雪花公司首席执行官斯 里达尔・拉马斯瓦米 知情人士透露，雪花公司（Snowflake）正洽谈收购加州应用监控初创企业Observe 公司，这笔交易的 估值约10 亿美元，若最终达成，或将成为雪花公司成立以来规模最大的一笔收购案。 总部位于美国加州圣马特奥市的 Observe 公司，主营可观测性工具的研发与销售。这类工具能帮助开发 者掌握自有应用的运行状态，及时发现程序故障与服务中断问题。此次收购完成后，雪花公司将与大数 据分析服务商 Datadog、思科旗下的 Splunk 等软件巨头展开更直接的正面竞争。 雪花公司近期也正式入局人工智能商业化赛道，推出多款人工智能工具，宣称可实现企业办公场景的自 动化处理，涵盖 IT 工单解决、数据看板生成、客户服务对接等各类业务；但该领域的赛道玩家众多， 已有大批软件服务商推出同类人工智能产品。雪花首席执行官斯里达尔・拉马斯瓦米本月早些时候透 露，公司人工智能相关产品的年化营收已突破1 亿美元，达成其在今年年初设定的内部营收目标。 雪花公司的股价今年累计上涨 43%，当前市值约770 亿美元。 核心要点 雪花此次拟收购的这家可观测性领域初创企业，其核心能力可助力各类企业， ...

Core Viewpoint - Snowflake is in talks to acquire application monitoring startup Observe Inc. for approximately $1 billion, which could be its largest acquisition to date [3]. Group 1: Acquisition Details - Observe Inc. specializes in observability tools that help developers monitor application performance and identify service disruptions [3]. - The acquisition will position Snowflake in direct competition with software companies like Datadog and Cisco's Splunk [3]. - Observe has raised over $470 million since its founding in 2018, with a recent valuation of $848 million [4]. Group 2: Previous Acquisitions - Last year, Snowflake acquired TruEra AI, a startup focused on monitoring the performance of large language model applications, although the deal amount was not disclosed [4]. Group 3: Financial Performance - Snowflake's stock has risen 43% this year, bringing its market capitalization to approximately $77 billion [6]. - The company reported a quarterly revenue growth of 29% to $1.21 billion, exceeding growth expectations by 3 percentage points [6]. - Snowflake has slightly raised its product revenue growth forecast for the fiscal year ending January from 25% to 27% [6]. Group 4: AI Product Launch - Recently, Snowflake began selling AI products aimed at automating customer tasks, achieving an annualized revenue of over $100 million [5].

12 月 10 日，观测云2025 可观测日·深圳站成功举办。来自云计算、AI、运维与工程领域的行业专家、企业技术负 责人齐聚深圳，在一个下午的深度交流中，共同探讨 AI 时代下，可观测性的进化方向与落地路径。 它不是一场"单向输出"的技术论坛，而是一场关于未来技术体系的集体对话。 01 开场致辞：观测云如何在 AI 时代走在前面 大会伊始，由观测云业务 VP 蔡文瑜带来开场致辞，系统回顾了观测云在 2025 年取得的关键进展，并阐述了观测 云面向 2026 的核心判断与发展方向。 过去三年里，观测云完成了 3 次大版本发布、100+ 次产品迭代，逐步搭建起一套完整、稳定、可持续演进的可观 测性平台；同时，累计沉淀了超过 45 万字的技术文档库，让每一位开发者都能用得明白。 目前，观测云已在全球部署 10+ 节点，服务 8 万+ 全球活跃用户账号，并获得 1000+ 付费商业用户的持续使用与 信任。 面向 2026，观测云将持续围绕更智能的分析能力、更工程化的落地方式，以及更开放的生态集成，推动可观测性 真正成为企业在 AI 时代应对复杂系统的底层支撑。在蔡文瑜看来，AI 正在重塑整个技术体系的运行方式。 A ...

