读场景下的数据特点，掌阅在数据方面主要面对以下挑战： 双向数据 – 人和书这个关系相对复杂。如一个人喜欢哪些书、同时这些书又被哪些人同时喜欢，这在关键性数据库里需要建两张表，人一张表，书一张表。更为重要的是，要在两张表的数据关系中找到潜在读者、客户。这就需要一个能够有效管理非结构化数据的复合型索引。 字段的不确定性 – 在具体阅读的过程中，掌阅旗下不同APP要适应多重场景和运营活动，随着定位和画像的不同，数据标签随时更新、变化。传统的关系型数据库不再适合厘清这些不确定字段的数据信息。 数据升级 – 围绕掌阅的几大主营业务，涉及各个领域的APP矩阵不断扩大，数据量级已达到TB级，这种高并发海量数据需要数据管理的同步升级。 解决方案 阿里云数据库MongoDB版落地掌阅多元业务场景 大幅提升服务性能 2022年4月，掌阅科技在年报内显示将其核心服务进行云原生的改造，在大幅提升研发效率的同时，保证良好的服务可用性，持续提升服务性能30%以上。对客户端进行大幅包体瘦身和稳定性加固，崩溃率降低50%以上，冷启动打开速度提升30%以上，包体积降低30%以上。 这与掌阅选择阿里云数据库MongoDB版不无关系。掌阅平台研发部负责人张博表示，这主要是考虑到MongoDB在非结构化数据管理方面的显著优势，一是文档结构，字段可灵活扩展，可实现双向数据查询；二是支持Sharding、多节点容灾，实现海量数据的存储和可用性；三是事务支持，可实现多文档事务；四是阿里云的支持，为未来混合云架构打好基础。 张博还对阿里云数据库MongoDB版在云书架、书评、流水帐单、类积分管理四个业务场景中的落地实践进行了介绍。 云书架 – 每一位掌阅用户都会拥有一个个人书架，可以自助添加、删除书目。书、人之间会产生错综复杂的对应关系，而且这种对应数据呈持续变化，MongoDB介入其中进行动态管理。 书评 – 书评是阅读的一项“附加产品”。做书评是用户在掌阅APP上进行的重要交互行为，对每一本书、每一章节、每一句话，都可以上传个人感悟、评价。在平台后端形成了多事务多文档，这也是MongoDB善于处理的数据文档形态。 流水帐单 – 掌阅的流水数据很长一段时间内存在互联网数据中心 (IDC) 内的 HBase 系统 中，由于时间和历史原因，Hbase可维护性、可扩展性已到瓶颈阶段。作为掌阅与阿里云合作的第一个项目，掌阅将所有流水帐单从HBase迁移到MongoDB系统中，完成财务共享中心建设，突破瓶颈，轻松应对流水数据的不断增加。 类积分管理 – 积分机制是掌阅的一种重要“用户管理”方式，但多种积分活动相互交叉，积分规则各不相同，积分数值变化相对琐碎。大量积分数据在平台后端形成单事务多文档，MongoDB能够帮助实现积分管理清晰化、标准化。 客户价值 以数据为驱动 掌阅科技不断提升精细化能力 年报显示，掌阅科技2021年营收为20.7亿元，较上年同期的20.6亿元增长0.49%。其中，数字阅读平台收入占营业收入的比例分别为75.13%。可以说，以数据为驱动，掌阅不断提升精细化能力，运营效率进一步提高。聚焦在阿里云数据库MongoDB版为掌阅带来的重要价值，张博更喜欢用这样的三个词来概括：效率、成本、稳定性。 提高效率 – 在与阿里云和 MongoDB合作过程中，MongoDB技术团队会直接进入项目，并对重点问题全程追踪，通力解决问题，双方达成良性合作，提高工作效率。 加强稳定性 – 数据弹性大是云原生的特色能力。阿里云数据库MongoDB版为掌阅数据带来了稳定的数据弹性支持，尤其针对掌阅不同的运营活动、不同的爆款产品、不同的热点书目，随着数据量波动，MongoDB都能很好地帮助掌阅应对数据库扩容和缩容。 降低成本 – 效率的提高、稳定性的提高都可以直接带来成本降低。据掌阅统计，引入MongoDB系统后，运营成本节省10%以上。 客户证言 掌阅平台研发部负责人 张博 “阿里云数据库MongoDB版为我们提供了一系列操作工具，并作为我们研发平台上的重要组件，帮助我们在统一的、开放的研发环境下，不断提升研发成效。随着互联网行业格局的逐步成型以及数字阅读行业的市场竞争日益激烈，掌阅将与阿里云和MongoDB继续深度合作，变现技术红利，继续深化存量用户精细化运营和加大增量用户贡献。”

December 5, 2023

라이엇게임즈코리아, MongoDB Atlas 기반으로 수십억 건의 전투 게임 경험을 새롭게 정립하다

역동적인 한국 게임 시장의 중심에는 PC방 문화가 있습니다. 몰입감 넘치는 플레이를 함께 즐기는 독특한 문화 덕분에 한국은 글로벌 게임 업계에서 주목하는 시장으로 성장했습니다. 세계적인 게임 개발 및 서비스 기업인 라이엇게임즈는 대표 게임인 리그 오브 레전드(League of Legends)를 필두로 한국 시장에서 강력한 입지를 구축했습니다. 실제 리그 오브 레전드는 국내 PC방 점유율의 40~50%(2023년 9월 기준)를 차지하는 인기 게임인만큼 라이엇게임즈와 PC방 업계는 협력 관계를 공고히 맺고 있습니다. 김동인 라이엇게임즈코리아 소프트웨어 엔지니어는 MongoDB.local Seoul에 참석해, 라이엇게임즈가 한국에서 독보적인 게임 경험을 제공하고 PC방 생태계와 협력을 강화하는 데 있어 MongoDB Atlas가 발휘하는 영향력에 대해 소개했습니다. 아래의 동영상을 통해서도 본 세션에 대한 자세한 내용을 확인하실 수 있습니다. PC방 관리 시스템에 혁신을 가져오다 라이엇게임즈코리아는 게이머들이 PC방에서 게임을 즐기는 시간에 따라 요금을 책정하며, 1시간 동안 진행된 게임 정보는 하나의 세션 데이터로 데이터베이스에 저장됩니다. 라이엇게임즈는 국내 게이머들의 많은 사랑을 받는 인기 게임을 필두로 한국에서 괄목할 만한 성공을 거두면서 동시에 연간 40억 건에 달하는 PC방 게임 세션 데이터를 효과적으로 관리해야 하는 과제를 안게 되었습니다. 기존 외주 시스템으로 PC방 과금 체계를 관리하던 라이엇게임즈코리아는 오과금 문제와 비용 상승로 인한 PC방 업주들의 끊임없는 요구에 대응해야 했으며, 동시에 PC방 게이머들을 위한 맞춤형 이벤트를 선보이는 등 서비스 측면에서 새로운 변화를 준비 중이었습니다. 이에 소규모 엔지니어로 구성된 라이엇게임즈코리아의 개발팀은 직접 새로운 시스템 구축 및 운영에 나섰습니다. 팀의 목표는 폭발적으로 증가하는 데이터를 매끄럽게 처리하고, 잦은 스키마 변경에도 문제없이 변화하는 요구 사항을 충족하기 위해 수평적으로 쉽게 확장할 수 있는 시스템을 구축하고 관리하는 것이었습니다. 관계형 데이터베이스의 한계에 대해 알고 있던 팀은 확장성과 빠른 변화에 대한 적응력을 갖춘 MongoDB와 협업을 진행했습니다. MongoDB Atlas를 통한 데이터 관리 간소화 라이엇게임즈코리아는 먼저 AWS EC2 기반 MongoDB 커뮤니티 버전을 사용해 클러스터를 구성했으며, MongoDB의 도큐먼트 모델로 손쉽게 스키마를 변경할 수 있었습니다. 그러나 서비스 확장에 따라 연간 40억 개의 게임 세션 데이터 도큐먼트가 축적되면서 운영 측면에서 부담이 증가했습니다. AWS EC2의 주기적인 점검과 다운타임 없이 MongoDB 버전 업그레이드를 진행하는 것은 물론 백업 데이터 저장, 서비스 중단, 데이터 손실 없는 서비스 복구 과정 또한 쉽지 않은 작업이었습니다. 이와 함께 내부 이해관계자로부터 게임 유저들에 대한 데이터 분석 및 시각화에 대한 요구도 계속됐습니다. 이에 라이엇게임즈코리아는 MongoDB Korea의 전문적인 가이드와 지원을 통해 AWS 기반 MongoDB Atlas로 마이그레이션을 진행했습니다. Atlas로의 마이그레이션 후 사용자 친화적인 웹 UI를 통해 클러스터 관리를 간소화하고, API를 통한 프로그래밍 방식의 클러스터 생성이 가능해졌습니다. 원활한 마이그레이션을 위해 사용한 Mongomirror 애플리케이션은 동기화 및 테일링(tailing)을 포함해 단 16시간 만에 100억 개의 게임 세션 데이터를 비롯한 도큐먼트를 동기화했습니다. 김동인 엔지니어는 "소프트웨어 백엔드 엔지니어로서 MongoDB Atlas로의 마이그레이션은 만만치 않은 작업이었다. 하지만 코드적으로는 구성에서 DB URI 하나만 수정하면 되었기 때문에 매우 간단하게 진행할 수 있었다"고 설명했습니다. 김동인 라이엇게임즈코리아 소프트웨어 엔지니어 개발자의 생산성 극대화 라이엇게임즈코리아는 특히 MongoDB Atlas로 마이그레이션 후 자동화된 유지·보수 기능으로 상당한 운영 개선 효과를 거두었고, 효율성을 높여 더 나은 게임 경험을 제공하는 데 주력할 수 있었습니다. 김동인 엔지니어는 "MongoDB Atlas를 통해 더 자주, 자동으로 백업을 진행할 수 있어 엔지니어가 새로운 서비스 개발에 집중할 수 있는 환경을 구축할 수 있었다"며 "이제 Query Profiler 메뉴에서 이상 쿼리를 탐지 및 식별하고, 슬로우 쿼리(slow query) 모니터링도 신속하게 진행할 수 있다. 데이터베이스 성능 향상을 위한 개선 사항을 제안하는 Performance Advisor는 인덱스 추천을 통해 효율적인 데이터베이스 관리를 가능케한다"고 전했습니다. 김동인 라이엇게임즈코리아 소프트웨어 엔지니어 이 밖에도 개발 팀은 MongoDB Atlas Charts를 통해 동시 접속자 수, 일일 매출, UV(Unique Visitor) 등 내부 직원들의 다양한 데이터 관련 요청사항에 신속하게 대응할 수 있었습니다. 이러한 인사이트는 곧 서비스 개발을 위한 인사이트 구체화에도 활용됐습니다. 또한 팀은 Atlas의 Online Archive기능을 활용해 자주 액세스하는 데이터와 사용 빈도가 낮은 오래된 데이터를 분리해 저장함으로써 데이터베이스 성능을 최적화하고 운영 부담을 크게 줄일 수 있었습니다. 이를 통해 150억 건에 육박하던 도큐먼트 워크로드가 46억 건으로 줄어들었습니다. 김동인 엔지니어는 "도큐먼트 기반 데이터베이스 중 가장 강력하고 편리한 기능을 제공하는 MongoDB를 통해 개발자의 운영 부담을 줄일 수 있었다"며 "무엇보다도 MongoDB Korea 팀이 보여준 컨설팅, 마이그레이션 및 퍼포먼스 역량과 다양한 요구사항에 대한 상세한 답변은 실무진에게 많은 도움이 됐다"고 강조했습니다. 라이엇게임즈코리아는 앞으로도 MoongoDB와 적극 협업하며 발전을 거듭하는 게임 산업의 밝은 미래를 이끌어갈 예정입니다.