Core Insights - The role and methodology of software architects are undergoing significant transformation due to the rapid evolution of software development and the rise of artificial intelligence [1][2][3] - Architects now face challenges beyond technical choices, focusing on maintaining architectural health and team efficiency in a fast-paced environment [1][2] - AI tools are enhancing software development processes, allowing architects to leverage automation for tasks like code generation and performance analysis, but human judgment remains crucial [1][4] Group 1: Architectural Transformation - Software architects are transitioning into a phase where technical skills, business understanding, and data-driven decision-making are intertwined [2][3] - The emergence of cloud-native architectures, microservices, and low-code/no-code platforms has increased system complexity, necessitating a shift in architectural practices [1][2] - The concept of "measurable and evolvable architecture" is gaining traction, emphasizing the need for architects to focus on quantifiable metrics to ensure system adaptability [3][6] Group 2: AI's Role in Architecture - AI is positioned as a supportive tool for analyzing metrics and suggesting improvements, but it cannot replace human decision-making [4][7] - Current AI-generated architectural suggestions are viewed as valuable assistants rather than partners, highlighting the importance of human oversight in architectural decisions [4][7] - The integration of observability into system design is essential for maintaining architectural quality, with continuous monitoring of software attributes being a key aspect [8][9] Group 3: Challenges and Cultural Shifts - The primary resistance to architectural transformation in organizations often stems from cultural rather than technical challenges [7][8] - Establishing a sustainable architectural culture requires collaboration and shared vision among team members, rather than merely creating an architectural department [7][8] - The importance of curiosity, analytical skills, and leadership in architects is emphasized, as these traits facilitate adaptation and innovation in a rapidly changing environment [10]

Core Insights - Bonree Data launched the Bonree ONE 2025 Fall Edition, an integrated intelligent observability platform aimed at helping enterprises navigate complex digital systems more effectively [1] - The company emphasizes that observability is a strategic cornerstone for businesses, especially in the context of AI-driven industrial transformation [1] Group 1: AI Deep Integration - The platform features a multi-dimensional intelligent module collaboration framework that integrates observability with AI, enabling autonomous operational decision-making and precise root cause analysis [3][4] - The "Xiao Rui Assistant" serves as a unified interaction entry point, offering intelligent Q&A, navigation guidance, and AI writing capabilities to enhance user experience [3] Group 2: Comprehensive Multi-Dimensional Observability - The observability capability is centered around business forms, organizing IT operations data for layered and categorized presentation, allowing for quick emergency recovery and business continuity [5] - Users can customize key path views around core business processes, enabling a holistic view of system architecture and operational status [5] Group 3: Architecture Breakthrough and Upgrades - The core ETL engine, Ingester, has been restructured to reduce resource consumption by 65% and achieve millisecond-level data access, enhancing query efficiency [6][8] - The QueryService has significantly improved compatibility with PromQL, increasing query convenience and capability [6][9] Group 4: AI Service and Intelligent Capabilities - The AI Service is built around large model technology, featuring intelligent modules for smart Q&A, next-generation root cause analysis, and natural language-driven intelligent retrieval [10] - The platform supports flexible scheduling and closed-loop service capabilities, facilitating comprehensive coverage of AI technology from generation to implementation [10] Group 5: Industry Recognition and Application - Bonree ONE has gained recognition from over a hundred leading clients across key sectors such as finance, internet, energy, and manufacturing [11] - Guotou Securities has implemented Bonree ONE to enhance its end-to-end observability system, improving collaboration efficiency across various operational scenarios [11] Group 6: Future Outlook - Bonree Data plans to increase overseas investments, focusing on deepening its presence in Southeast Asia and expanding its global business footprint [16] - The company aims to become a top-tier high-tech firm in the enterprise service sector, committed to building smarter and more reliable observability capabilities for global clients [16]

Core Viewpoint - The article discusses a live event featuring experts from Alibaba Cloud, ByteDance, and Xiaohongshu, focusing on the theme of observability in the AI era, highlighting the transformation and control of intelligence in this context [2][3]. Group 1: Event Details - The live event is scheduled for October 15, from 20:00 to 21:30, and will be hosted by Zhang Cheng, a senior technical expert from Alibaba Cloud [2]. - The guest speakers include Dr. Li Ye, an algorithm expert from Alibaba Cloud, Dr. Dong Shandong, the algorithm lead for ByteDance's Dev-Infra observability platform, and Wang Yap, the head of the observability team at Xiaohongshu [3]. Group 2: Discussion Topics - The event will address the "route dispute" regarding whether the implementation of large models should prioritize intelligent governance or algorithms [3]. - It will also cover the efficiency revolution, specifically how SRE Agents can reduce noise and improve efficiency [6]. Group 3: Live Event Benefits - Attendees will receive an AI observability resource package, which includes insights on building a general intelligent closed loop of "observability - analysis - action" [6]. - The package will provide foundational principles for observability metrics attribution and share experiences with eBPF in large-scale operations [6]. - A new attribution platform is highlighted, which can locate 80% of online faults within minutes, providing essential support for mobile fault mitigation [6].