December 5, 2023

Vector Search and Dedicated Search Nodes: Now in General Availability

Today we’re excited to take the next step in adding even more value to the Atlas platform with the general availability (GA) release of both Atlas Vector Search and Search Nodes. Since announcing Atlas Vector Search and dedicated infrastructure with Search Nodes in public preview, we’ve seen continued excitement and demand for additional workloads using vector-optimized search nodes. This new level of scalability and performance ensures workload isolation and the ability to better optimize resources for vector search use cases. Atlas Vector Search allows developers to build intelligent applications powered by semantic search and generative AI over any data type. Atlas Vector Search solves the challenge of providing relevant results even when users don’t know what they’re looking for and uses machine learning models to find results that are similar for almost any type of data. Within just five months of being announced in public preview, Atlas Vector Search has already received the highest developer net promoter score (NPS) — a measure of how likely someone is to recommend a solution to someone else — and is the second most widely used vector database, according to Retool’s State of AI report . There are two key use cases for Atlas Vector Search to build next-gen applications: Semantic search: searching and finding relevant results from unstructured data, based on semantic similarity Retrieval augmented generation (RAG): augment the incredible reasoning capabilities of LLMs with feeds of your own, real-time data to create GenAI apps uniquely tailored to the demands of your business. Atlas Vector Search unlocks the full potential of your data, no matter whether it’s structured or unstructured, taking advantage of the rise in popularity and usage of AI and LLMs to solve critical business challenges. This is possible due to Vector Search being part of the MongoDB Atlas developer data platform, which starts with our flexible document data model and unified API providing one consistent experience. To ensure you unlock the most value possible from Atlas Vector Search, we have cultivated a robust ecosystem of AI integrations, allowing developers to build with their favorite LLMs or frameworks. Our ecosystem of AI integrations for Atlas Vector Search To learn more about Atlas Vector Search, watch our short video or jump right into the tutorial . Atlas Vector Search also takes advantage of our new Search Nodes dedicated architecture, enabling better optimization for the right level of resourcing for specific workload needs. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, allowing you to optimize compute resources and fully scale search needs independent of the database. Search Nodes provide better performance at scale, delivering workload isolation, higher availability, and the ability to better optimize resource usage. In some cases we’ve seen 60% faster query time for some users' workloads, leveraging concurrent querying in Search Nodes. In addition to the compute-heavy search nodes we provided in the public preview, this GA release includes a memory-optimized, low CPU option that is optimal for Vector Search in production. This makes resource contention or the possibility of a resulting service interruption (due to your database and search sharing the same infrastructure previously) a thing of the past. Coupled Architecture (left) compared with the decoupled Search Node architecture (right) We see this as the next evolution of our architecture for both Atlas Search and Vector Search, furthering the value provided by the MongoDB developer data platform. At this time Search Nodes are currently available on AWS single-region clusters (with Google Cloud and Azure coming soon), as customers can continue using shared infrastructure for Google Cloud and Microsoft Azure. Read our initial announcement blog post to view the steps of how to turn on Search Nodes today, or jump right into the tutorial . Both of these features are available today for production usage. We can’t wait to see what you build, and please reach out to us with any questions.

December 4, 2023

How Canara HSBC Life Insurance Optimized Costs and Claims Processing with MongoDB

Since 2008, Canara HSBC Life Insurance has focused relentlessly on bringing a fresh perspective to an industry known more for stability and conservatism rather than innovation. Since its inception in 2008 as a joint venture between Canara Bank and HSBC Insurance, Canara HSBC Life Insurance has strived to differentiate itself from the competition through enhanced customer interactions, launching cutting-edge digital products, and integrating digital services that cater to the evolving needs of customers. For the past six years Chief Operating Officer, Mr. Sachin Dutta, has been on a mission to bring this customer-first mindset to the digital products and touchpoints his team creates. Speaking at MongoDB’s annual .local developer conference in Delhi, Dutta outlined Canara HSBC Life Insurance’s ongoing digital transformation journey, and how his team's focus on customer success and business efficiency led them to work with MongoDB for improved efficiencies and results. “I truly value the partnership we have with MongoDB. We are building a future-ready organization, and this partnership clearly helps us achieve our aim of reaching the last mile possible in customer servicing. Mr. Sachin Dutta, Chief Operating Officer, Canara HSBC Modernizing the architecture and driving developer efficiency Canara HSBC’s digital transformation was centered on three technical pillars: the cloud, analytics, and mobility. The company focused on creating a more integrated organization and automating manual processes within the system. “We try to remove human intervention with a life insurance policy delivered in seconds and claims that are settled virtually in seconds,” Dutta says. To get there, Canara HSBC Life Insurance had to move on from its existing architecture, which required multifaceted changes and several new implementations: Monolithic applications made alterations a time-consuming process A reliance on rigid relational databases prolonged development timelines, forcing developers to spend time wrangling data when they could be building better products for customers. The fully on-premises system had supported the organization in the past but required future-proofing to support growth and deliver a better customer experience. Because of this valuable development time and money were spent managing, patching, and scaling databases, rather than getting new products into the hands of customers. These technical issues impacted the speed of business, particularly during month-end and year-end data processing, when the volumes were high. In addition, batch processing stood in the way of creating the real-time availability of information customers wanted. Dutta and his senior team also realized that their existing infrastructure would make it more challenging to find the right talent in the market, as the existing infrastructure was increasingly becoming outdated. Dutta realized early on that, in order for Canara HSBC to attract and retain the best and brightest developers, the insurer had to offer the chance to work with the latest technologies. Platforms like MongoDB would be integral to this effort. “I want to create an organization that is attracting talent and where people start to enjoy their work, and that benefit then gets passed on to the customers, ” Mr. Dutta says. Looking to overhaul its existing infrastructure, Canara HSBC Life Insurance wanted to move fast and hire the talent required to best serve its end customers. Dutta summarized the situation succinctly: "We found that some of those relational structures that had worked for us would not take us through the next 10 years.” Migrating to a secure, fully managed database platform After evaluating the solutions on the market, the team decided to transition from their existing on-premises relational databases, like IBM DB2, MySQL, and Postgres, to MongoDB Atlas . In the last six years of my work, I’m pleased to say that MongoDB has seamlessly integrated all the processes in the backend. We migrated from a completely legacy-based setup to the new fully managed MongoDB service to enhance IT productivity Mr. Sachin Dutta The first stage of the journey was moving from monolithic applications and relational databases to a microservices architecture. With its flexible schema and capabilities for redundancy, automation, and scalability, MongoDB served as the best partner to help facilitate the transition. Next, the team moved to modernize key parts of the business, such as underwriting, freeing their data to power more automation in straight-through processing (STP) of policies and faster claims processing. The adoption of a hybrid cloud model shifted Canara HSBC Life Insurance away from on-premises databases to MongoDB Atlas. As a fully managed cloud database, MongoDB Atlas solves issues related to scalability, database management, and overall reliability. MongoDB Atlas is also cloud agnostic, giving the insurance company an option to work with Azure, AWS, and Google Cloud. Mongo Atlas’ BI Connector bridged the gap between MongoDB and traditional BI tools. This seamless integration allowed Canara HSBC Life Insurance to deploy its preferred reporting tools and, when coupled with MongoDB Atlas’ real-time analytics capability, made batch processing a thing of the past. Halving delivery times and driving business efficiencies Moving to MongoDB Atlas has had a profound impact on the breadth of digital experiences Canara HSBC Life Insurance can offer customers and the speed at which new products can be developed. Something that used to take months, with the implementation of our new tools could be completed in a couple of weeks or days Mr. Sachin Dutta And it’s not only the customer experience and product delivery that has benefited from the partnership. Canara HSBC Life Insurance has also realized substantial efficiency gains and savings as a result of working with MongoDB. We are leveraging artificial intelligence as a core capability to predict human behavior and auto-underwrite policies wherein around half of the policies issued today are issued by the system Mr. Sachin Dutta Highlighted results include: Straight-through processing (STP) surged from 37% to an impressive 60%. This is set to increase further with AI/ML integrations and rule suggestions. Policy issuance turnaround time improved by 60%. Efficiency in operations led to a 20% cost-saving per policy issuance. Canara HSBC experienced 2x top-line growth due to seamless integration with analytical tools. Looking ahead, Canara HSBC Life Insurance has already outlined three key areas where the MongoDB partnership will grow. First, Dutta wants to take advantage of MongoDB Atlas’ flexible document data model to collect and organize data on customers from across the business, making MongoDB Atlas the sole database at Canara HSBC Life Insurance and creating a true customer 360 data layer to power sophisticated data analytics. In financial services, this capability is referred to as know your customer (KYC). “We want to build a data layer that provides a unique experience to the customer after getting to know them,” he says. “That’ll help the company generate better NPS scores and retain customers.” Second, the adoption and integration of AI and machine learning tools also factor heavily into future plans. MongoDB Atlas, with its flexible schema, compatibility with various machine learning platforms, and AI-specific features — such as Vector Search and storage — is a good fit for the company. In Dutta's words, "We are going to scale up and capture the GenAI space.” Last, Dutta wants to take advantage of the MongoDB Atlas SQL interface, connectors, and drivers to augment business intelligence for reporting and precise SQL-based report conversions. Learn More about how MongoDB Works with global Insurers

December 4, 2023

MongoDB Doubles Down on Aotearoa as Part of Continued APAC Expansion

MongoDB is expanding its business in New Zealand to help Kiwi organisations build modern applications and take advantage of the AI opportunity that exists today. With hundreds of customers already in Aotearoa, including Pathfinder, Rapido, and Tourism Holdings, we're continuing to hire and invest to continue to grow our community in the country. Powering the next generation of modern applications Interest and excitement in AI, and particularly generative AI, has exploded. With a proud history of Innovation, it's not a surprise that many New Zealand companies are early adopters of this incredible technology. In fact, an AI Forum report has revealed that AI has the potential to increase New Zealand's GDP by as much as $54 billion by 2035. No matter what you think of the veracity of those bold predictions, one thing is sure: Almost every company is trying to figure out how to take advantage of data and software, to help them build better products, more efficiently and more quickly. Jake McInteer speaking at MongoDB.local Auckland As organisations transform into digital-first businesses, they’re faced with a growing list of application and data requirements. Modern applications are complex – they need to handle transactional workloads, app-driven analytics, full-text search, AI-enhanced experiences, stream data processing, and more. Companies are being asked to do this all while reducing data infrastructure sprawl, complexity and often also cut costs. What we are seeing globally is our developer data platform solves this challenge and complexity since it integrates all of the data services organisations need to build modern applications in a unified developer experience. Additionally, we also allow our customers to easily run anywhere in the world with over 110+ locations making us uniquely placed to enable Kiwi companies to adapt to a multicloud future. We also have strong local partnerships with all three cloud hyperscalers, all of which plan to open new cloud regions in New Zealand in the coming years. With the support of our cloud partners, in New Zealand we've already seen great adoption of MongoDB Atlas, including the largest established enterprises, through to cutting-edge startups. Here are a couple of examples. Pathfinder: Protecting vulnerable children Pathfinder , headquartered in Auckland, is a global leader in software development specialising in protecting vulnerable children. The company's mission centres on empowering law enforcement agencies with state-of-the-art technology, meticulously designed to combat the reprehensible crime of child exploitation. "We are committed to delivering investigators the most advanced tools. We cannot accept delays in removing a child from harm due to investigations being overwhelmed by large amounts of disparate data. In situations where every minute impacts a child's well-being, these tools must enable investigators to swiftly navigate data challenges, and rapidly apprehend perpetrators" said Bree Atkinson, CEO of Pathfinder Labs. Pathfinder’s Paradigm service is being built on MongoDB Atlas, running on AWS, and takes advantage of the wider developer data platform features in order to enable the next generation of data-driven investigative capabilities. By using MongoDB Atlas Vector Search , a native part of the MongoDB Atlas platform, the Pathfinder team are also able to match images and details within images (such as people and objects), classify documents and text, and build better search experiences for their users via semantic search. This enables Paradigm to efficiently aid law enforcement in identifying victims and apprehending offenders. Bree Atkinson, CEO of Pathfinder Labs, and Peter Pilly, DevOps Architect at Pathfinder Labs, with the MongoDB team in Auckland at the recent .local event "MongoDB Atlas allows our team to focus on our strengths: developing outstanding technology. It works with us not against us, enhancing integration which enables us to build better user experiences," said Peter Pilley, DevOps Architect at Pathfinder Labs. "Take MongoDB Atlas Vector Search, for example. Before MongoDB, we would have needed to incorporate multiple tools to achieve that functionality. Now we can handle it all from a single platform removing complexity and architecture that wasn't needed. With MongoDB Atlas, we're able to make data-driven decisions swiftly, boosting our productivity and decision-making speed." Peter's team at Pathfinder also uses MongoDB's performance advisor. They say it's like having an extra team member who suggests the best indexes for accessing their data, which is critical in an industry where getting to a specific piece of data could make all the difference. Rapido: Optimising B2B revenue and distribution Rapido has been utilising MongoDB Atlas for over five years. The team was originally part of MongoDB for Startups , a programme that offers startups free credits and technical advice to help them build faster and scale further. Their eagerness to adopt new technologies has enabled them to effectively harness MongoDB Atlas's evolving features. Working with the Accredo ERP system, Rapido has harnessed MongoDB Atlas to innovate in business-to-business (B2B) transactions. Using features like MongoDB Atlas Vector Search, the ' moreLikeThis ' operator, and MongoDB App Services, they've transformed business interactions, offering precise product recommendations and improved real-time visibility via change streams. Rapido's platform, which has processed orders collectively worth more than $100m to date, is essential for many wholesale businesses in New Zealand. Adam Holt, CEO of Rapido, summarises their experience: "Our journey with MongoDB Atlas has been transformative. By building on a cohesive developer data platform, we don't need to bolt-on and learn special technologies for every requirement. Continuously integrating new features keeps our platform advanced in the fast-paced B2B market. It's about leveraging technology to innovate and deliver better solutions to our clients." MongoDB expands in Aotearoa The increased demand from Kiwi organisations who are looking to innovate faster and take advantage of cutting-edge technologies, like AI, means MongoDB is now doubling down on its New Zealand footprint. Earlier this month, MongoDB established its local operations in Aotearoa, New Zealand. Jake McInteer , a native Kiwi, has officially transferred from MongoDB’s Australia business to lead the organisation in New Zealand. MongoDB already has a large, engaged community, more than 200 customers, and an extensive partner network. CEO of Lumin Max Ferguson presents at the Christchurch MongoDB user group We are incredibly excited about the opportunity to invest in and contribute to the Kiwi tech ecosystem, both to support local companies and help kiwi startups like Lumin and Marsello as well as established companies like Tourism Holdings , Figured , and Foster Moore . To support our growth, we have roles open on our Sales and Solutions Architecture team. If you are based in NZ and interested in joining our incredible team, working in our hybrid environment, please check out and apply for the roles here: Enterprise Account Executive, Acquisition Senior Solutions Architect Additionally, read here about the massive opportunity at MongoDB in APAC from our SVP Simon Eid.