Core Insights - The article discusses the significant impact of AI programming tools on development efficiency while highlighting new challenges such as security vulnerabilities, low-quality code, and the complexity of debugging AI-generated code [2][4]. Group 1: AI Tools and Challenges - AI programming tools have been reported to significantly enhance development efficiency, but they also introduce new security vulnerabilities and low-quality code issues [2]. - The increase in API numbers due to AI tools has led to a heavier operational burden for enterprises [2]. - The "black box" issue complicates the understanding of AI-generated code, making debugging and security checks more time-consuming [2]. Group 2: Security and Performance - Performance is crucial for user experience, and balancing security with user-friendly authentication processes is a pressing challenge [4]. - Over 91% of users have implemented WAAP (Web Application and API Protection) to secure AI and machine learning models [5]. Group 3: AI in Operations - A significant percentage of operational staff are utilizing AI to streamline processes: 57% use AI for script generation, 56% for custom policy creation, and 55% for executing scripts [7]. - Observability is key for AI-driven automation, with 65% of respondents leveraging it for this purpose [7]. Group 4: Application Trends - The proportion of modern applications is expected to surpass traditional applications by 2025, with modern applications rising from 29% in 2020 to 53% [7]. - By 2025, 54% of application and API performance analysis will be based on large models [7]. Group 5: AI Implementation Challenges - Complex IT architectures, unique security needs, and cost control are identified as major challenges for enterprises adopting AI applications [9]. - By 2028, 80% of enterprises are expected to embed AI capabilities, with 94% of AI applications deployed in hybrid cloud environments [12]. Group 6: F5's Response - F5 has transitioned to an Application Delivery and Security Platform (ADSP) to meet the growing demand for integrated performance and security solutions [11]. - The ADSP platform aims to provide seamless operation across various environments, addressing the complexities of modern application security [14]. Group 7: AI Gateway and Security - F5 has introduced the AI Gateway, which offers capabilities for routing based on large language models and provides protection against prompt injection and PII data leakage [16]. - The AI Gateway enhances GPU utilization rates by 30-60% while improving service success rates by at least 8% in specific applications [16]. Group 8: Comprehensive Services - F5 offers comprehensive application delivery and security services, including load balancing, DNS, CDN, and API gateways, adaptable to various deployment environments [17]. - The platform integrates capabilities across NetOps, SecOps, and DevOps, providing unified policy management and deep security analysis [17]. Group 9: AI Assistant - F5 has launched an AI assistant that enhances the platform's intelligence, capable of explanation, generation, and optimization across all F5 products [19].

Core Viewpoint - The China Securities Association (CSA) is developing a standard for the stability assurance system of information systems in the securities industry to enhance the stability of capital markets and address existing pain points in system management [1][2][3] Group 1: Industry Challenges - The industry faces four main challenges: lack of resilience design in system development, high operational risk prevention costs, reliance on expert experience for emergency response, and insufficient application of intelligent technologies [2][3] - Current operational risk perception is primarily reactive, lacking proactive data-driven risk detection capabilities [2] - Emergency response efficiency is hindered by dependence on individual expert knowledge rather than data-driven collaborative capabilities [2] Group 2: Standard Development Principles - The standard is based on four principles: compliance, controllability, closed-loop processes, and data-driven approaches [2] - It aims to provide technical support for securities firms to meet regulatory compliance requirements while being adaptable to different institutional sizes [2][3] Group 3: Stability Assurance Framework - The standard proposes a "three-in-one" stability assurance framework, which includes organizational support, institutional support, and process support [3] - Organizational support defines the structure and personnel competency requirements for stability assurance [3] - Institutional support encompasses regulations, technical standards, and operational procedures to ensure traceability and implementation [3] Group 4: Innovative Approaches - The standard integrates advanced technologies such as AI algorithms and big data analysis into stability management processes [3][4] - It establishes measurable stability evaluation metrics, including fault monitoring discovery rates and recovery capability standards [4] - A continuous improvement mechanism is proposed, focusing on monitoring, evaluation, and optimization [4]