November 30, 2023

Atlas Vector Search obtient le meilleur taux de satisfaction des développeurs dans l'enquête Retool State of AI 2023

Retool vient de publier son tout premier rapport sur l'état de l'IA , qui vaut la peine d'être lu. Inspirée du rapport State of Internal Tools, très populaire, l'enquête State of AI a pris le pouls de plus de 1 500 techniciens issus de divers secteurs d'activité : ingénieurs logiciels, dirigeants, chefs de produit, concepteurs, etc. L'objectif de l'enquête est de comprendre comment ces passionnés de technologie utilisent et construisent avec l'intelligence artificielle (IA). Dans le cadre de l'enquête, Retool a étudié les outils les plus populaires, notamment les bases de données vectorielles les plus fréquemment utilisées dans le domaine de l'IA. L'enquête a révélé que MongoDB Atlas Vector Search affichait le meilleur score de promotion (NPS) sur .NET et était la deuxième base de données vectorielles la plus utilisée, cinq mois seulement après sa mise en service. Il devance ainsi les solutions concurrentes qui existent depuis des années. Dans cet article de blog, nous examinerons l'essor phénoménal des bases de données vectorielles et la façon dont les développeurs utilisent des solutions telles qu'Atlas Vector Search pour créer des applications alimentées par l'IA. Nous aborderons également d'autres points essentiels du rapport Retool. Consultez notre page de ressources sur l'IA pour en savoir plus sur la création d'applications alimentées par l'IA avec MongoDB. Adoption d'une base de données vectorielle : Sur le site Charts (enfin presque...) De curiosité mathématique à superpuissance derrière l'IA générative et les LLM, les vector embeddings et les bases de données qui les managed ont parcouru un long chemin en très peu de temps. Consultez les tendances de DB-moteur dans les modèles de bases de données au cours des 12 derniers mois et vous verrez que les bases de données vectorielles dépassent de loin toutes les autres en termes de popularité. Il suffit de regarder la trajectoire "vers le haut et vers la droite" de la ligne rose dans le site Charts ci-dessous. Capture d'écran avec l'aimable autorisation de DB-moteur, 8 novembre 2023 Mais pourquoi les bases de données vectorielles sont-elles devenues si populaires ? Ils constituent un élément clé d'un nouveau modèle architectural appelé "génération augmentée par la recherche" ( RAG ). Il s'agit d'un mélange puissant qui combine les capacités de raisonnement de LLM préformés et polyvalents et les alimente en temps réel avec des données spécifiques à l'entreprise. Les résultats sont des applications alimentées par l'IA qui servent de manière unique l'entreprise - qu'il s'agisse de créer de nouveaux produits, de réimaginer l'expérience client ou de porter la productivité et l'efficacité internes à des niveaux sans précédent. Les encastrements vectoriels sont l'un des composants fondamentaux nécessaires pour libérer la puissance de RAG. Les modèles d'intégration vectorielle codent les données de l'entreprise, qu'il s'agisse de texte, de code, de vidéo, d'images, de flux audio ou de tableaux, sous forme de vecteurs. Ces vecteurs sont ensuite stockés, indexés et interrogés dans une base de données vectorielles ou un moteur de recherche vectoriel, fournissant les données d'entrée pertinentes en tant que contexte pour le LLM choisi. Il en résulte des applications d'IA fondées sur les données et les connaissances de l'entreprise qui sont pertinentes pour l'activité, exactes, fiables et à jour. Comme le montre l'enquête Retool, le paysage des bases de données vectorielles est encore largement vierge. Moins de 20 % des personnes interrogées utilisent des bases de données vectorielles aujourd'hui, mais avec la tendance croissante à la personnalisation des modèles et de l'infrastructure d'IA, l'adoption est assurée de croître. Pourquoi les développeurs adoptent-ils Atlas Vector Search ? L'étude de Retool sur l'état de l'IA présente quelques grandes bases de données vectorielles qui ont ouvert la voie au cours des deux dernières années, en particulier pour les applications nécessitant une recherche sémantique contextuelle. Pensez aux catalogues de produits ou à la découverte de contenu. Cependant, le défi auquel les développeurs sont confrontés lorsqu'ils utilisent ces bases de données vectorielles est qu'ils doivent les intégrer avec d'autres bases de données dans la pile technologique de leur application. Chaque couche de base de données supplémentaire dans la pile technologique de l'application ajoute encore une autre source de complexité, de latence et de frais généraux opérationnels. Cela signifie qu'ils doivent se procurer une autre base de données, l'apprendre, l'intégrer (pour le développement, les tests et la production), la sécuriser et la certifier, la répartir, la surveiller et la sauvegarder, tout en maintenant la synchronisation des données entre ces multiples systèmes. MongoDB adopte une approche différente qui évite totalement ces problèmes : Les développeurs stockent et recherchent les encastrements vectoriels natifs dans le même système que celui qu'ils utilisent comme base de données opérationnelle. Grâce à l'architecture distribuée de MongoDB, ils peuvent isoler ces différentes charges de travail tout en gardant les données entièrement synchronisées. Search Nodes fournissent un calcul dédié et une isolation de la charge de travail, ce qui est vital pour les charges de travail de recherche vectorielle à forte intensité de mémoire, permettant ainsi d'améliorer les performances et la disponibilité. Grâce au schéma de document flexible et dynamique de MongoDB, les développeurs peuvent modéliser et faire évoluer les relations entre les vecteurs, les métadonnées et les données d'application comme d'autres bases de données ne peuvent le faire. Ils peuvent traiter et filtrer les données vectorielles et opérationnelles selon les besoins de l'application grâce à une API d'interrogation expressive et à des pilotes qui prennent en charge tous les langages de programmation les plus courants. L'utilisation de la plateforme de données de développement MongoDB Atlas entièrement gérée permet aux développeurs d'obtenir le répartir, la sécurité et la performance que les utilisateurs de leurs applications attendent. Que signifie cette approche unifiée MEAN pour les développeurs ? Cycles de développement plus rapides, application plus performante offrant une latence plus faible avec des données plus fraîches, associée à une réduction des frais généraux et des coûts d'exploitation. Des résultats qui se reflètent dans le score NPS de MongoDB, le meilleur de sa catégorie. Atlas Vector Search est robuste, rentable et extrêmement rapide ! Saravana Kumar, CEO, Kovai parle du développement de l'assistant d'intelligence artificielle de son entreprise Consultez notre série Construire l'IA avec MongoDB blog (rendez-vous dans la section Prise en main pour consulter les anciens numéros). Ici, vous verrez Atlas Vector Search utilisé pour des applications alimentées par GenAI couvrant l'IA conversationnelle avec des chatbots et des voicebots, des co-pilotes, l'intelligence des menaces et la cybersécurité, la gestion des contrats, la réponse aux questions, la conformité des soins de santé et les assistants de traitement, la découverte et la monétisation de contenu, et bien d'autres choses encore. MongoDB stockait déjà des métadonnées sur les artefacts dans notre système. Avec l'introduction d'Atlas Vector Search, nous disposons désormais d'une base de données vectorielles complète qui a été testée pendant plus d'une décennie et qui répond à nos besoins en matière de recherche dense. Il n'est pas nécessaire de déployer une nouvelle base de données que nous aurions dû managed et apprendre. Nos vecteurs et les métadonnées des artefacts peuvent être stockés les uns à côté des autres. Pierce Lamb, ingénieur logiciel principal au sein de l'équipe "Données et apprentissage automatique" de VISO TRUST Que peut-on apprendre sur l'état de l'IA à partir du rapport Retool ? Au-delà de la découverte des bases de données vectorielles les plus populaires, l'enquête aborde l'IA sous différents angles. Elle commence par explorer les perceptions de l'IA par les personnes interrogées. (Il n'est pas surprenant de constater que les dirigeants sont plus optimistes que les collaborateurs individuels). Il explore ensuite les priorités d'investissement, l'impact de l'IA sur les perspectives d'emploi futures et la manière dont elle affectera probablement les développeurs et les compétences dont ils auront besoin à l'avenir. L'enquête explore ensuite le niveau d'adoption et de maturité de l'IA. Plus de 75 % des répondants à l'enquête déclarent que leur entreprise s'efforce de commencer à utiliser l'IA, mais environ la moitié d'entre eux déclarent qu'il s'agit encore de projets précoces, principalement axés sur des applications internes. L'enquête se penche ensuite sur la nature de ces applications et sur l'utilité que les personnes interrogées leur prêtent pour l'entreprise. Elle constate que presque tout le monde utilise l'IA au travail, que cela soit autorisé ou non, et identifie ensuite les principaux points problématiques. Il n'est pas surprenant que la précision des modèles, la sécurité et les hallucinations figurent en tête de liste. L'étude se termine par l'examen des principaux modèles utilisés. Encore une fois, il n'est pas surprenant que les offres d'Open AI ouvrent la voie, mais cela indique également une intention croissante d'utiliser des modèles open source avec l'infrastructure et les outils d'IA pour la personnalisation à l'avenir. Vous pouvez approfondir tous les détails de l'enquête en lisant le rapport . Prise en main avec Atlas Vector Search Vous souhaitez découvrir notre offre de recherche vectorielle ? Rendez-vous sur notre page produit Atlas Vector Search . Vous y trouverez des liens vers des tutoriels, de la documentation et des intégrations clés de l'écosystème de l'IA afin que vous puissiez vous plonger directement dans la construction de votre propre application alimentée par genAI . Si vous souhaitez en savoir plus sur les possibilités de haut niveau de la recherche vectorielle, téléchargez notre livre blanc sur l'intégration de l'IA générative .

November 30, 2023

Atlas Vector Search erzielt den höchsten Entwickler-NPS in der Retool State of AI 2023-Umfrage

Retool hat gerade seinen allerersten State of AI-Bericht veröffentlicht und es lohnt sich, ihn zu lesen. Basierend auf dem äußerst beliebten Bericht „State of Internal Tools“ erfasste die „State of AI“-Umfrage den Puls von über 1.500 Technikleuten aus den Bereichen Softwareentwicklung, Führung, Produktmanager, Designer und mehr aus verschiedenen Branchen. Der Zweck der Umfrage besteht darin, zu verstehen, wie diese Technikleute künstliche Intelligenz (KI) nutzen und damit bauen. Im Rahmen der Umfrage untersuchte Retool, welche Tools beliebt sind, einschließlich der Vektordatenbanken, die am häufigsten mit KI verwendet werden. Die Umfrage ergab, dass MongoDB Atlas Vector Search den höchsten .NET Promoter Score (NPS) erzielte und die am zweithäufigsten genutzte Vektordatenbank war – und das innerhalb von nur fünf Monaten nach ihrer Veröffentlichung. Damit liegt es vor konkurrierenden Lösungen, die es schon seit Jahren gibt. In diesem Blog untersuchen wir den phänomenalen Aufstieg von Vektordatenbanken und wie Entwickler Lösungen wie Atlas Vector Search nutzen, um KI-gestützte Anwendungen zu erstellen. Wir werden auch andere wichtige Highlights aus dem Retool-Bericht behandeln. Schauen Sie sich unsere KI-Ressourcenseite an, um mehr über die Erstellung KI-gestützter App mit MongoDB zu erfahren. Einführung von Vektordatenbanken: Aus den Charts (naja, fast ...) Von mathematischer Neugier bis zur Superkraft hinter generativer KI und LLMs haben Vektoreinbettungen und die Datenbanken, die sie managed , in sehr kurzer Zeit einen langen Weg zurückgelegt. Schauen Sie sich die DB-Engine Trends bei Datenbankmodellen in den letzten 12 Monaten an und Sie werden sehen, dass Vektordatenbanken bei der Beliebtheitsänderung alle anderen um Längen übertreffen. Schauen Sie sich einfach die Flugbahn der rosa Linie „nach oben und rechts“ in den Charts unten an. Screenshot mit freundlicher Genehmigung von DB-Engine, 8. November 2023 Aber warum sind Vektordatenbanken so beliebt geworden? Sie sind eine Schlüsselkomponente in einem neuen Architekturmuster namens Retrieval-Augmented Generation – auch bekannt als RAG – einer leistungsstarken Mischung, die die Argumentationsfähigkeiten vorab trainierter Allzweck-LLMs kombiniert und ihnen unternehmensspezifische Echtzeitdaten zuführt. Das Ergebnis sind KI-gestützte App , die dem Unternehmen auf einzigartige Weise dienen – sei es bei der Entwicklung neuer Produkte, der Neugestaltung der Kundenerfahrung oder der Steigerung der internen Produktivität und Effizienz auf beispiellose Höhen. Vektoreinbettungen sind eine der grundlegenden Komponenten, die erforderlich sind, um die Leistungsfähigkeit von RAG freizusetzen. Vektor-Einbettungsmodelle kodieren Unternehmensdaten, egal ob Text, Code, Video, Bilder, Audio- Stream oder Tabellen, als Vektoren. Diese Vektoren werden dann gespeichert, Index und in einer Vektordatenbank oder Engine abgefragt, wodurch die relevanten Eingabedaten als Kontext für das ausgewählte LLM bereitgestellt werden. Das Ergebnis sind KI- App , die auf Unternehmensdaten und -wissen basieren, die für das Unternehmen relevant, genau, vertrauenswürdig und aktuell sind. Wie die Retool-Umfrage zeigt, ist die Vektordatenbanklandschaft noch weitgehend auf der grünen Wiese. Weniger als 20 % der Befragten nutzen heute Vektordatenbanken, aber mit dem wachsenden Trend zur individuellen Anpassung von Modellen und KI-Infrastruktur wird die Akzeptanz garantiert zunehmen. Warum übernehmen Entwickler Atlas Vector Search? Die State of AI-Umfrage von Retool enthält einige großartige Vektordatenbanken, die in den letzten Jahren eine Vorreiterrolle gespielt haben, insbesondere bei Anwendungen, die eine kontextbewusste semantische Suche erfordern. Denken Sie an Produktkataloge oder Content Discovery. Die Herausforderung für Entwickler bei der Verwendung dieser Vektordatenbanken besteht jedoch darin, dass sie sie zusammen mit anderen Datenbanken in den Tech-Stack ihrer Anwendung integrieren müssen. Jede zusätzliche Datenbankschicht im Anwendungstechnologie-Stack fügt eine weitere Quelle für Komplexität, Latenz und betrieblichen Overhead hinzu. Das bedeutet, dass sie über eine weitere Datenbank verfügen, die sie beschaffen, erlernen, integrieren (für Entwicklung, Tests und Produktion), sichern und zertifizieren, skalieren, überwachen und sichern müssen – und das alles, während die Daten über diese mehreren Systeme hinweg synchron bleiben. MongoDB verfolgt einen anderen Ansatz, der diese Herausforderungen vollständig vermeidet: Entwickler speichern und durchsuchen native Vektoreinbettungen in demselben System, das sie als Betriebsdatenbank verwenden. Mithilfe der verteilten Architektur von MongoDB können sie diese verschiedenen Arbeitslasten isolieren und gleichzeitig die Daten vollständig synchronisieren. Search Nodes bieten dedizierte Rechen- und Workload- Isolation , die für speicherintensive Vektorsuch-Workloads von entscheidender Bedeutung ist, und ermöglichen so eine verbesserte Leistung und höhere Verfügbarkeit Mit dem flexiblen und dynamischen Schema von MongoDB können Entwickler Beziehungen zwischen Vektoren, Metadaten und Anwendungsdaten auf eine Weise modellieren und weiterentwickeln, die andere Datenbanken nicht können. Mit einer ausdrucksstarken Abfrage-API und Treibern, die alle gängigen Programmiersprachen unterstützen, können sie Vektor- und Betriebsdaten auf jede von der Anwendung benötigte Weise verarbeiten und filtern. Mithilfe der vollständig verwalteten Entwicklerdatenplattform MongoDB Atlas können Entwickler die Skalierbarkeit, Sicherheit und Leistung erreichen, die ihre Anwendungsbenutzer erwarten. Was bedeutet dieser MEAN Ansatz für Entwickler? Schnellere Entwicklungszyklen, eine leistungsstärkere App mit geringerer Latenz und aktuelleren Daten, gepaart mit geringerem Overhead und geringeren Kosten. Ergebnisse, die sich im erstklassigen NPS-Score von MongoDB widerspiegeln. Atlas Vector Search ist robust, kostengünstig und unglaublich schnell! Saravana Kumar, CEO, Kovai spricht über die Entwicklung des KI-Assistenten seines Unternehmens Schauen Sie sich unsere Blog Reihe „ Building AI with MongoDB “ an (gehen Sie zum Abschnitt „Erste Schritte“, um frühere Ausgaben zu sehen). Hier sehen Sie, wie Atlas Vector Search für GenAI-basierte Anwendungen verwendet wird, die Konversations-KI mit Chatbots und Voicebots, Co-Piloten, Bedrohungsinformationen und Cybersicherheit, Vertragsmanagement, Fragebeantwortung, Compliance- und Behandlungsassistenten im Gesundheitswesen, Inhaltserkennung und Monetarisierung usw. umfassen mehr. MongoDB speicherte bereits Metadaten zu Artefakten in unserem System. Mit der Einführung von Atlas Vector Search verfügen wir nun über eine umfassende Vektor-Metadaten-Datenbank, die sich über ein Jahrzehnt im Kampf bewährt hat und unsere Anforderungen an die Suche nach dichten Daten erfüllt. Es ist nicht erforderlich, eine neue Datenbank bereitzustellen, die wir managed und erlernen müssten. Unsere Vektoren und Artefaktmetadaten können direkt nebeneinander gespeichert werden. Pierce Lamb, Senior Software Engineer im Team für Daten und maschinelles Lernen bei VISO TRUST Was können Sie aus dem Retool-Bericht über den Stand der KI lernen? Die Umfrage deckt nicht nur die beliebtesten Vektordatenbanken auf, sondern befasst sich auch mit KI aus verschiedenen Perspektiven. Zunächst wird untersucht, wie die Befragten KI wahrnehmen. (Es überrascht nicht, dass die C-Suite optimistischer ist als einzelne Mitwirkende.) Anschließend werden Investitionsprioritäten, die Auswirkungen von KI auf zukünftige Berufsaussichten und die voraussichtlichen Auswirkungen auf Entwickler und die von ihnen in Zukunft benötigten Fähigkeiten untersucht. Anschließend untersucht die Umfrage den Grad der KI-Einführung und -Reife. Über 75 % der Umfrageteilnehmer geben an, dass ihre Unternehmen Anstrengungen unternehmen, um mit KI zu beginnen. Etwa die Hälfte gab an, dass es sich hierbei noch um frühe Projekte handelte, die hauptsächlich auf interne Anwendungen ausgerichtet waren. In der Umfrage wird anschließend untersucht, um welche Anwendungen es sich handelt und welchen Nutzen die Befragten für das Unternehmen davon halten. Es stellt fest, dass fast jeder KI bei der Arbeit einsetzt, unabhängig davon, ob es ihm erlaubt ist oder nicht, und identifiziert dann die größten Schwachstellen. Es ist keine Überraschung, dass Modellgenauigkeit, Sicherheit und Halluzinationen ganz oben auf dieser Liste stehen. Den Abschluss der Umfrage bildet die Untersuchung der im Einsatz befindlichen Top-Modelle. Auch hier ist es keine Überraschung, dass die Angebote von Open AI wegweisend sind, aber es deutet auch auf eine wachsende Absicht hin, in Zukunft Open Source Modelle zusammen mit KI-Infrastruktur und Tools für individuelle Anpassungen zu nutzen. Sie können sich alle Einzelheiten der Umfrage ansehen, indem Sie den Bericht lesen . Erste Schritte mit Atlas Vector Search Möchten Sie einen Blick auf unser Vektorsuchangebot werfen? Besuchen Sie unsere Atlas Vector Search-Produktseite . Dort finden Sie Links zu Tutorials, Dokumentationen und wichtigen AI Umgebung-Integrationen, sodass Sie direkt mit der Entwicklung Ihrer eigenen GenAI-basierten App beginnen können . Wenn Sie mehr über die umfassenden Möglichkeiten der Vektorsuche erfahren möchten, laden Sie unser Whitepaper zum Einbetten generativer KI herunter .