Core Viewpoint - The China Securities Association is seeking industry feedback on the draft standard for the stability assurance system of securities industry information systems, aiming to enhance the security and stability of network and information systems in the capital market [1][2]. Summary by Sections Current Issues in System Operation - The securities market requires high transaction continuity, and any anomalies in trading systems can directly impact investor rights and market order. The complexity of system architecture has increased significantly due to the widespread adoption of technologies like cloud computing and distributed architecture, making traditional operation and maintenance models inadequate [3]. - Current practices in stability management include change control, emergency response, and monitoring mechanisms, but the deep application of distributed architecture and microservices has led to exponential complexity, necessitating a proactive and intelligent stability assurance system [3]. - There is a lack of embedded resilience design in system development, insufficient capabilities in monitoring and automation, and a predominant reactive approach to risk perception, which hinders the ability to preemptively address potential issues [3]. Proposed "Three-in-One" Assurance System - The draft standard aims to integrate best practices from leading securities firms to provide a practical framework for stability assurance, promoting the digital, standardized, and collaborative development of technical capabilities across the industry [4]. - The standard focuses on the actual needs of the securities industry, extracting replicable technical solutions and management processes while allowing flexibility for different-sized institutions. It incorporates advanced technologies like AI algorithms and big data analysis into stability management processes [4]. - The "Three-in-One" framework includes organizational assurance, institutional assurance, and process assurance, detailing the organizational structure, personnel competency requirements, and management goals [4]. Process Assurance Focus - The standard emphasizes ten core processes related to stability architecture management, observability management, monitoring and alerting, and fault management, each with mechanisms, key activities, and evaluation elements [5]. - The content was developed with input from nearly 20 industry experts, focusing on the core value of stability assurance and guiding the industry to enhance operational resilience through digital means [5]. - Measurable stability evaluation elements such as "fault monitoring discovery rate" and "automation release rate" are established, with a continuous assessment and review mechanism to form a closed-loop improvement process [5].

Core Insights - The report highlights the significance of observability practices in enhancing operational efficiency and service quality during the digital transformation of the financial industry, particularly in banking, securities, and insurance sectors [1][2][8]. Group 1: Industry Overview - The financial industry's digital transformation is transitioning from basic informatization to intelligent systems, with global digital transformation spending expected to approach $4 trillion by 2027, and China's financial IT spending projected to reach 335.936 billion yuan by 2025 [8][12]. - Observability is increasingly recognized as a critical support for digital transformation, with its market experiencing explosive growth driven by policy and demand [12][14]. Group 2: Technological Trends - Real-time data collection and analysis technologies are evolving from mere support tools to core systems, enabling real-time decision-making and agile responses [20][21]. - Artificial intelligence (AI) is becoming a key driver in reshaping observability capabilities, enhancing risk prediction, root cause diagnosis, and user experience optimization [24][25]. - Distributed system monitoring is essential for ensuring business continuity and system reliability, with innovations in monitoring solutions addressing the unique challenges of the financial sector [26][27]. Group 3: Sector-Specific Practices - In banking, full-link monitoring has reduced fault localization time by 80%, significantly enhancing system stability [34][36]. - The securities sector has optimized trading system performance, achieving response times under 300 milliseconds, crucial for high-frequency trading scenarios [34][40]. - The insurance industry has improved underwriting efficiency by 35% through data visualization and real-time monitoring tools, enhancing process optimization and risk management capabilities [34][46]. Group 4: Challenges and Future Directions - The financial industry faces challenges in optimizing data flow and enhancing monitoring comprehensiveness and accuracy due to increasing business complexity and system scale [2][12]. - Future developments in observability will focus on establishing industry standards, building intelligent operation ecosystems, and adapting traditional architectures to new observability frameworks [8][12][30].