November 30, 2023

Atlas Vector Search comanda o NPS de desenvolvedor mais alto na pesquisa Retool State of AI 2023

A Retool acaba de publicar seu primeiro relatório sobre o estado da IA e vale a pena lê-lo. Com base no relatório extremamente popular sobre o estado das ferramentas internas, a pesquisa sobre o estado da IA mediu o pulso de mais de 1.500 profissionais de tecnologia, abrangendo engenharia de software, liderança, gerentes de produto, designers e muito mais, provenientes de uma variedade de setores. O objetivo da pesquisa é entender como esse pessoal da tecnologia usa e constrói com inteligência artificial (IA). Como parte da pesquisa, a Retool investigou quais ferramentas eram populares, incluindo os bancos de dados vetoriais usados com mais frequência com IA. A pesquisa descobriu que o MongoDB Atlas Vector Search comandou o .NET Promoter Score (NPS) mais alto e foi o segundo banco de dados de vetores mais usado - apenas cinco meses após seu lançamento. Isso o coloca à frente de soluções concorrentes que já existem há anos. Nesta postagem do blog, examinaremos o aumento fenomenal dos bancos de dados vetoriais e como os desenvolvedores estão usando soluções como o Atlas Vector Search para criar aplicativos com tecnologia de IA. Também abordaremos outros destaques importantes do relatório Retool. Confira nossa página de recursos de IA para saber mais sobre como criar aplicativos baseados em IA com MongoDB. Adoção de banco de dados vetorial: fora do Charts (bem, quase...) Da curiosidade matemática ao superpoder por trás da IA generativa e dos LLMs, os embeddings de vetores e os bancos de dados que os managed percorreram um longo caminho em muito pouco tempo. Confira as tendências dos DB-Engines em modelos de banco de dados nos últimos 12 meses e você verá que os bancos de dados vetoriais estão muito acima de todos os outros em termos de mudança de popularidade. Basta observar a trajetória “para cima e para a direita” da linha rosa nos Charts abaixo. Captura de tela cortesia de DB-engines, 8 de novembro de 2023 Mas por que os bancos de dados vetoriais se tornaram tão populares? Eles são um componente-chave em um novo padrão de arquitetura chamado geração aumentada de recuperação – também conhecido como RAG – uma combinação potente que combina os recursos de raciocínio de LLMs de uso geral pré-treinados e os alimenta com dados específicos da empresa em tempo real. Os resultados são aplicativos baseados em IA que atendem exclusivamente aos negócios, seja criando novos produtos, reimaginando as experiências do cliente ou elevando a produtividade e a eficiência internas a níveis sem precedentes. Os embeddings vetoriais são um dos componentes fundamentais necessários para desbloquear o poder do RAG. Os modelos de incorporação de vetores codificam dados corporativos, sejam eles texto, código, vídeo, imagens, fluxos de áudio ou tabelas, como vetores. Esses vetores são então armazenados, indexados e consultados em um banco de dados de vetores ou mecanismo de busca de vetores, fornecendo os dados de entrada relevantes como contexto para o LLM escolhido. O resultado são aplicativos de IA baseados em dados e conhecimentos corporativos relevantes para os negócios, precisos, confiáveis e atualizados. Como mostra a pesquisa Retool, o cenário dos bancos de dados vetoriais ainda é em grande parte inexplorado. Atualmente, menos de 20% dos entrevistados usam bancos de dados vetoriais, mas com a tendência crescente de personalização de modelos e infraestrutura de IA, a adoção certamente crescerá. Por que os desenvolvedores estão adotando o Atlas Vector Search? A pesquisa State of AI da Retool apresenta alguns excelentes bancos de dados vetoriais que abriram caminho nos últimos dois anos, especialmente em aplicações que exigem pesquisa semântica sensível ao contexto. Pense em catálogos de produtos ou descoberta de conteúdo. No entanto, o desafio que os desenvolvedores enfrentam ao usar esses bancos de dados vetoriais é que eles precisam integrá-los a outros bancos de dados na pilha de tecnologia de seus aplicativos. Cada camada adicional de banco de dados na pilha de tecnologia de aplicativos adiciona outra fonte de complexidade, latência e sobrecarga operacional. Isso significa que eles têm outro banco de dados para adquirir, aprender, integrar (para desenvolvimento, teste e produção), proteger e certificar, dimensionar, monitorar e fazer backup, e tudo isso enquanto mantêm os dados sincronizados entre esses vários sistemas. O MongoDB adota uma abordagem diferente que evita totalmente esses desafios: Os desenvolvedores armazenam e pesquisam incorporações de vetores nativos no mesmo sistema que usam como banco de dados operacional. Usando a arquitetura distribuída do MongoDB, eles podem isolar essas diferentes cargas de trabalho enquanto mantêm os dados totalmente sincronizados. Os nós de pesquisa fornecem computação dedicada e isolamento de carga de trabalho que é vital para cargas de trabalho de pesquisa vetorial com uso intensivo de memória, permitindo assim melhor desempenho e maior disponibilidade Com o esquema de documento flexível e dinâmico do MongoDB, os desenvolvedores podem modelar e desenvolver relacionamentos entre vetores, metadados e dados de aplicativos de uma forma que outros bancos de dados não conseguem. Eles podem processar e filtrar dados vetoriais e operacionais da maneira que o aplicativo precisar, com uma API de consulta expressiva e drivers que suportam todas as linguagens de programação mais populares. O uso da plataforma de dados de desenvolvedor MongoDB Atlas totalmente managed permite que os desenvolvedores alcancem a escala, a segurança e o desempenho que os usuários de seus aplicativos esperam. O que essa abordagem unificada MEAN para os desenvolvedores? Ciclos de desenvolvimento mais rápidos, aplicativos de maior desempenho, proporcionando menor latência com dados mais atualizados, juntamente com menores custos e despesas operacionais. Resultados que são refletidos na melhor pontuação NPS do MongoDB. Atlas Vector Search é robusto, econômico e extremamente rápido! Saravana Kumar, CEO, Kovai discutindo o desenvolvimento do assistente de IA de sua empresa Confira nossa série de blog Construindo IA com MongoDB (vá para a seção Primeiros passos para ver as edições anteriores). Aqui você verá o Atlas Vector Search usado para aplicativos baseados em GenAI, abrangendo IA de conversação com chatbots e voicebots, copilotos, inteligência de ameaças e segurança cibernética, gerenciamento de contratos, resposta a perguntas, conformidade de saúde e assistentes de tratamento, descoberta e monetização de conteúdo, e mais. O MongoDB já armazenava metadados sobre artefatos em nosso sistema. Com a introdução do Atlas Vector Search, agora temos um banco de dados abrangente de metadados vetoriais que foi testado em batalha ao longo de uma década e que resolve nossas densas necessidades de recuperação. Não há necessidade de implantar um novo banco de dados que teríamos que managed e aprender. Nossos vetores e metadados de artefatos podem ser armazenados um ao lado do outro. Pierce Lamb, engenheiro de software sênior da equipe de dados e aprendizado de máquina da VISO TRUST O que você pode aprender sobre o estado da IA no relatório Retool? Além de revelar os bancos de dados de vetores mais populares, a pesquisa abrange a IA sob diversas perspectivas. Começa explorando as percepções dos entrevistados sobre IA. (Sem surpresa, o C-suite é mais otimista do que os colaboradores individuais.) Em seguida, explora as prioridades de investimento, o impacto da IA nas futuras perspectivas de emprego e como provavelmente afectará os programadores e as competências de que necessitarão no futuro. A pesquisa explora então o nível de adoção e maturidade da IA. Mais de 75% dos entrevistados afirmam que as suas empresas estão a envidar esforços para começar a utilizar a IA, com cerca de metade a dizer que estes ainda são projetos iniciais e orientados principalmente para aplicações internas. A pesquisa prossegue examinando quais são esses aplicativos e quão úteis os entrevistados consideram que eles são para os negócios. Ele descobre que quase todo mundo usa IA no trabalho, quer tenham permissão ou não, e então identifica os principais pontos problemáticos. Não é nenhuma surpresa que a precisão, a segurança e as alucinações do modelo estejam no topo dessa lista. A pesquisa termina explorando os principais modelos em uso. Mais uma vez, não é surpresa que as ofertas de IA aberta estejam liderando o caminho, mas também indica uma intenção crescente de usar modelos de código aberto juntamente com infraestrutura e ferramentas de IA para personalização no futuro. Você pode se aprofundar em todos os detalhes da pesquisa lendo o relatório . Introdução ao Atlas Vector Search Quer dar uma olhada em nossa oferta de pesquisa de vetores? Acesse nossa página de produto Atlas Vector Search . Lá você encontrará links para tutoriais, documentação e integrações importantes do ecossistema de IA para que possa mergulhar diretamente na construção de seus próprios aplicativos com tecnologia genAI . Se você quiser saber mais sobre as possibilidades de alto nível da pesquisa vetorial, baixe nosso whitepaper Incorporação de IA generativa .

November 30, 2023

Atlas Vector Search obtiene el NPS más alto para desarrolladores en la encuesta Retool State of AI 2023

Retool acaba de publicar su primer informe sobre el estado de la IA y merece la pena leerlo. Inspirada en su popular informe State of Internal Tools, la encuesta State of AI tomó el pulso de más de 1,500 técnicos que abarcan ingeniería de software, liderazgo, gerentes de producto, diseñadores y más provenientes de una variedad de industrias. El propósito de la encuesta es entender cómo esta gente de tecnología usa y construye con inteligencia artificial (IA). Como parte de la encuesta, Retool averiguó qué herramientas eran populares, incluidas las bases de datos vectoriales que se utilizan con mayor frecuencia con IA. La encuesta encontró que MongoDB Atlas Vector Search tenía el Net Promoter Score (NPS) más alto y era la segunda base de datos de vectores más utilizada, tan solo cinco meses después de su versión. Esto lo coloca por delante de las soluciones de la competencia que han existido durante años. En esta publicación de blog, examinaremos el fenomenal aumento de las bases de datos vectoriales y cómo los desarrolladores están utilizando soluciones como Atlas Vector Search para crear aplicaciones impulsadas por IA. También cubriremos otros aspectos destacados clave del informe Retool. Consulte nuestra página de recursos de IA para obtener más información sobre cómo crear aplicaciones basadas en IA con MongoDB. Adopción de bases de datos vectoriales: fuera de Charts (bueno, casi...) Desde la curiosidad matemática hasta la superpotencia detrás de la IA generativa y los LLM, las incorporaciones de vectores y las bases de datos que las managed han recorrido un largo camino en muy poco tiempo. Consulte las tendencias de DB-Engines en modelos de bases de datos durante los últimos 12 meses y verá que las bases de datos vectoriales están muy por encima de todas las demás en cuanto a cambios de popularidad. Basta con mirar la trayectoria "hacia arriba y hacia la derecha" de la línea rosa en los gráficos a continuación. Captura de pantalla cortesía de DB-engines, 8 de noviembre de 2023 Pero, ¿por qué las bases de datos vectoriales se han vuelto tan populares? Son un componente clave en un nuevo patrón arquitectónico llamado generación aumentada de recuperación, también conocida como RAG , una mezcla potente que combina las capacidades de razonamiento de LLM pre-entrenadas y de propósito general y les proporciona datos específicos de la compañía en tiempo real. Los resultados son una aplicación impulsada por IA que sirven de manera única al negocio, ya sea creando nuevos productos, reinventando la experiencia del cliente o impulsando la productividad y eficiencia internas a niveles sin precedentes. Las incrustaciones vectoriales son uno de los componentes fundamentales necesarios para liberar el poder de RAG. Los modelos de incrustación de vectores codifican datos de la empresa, ya sean texto, código, vídeo, imágenes, transmisión de audio o tablas, como vectores. Luego, esos vectores se almacenan, clasifican y consultan en una base de datos de vectores o en un motor de búsqueda de vectores, proporcionando los datos de entrada relevantes como contexto para el LLM elegido. El resultado es una aplicación de IA (en este contexto móvil); también válido: aplicación basada en datos y conocimientos de la empresa que sean relevantes para el negocio, precisos, confiables y actualizados. Como muestra la encuesta de Retool, el panorama de la base de datos vectorial sigue siendo en gran medida un campo nuevo. Menos del 20% de los encuestados utilizan bases de datos vectoriales hoy en día, pero con la tendencia creciente hacia la personalización de modelos e infraestructura de IA, se garantiza que la adopción crecerá. ¿Por qué los desarrolladores están adoptando Atlas Vector Search? La encuesta sobre el estado de la IA de Retool presenta algunas excelentes bases de datos vectoriales que han abierto un camino en los últimos años, especialmente en aplicaciones que requieren búsqueda semántica consciente del contexto. Piense en catálogos de productos o descubrimiento de contenido. Sin embargo, el desafío que enfrentan los desarrolladores al utilizar esas bases de datos vectoriales es que tienen que integrarlas junto con otras bases de datos en la pila tecnológica de su aplicación. Cada capa de base de datos adicional en la pila tecnológica de la aplicación agrega otra fuente de complejidad, latencia y gastos operativos generales. Esto significa que tienen otra base de datos para adquirir, aprender, integrar (para desarrollo, pruebas y producción), proteger y certificar, escalar, monitorear y realizar copias de seguridad, y todo esto mientras mantienen los datos sincronizados en estos múltiples sistemas. MongoDB adopta un enfoque diferente que evita estos desafíos por completo: Los desarrolladores almacenan y buscan incrustaciones de vectores nativos en el mismo sistema que utilizan como base de datos operativa. Al utilizar la arquitectura distributed de MongoDB, pueden aislar estas diferentes cargas de trabajo mientras mantienen los datos completamente sincronizados. Search Nodes proporcionan computación dedicada y aislamiento de cargas de trabajo que es vital para cargas de trabajo de búsqueda vectorial con uso intensivo de memoria, lo que permite un mejor rendimiento y una mayor disponibilidad. Con el esquema de documento dinámico y flexible de MongoDB, los desarrolladores pueden modelar y desarrollar relaciones entre vectores, metadatos y datos de aplicaciones de maneras que otras bases de datos no pueden. Pueden procesar y filtrar datos vectoriales y operativos de cualquier forma que la aplicación necesite con una API de consulta expresiva y controladores que brindan asistencia técnica a todos los lenguajes de programación más populares. El uso de la plataforma de datos para desarrolladores MongoDB Atlas , totalmente gestionada, permite a los desarrolladores lograr la escalabilidad, la seguridad y el rendimiento que esperan los usuarios de sus aplicaciones. ¿Qué significa este enfoque unificado para los desarrolladores? Ciclos de desarrollo más rápidos, aplicaciones de mayor rendimiento que proporcionan menor latencia con datos más actualizados, junto con menores costos y gastos operativos generales. Resultados que se reflejan en la mejor puntuación NPS de su clase de MongoDB. ¡Atlas Vector Search es sólido, rentable e increíblemente rápido! Saravana Kumar, CEO, Kovai , habla sobre el desarrollo del asistente de inteligencia artificial de su empresa Consulte nuestra serie blog Construyendo IA con MongoDB (diríjase a la sección Introducción para ver los números anteriores). Aquí verá Atlas Vector Search utilizado para aplicaciones impulsadas por GenAI que abarcan IA conversacional con chatbots y voicebots, copilotos, inteligencia de amenazas y ciberseguridad, gestión de contratos, respuesta a preguntas, asistentes de tratamiento y cumplimiento de atención médica, descubrimiento de contenido y monetización, y más. MongoDB ya estaba almacenando metadatos sobre artefactos en nuestro sistema. Con la introducción de Atlas Vector Search, ahora contamos con una base de datos integral de metadatos vectoriales que ha sido probada durante más de una década y que resuelve nuestras densas necesidades de recuperación. No es necesario implementar una nueva base de datos, tendríamos que administrar y aprender. Nuestros vectores y metadatos de artefactos se pueden almacenar uno al lado del otro. Pierce Lamb, ingeniero sénior de software del equipo de datos y aprendizaje automático de VISO TRUST ¿Qué puede aprender sobre el estado de la IA en el informe de Retool? Más allá de descubrir las bases de datos vectoriales más populares, la encuesta cubre la IA desde una variedad de perspectivas. Comienza explorando las percepciones de los encuestados sobre la IA. (Como era de esperar, el C-suite es más alcista que los contribuyentes individuales). Luego explora las prioridades de inversión, el impacto de la IA en las perspectivas laborales futuras y cómo afectará probablemente a los desarrolladores y las habilidades que necesitan en el futuro. Luego, la encuesta explora el nivel de adopción y madurez de la IA. Más del 75% de los encuestados dicen que sus empresas están haciendo esfuerzos para comenzar con la IA, y alrededor de la mitad dice que estos todavía eran proyectos iniciales, y principalmente orientados a aplicaciones internas. La encuesta continúa para examinar cuáles son esas aplicaciones y qué tan útiles piensan los encuestados que son para el negocio. Encuentra que casi todo el mundo usa IA en el trabajo, ya sea que se les permita o no, y luego identifica los principales puntos débiles. No es de extrañar que la precisión del modelo, la seguridad y las alucinaciones encabecen esa lista. La encuesta concluye explorando los mejores modelos en uso. Una vez más, no sorprende que las ofertas de Open AI estén liderando el camino, pero también indica una creciente intención de utilizar modelos de código abierto junto con infraestructura de AI y herramientas para personalización en el futuro. Puedes profundizar en todos los detalles de la encuesta leyendo el informe . Introducción con Atlas Vector Search ¿Deseas echar un vistazo a nuestra oferta de búsqueda vectorial? Dirígete a nuestra página de productos Atlas Vector Search . Allí encontrarás enlaces a tutoriales, documentación e integraciones clave del ecosistema de IA para que puedas sumergirte directamente en la creación de tu propia aplicación impulsada por GenAI . Si desea obtener más información sobre las posibilidades de alto nivel de la búsqueda vectorial, descargue nuestro documento técnico de " Embedding Generative AI ."

November 30, 2023

How Atlas Edge Server Bridges the Gap Between Connected Retail Stores and the Cloud

Efficient operations and personalized customer experiences are essential for the success of retail businesses. In today's competitive retail industry, retailers need to streamline their operations, optimize inventory management, and personalize the customer experience to stay ahead. In a recent announcement at MongoDB .local London, we unveiled the private preview of MongoDB Atlas Edge Server , offering a powerful platform that empowers retailers to achieve their goals, even when low or intermittent connectivity issues may arise. What is edge computing, and why is it so relevant for retail? The retail industry's growing investment in edge computing, projected to reach $208 billion by 2023, confirms the strategic shift retailers are willing to take to reach new markets and enhance their offers. And for good reason — in scenarios where connectivity is unreliable, edge computing allows operations to continue uninterrupted. Edge computing is a strategic technology approach that brings computational power closer to where data is generated and processed, such as in physical retail stores or warehouses. Instead of relying solely on centralized data centers, edge computing deploys distributed computing resources at the edge of the network. The evolution of investments in edge computing reflects a journey from initial hesitation to accelerated growth. As edge computing continues to mature and demonstrate its value, retailers are likely to further embrace and expand their focus in bringing applications where the computing and data is as close as possible to the location where it's being used. Let’s dig into how MongoDB addresses the current challenges any retailer would experience when deploying or enhancing in-store servers using edge computing. Connected store: How MongoDB's versatile deployment from edge to cloud powers critical retail applications. Currently, many retail stores operate with an on-site server in place acting as the backbone for several critical applications within the store ecosystem. Having an on-site server means that the data doesn't have to travel over long distances to be processed, which can significantly reduce latency. This setup can often also be more reliable, as it doesn't depend on internet connectivity. If the internet goes down, the store can continue to operate since the essential services are running on the local network. This is crucial for applications that require real-time access to data, such as point-of-sale (POS) systems, inventory management, and workforce-enablement apps for customer service. The need for sync: Seamless edge-to-cloud integration The main driver for retailers taking a hybrid approach is that they want to experience the low latency and reliability of an on-site server coupled with the scalability and power of cloud computing for their overall IT stack. The on-site server ensures that the devices and systems that are critical to sales floor operations — RFID tags and readers for stock management, mobile scanners for associates, and POS systems for efficient checkout — remain functional even with intermittent network connectivity. This data must be synced to the retailer’s cloud-based application stack so that they have a view of what’s happening across the stores. Traditionally this was done with an end-of-day batch job or nightly upload. The aim for the next generation of these architectures is to give real-time access to the same data set, seamlessly reflecting changes made server-side or in the cloud. This needs to be achieved without a lag from the store being pushed to the cloud and without creating complex data sync or conflict resolution that needs to be built and maintained. These complexities may cause discrepancies between the online and offline capabilities of the store's operations. It makes sense that for any retailer wanting to benefit from both edge and cloud computing, it must simplify its architecture and focus on delivering value-added features to delight the customer and differentiate from their competitors. Low-latency edge computing with Atlas Edge Server and its different components to achieve data consistency and accuracy across layers This is when Atlas Edge Server steps in to bridge the gap. Edge Server runs on-premises and handles sync between local devices and bi-directional sync between the edge server and Atlas. It not only provides a rapid and reliable in-store connection but also introduces a tiered synchronization mechanism, ensuring that data is efficiently synced with the cloud. These devices are interconnected through synchronized data layers from on-premises systems to the cloud, simplifying the creation of mobile apps thanks to Atlas Device SDK , which supports multiple programming languages, development frameworks, and cloud providers. Additionally, Atlas Device Sync automatically handles conflicts, eliminating the need to write complex conflict-resolution code. In the below diagram, you can see how the current architecture for a connected store with devices using Atlas Device SDK and Atlas Device Sync would work. This is an ideal solution for devices to sync to the Atlas backend. A high-level overview of the Architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas when connectivity is unreliable. In a store with Atlas Edge Server, the devices sync to Atlas on-premises. All changes made on the edge or on the main application database are synced bidirectionally. If the store server goes offline or loses connectivity, the devices can still access the database and update it locally. The store can still run its operations normally. Then, when it comes back online, the changes on both sides (edge and cloud) are resolved, with conflict resolution built into the sync server. A high-level overview of the architecture for connected devices in a retail space with MongoDB Device Sync and MongoDB Atlas solving connectivity issues by implementing an on-premises Atlas Edge Server. Deploying Atlas Edge Server in-store turns connected stores into dynamic, customer-centric hubs of innovation. This transformation produces advantageous business outcomes including: Enhanced inventory management — The hybrid model facilitates real-time monitoring of logistics, enabling retailers to meticulously track stock in store as shipments come in and sales or orders are processed. By processing data locally and syncing with the cloud, retailers gain immediate insights, allowing for more precise inventory control and timely restocking. Seamless operational workflows — The reliability of edge computing ensures essential sales tools — like RFID systems, handheld scanners, workforce apps, and POS terminals — remain operational even during connectivity hiccups. Meanwhile, the cloud component helps ensure that all data is backed up and accessible for analysis, leading to more streamlined store operations. Customized shopping experiences — With the ability to analyze data on-the-spot (at the edge) and harness historical data from the cloud, retailers can create highly personalized shopping experiences. This approach enables real-time, tailored product recommendations and promotions, enhancing customer engagement and satisfaction. Conclusion With Atlas Edge Server, MongoDB is committed to meeting the precise needs of modern retail stores and their diverse use cases. Lacking the seamless synchronization of data between edge devices and the cloud, delivering offline functionality that enables modern, next-generation workforce applications, as well as in-store technologies like POS systems, is daunting. Retailers need ready-made solutions so they don't have to deal with the complexities of in-house, custom development. This approach allows them to channel their development efforts towards value-added, differentiating features that directly benefit their customers by improving their in-store operations. With this approach, we aim to empower retailers to deliver exceptional customer experiences and thrive in the ever-evolving retail landscape. Ready to revolutionize your retail operations with cutting-edge technology? Discover how MongoDB's Atlas Edge Server can transform your store into a dynamic, customer-centric hub. Don't let connectivity issues hold you back. Embrace the future of retail with Atlas Edge Server!

November 30, 2023