New World's Slow-Moving Mega Mall Project Adds to Debt Concerns
The concerns have centered around slow preparation work by tenants to move into the 11 Skies mall, signs that carriers are reluctant to shift flights to Hong Kong's second airport terminal adjacent to the shopping center, and a tepid economic outlook that threatens tourist spending, the people said, asking not to be named discussing private information.
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13 minutes ago
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The Fourth Generation Rises: How Singdata's Lakehouse is Defining the General Incremental Compute Standard and Revolutionizing Data Processing Architecture
BEIJING, July 24, 2025 /PRNewswire/ -- The data processing landscape is experiencing a seismic shift. As enterprises grapple with exponentially growing data volumes and increasingly complex real-time requirements, traditional architectures are reaching their breaking point. Today, a new paradigm is emerging—one that promises to fundamentally reshape how we think about data processing efficiency, cost optimization, and architectural design. At the forefront of this transformation stands Singdata, the pioneering force behind General Incremental Compute (GIC), a revolutionary approach that represents the fourth generation of data processing architecture. Far from being merely another Flink alternative, GIC introduces a paradigm shift that addresses the fundamental limitations plaguing modern data infrastructure. The Flink Era: Hitting the Ceiling of Traditional Stream Processing Apache Flink has undoubtedly been a cornerstone of real-time data processing, excelling in millisecond-latency scenarios like real-time dashboards, fraud detection, and programmatic advertising. However, as AI-driven applications proliferate and data volumes reach unprecedented scales, Flink's architectural constraints have become increasingly apparent. The most significant limitation lies in computational scope. While Flink dominates millisecond-response scenarios, the vast majority of enterprise use cases—user behavior analytics, business metric monitoring, and ML model observability—operate in minute-to-hour timeframes. For these scenarios, Flink's continuous resource occupation model proves economically inefficient, creating an unsustainable cost structure that forces organizations into difficult trade-offs between real-time insights and operational expenses. Data processing fragmentation compounds these challenges. Enterprises must maintain separate Flink and Spark infrastructures for stream and batch processing, respectively. This dual-engine approach introduces syntax incompatibilities, forces development teams to master multiple technology stacks, and creates consistency nightmares where identical metrics produce different results across systems. The operational overhead of maintaining these parallel architectures has become a significant barrier to digital transformation initiatives. Development complexity further exacerbates the problem. Implementing sophisticated operations like dimension table joins, state management, and windowed computations in Flink requires extensive boilerplate code and deep understanding of internal mechanisms. Every business requirement change demands significant engineering effort, making agile data product development nearly impossible for most organizations. Perhaps most critically, Flink's resource utilization model reveals fundamental architectural flaws. Stream processing requires persistent resource allocation regardless of data flow volume, creating massive inefficiencies during low-traffic periods. As data volumes scale, resource consumption grows exponentially, forcing enterprises into an untenable choice between real-time capabilities and cost control. Singdata's Vision: Establishing Technical Authority in Incremental Computing Recognizing these systemic limitations, Singdata has emerged as the definitive standard-setter in incremental computing, establishing both conceptual framework and technical implementation standards for the industry. Since 2023, Singdata has pioneered the "incremental computing" concept, evolving it into the comprehensive "General Incremental Compute" framework by 2025. This progression represents more than incremental innovation—it constitutes a fundamental rethinking of data processing architecture that addresses the core inefficiencies of existing approaches. Central to Singdata's leadership is the SPOT standard—a comprehensive framework encompassing Standard SQL for unified syntax, Performance optimization for efficiency gains, Open Format compatibility with ecosystems like Apache Iceberg, and Trade-offs for flexible cost-performance balancing. This standard doesn't merely solve current pain points; it establishes a roadmap for industry-wide architectural evolution. Singdata's strategic approach to ecosystem development demonstrates remarkable technical confidence. Rather than pursuing proprietary lock-in strategies, the company has embraced collaborative ecosystem development, accelerating technology adoption while reinforcing its position as the authoritative voice in incremental computing standards. The Fourth Generation Advantage: Architectural Breakthrough Singdata represents a generational leap in data processing architecture, delivering unprecedented capabilities that traditional Lambda architectures cannot match. The core innovation lies in computational methodology. While Flink employs "continuous processing" requiring persistent resource allocation and batch systems use "full recalculation" with poor latency characteristics, incremental computing implements "compute-on-need" processing. This approach processes only data deltas, achieving minute-level freshness while eliminating persistent resource occupation—delivering cost efficiency improvements of several orders of magnitude. Unified technology stack integration eliminates the architectural fragmentation that plagues traditional systems. Beyond supporting standard SQL syntax, incremental computing enables truly unified stream-batch development. Complex operations like real-time dimension joins and sophisticated queries execute through simple SQL statements, improving development efficiency by an order of magnitude. Open format ecosystem compatibility breaks down vendor lock-in barriers. Built on Apache Iceberg and other open data lake formats, Singdata lakehouse architecture integrates seamlessly with existing AI data ecosystems. Organizations can leverage new capabilities without massive technology migrations, dramatically reducing adoption barriers. Flexible scheduling capabilities provide unprecedented resource configuration freedom. Enterprises can adjust processing frequency from one minute to several hours based on business requirements, optimizing the balance between data freshness and computational costs—a flexibility impossible with traditional stream processing. Redefining the Boundaries of Data Processing Singdata's innovation transcends Flink replacement, fundamentally redefining data processing paradigms and expanding the realm of what's possible in real-time analytics. The diversification of technical choices proves that real-time data processing no longer follows a single path. Organizations can now select optimal solutions based on specific latency requirements: millisecond scenarios continue leveraging stream processing, while minute and hour-scale scenarios benefit from incremental computing, with seamless transitions to daily batch processing when appropriate. This technological pathway diversity provides enterprises with unprecedented choice and flexibility. In cost-efficiency optimization, incremental computing achieves what traditional technologies considered impossible—simultaneously delivering real-time capabilities, cost control, and high performance. This breakthrough unlocks real-time data processing benefits for organizations previously constrained by resource limitations, democratizing advanced analytics capabilities across enterprise segments. The open ecosystem approach positions Singdata's SPOT standard as the emerging industry benchmark. Major technology vendors are increasingly focusing on incremental computing, with companies like Alibaba and Tencent making significant investments in this space. This ecosystem convergence effect accelerates industry-wide migration toward fourth-generation data processing paradigms. Leading the Revolution The successful adoption by Rednote demonstrates real-world validation of GIC's capabilities, proving that this isn't merely theoretical advancement but practical transformation delivering measurable business value. As more organizations recognize the limitations of traditional architectures and seek sustainable paths to real-time analytics, incremental computing emerges as the definitive solution. Singdata stands at the epicenter of this transformation, not merely as a technology provider but as the architect of an entirely new category. By establishing technical standards, fostering ecosystem development, and proving real-world viability, Singdata is reshaping the fundamental assumptions underlying modern data infrastructure. The fourth generation of data processing has arrived, and it promises to be as transformative as the shift from batch to stream processing a decade ago. Organizations that recognize this paradigm shift and embrace incremental computing will find themselves with sustainable competitive advantages in the data-driven economy. Those that don't risk being left behind by an industry moving inexorably toward more efficient, cost-effective, and flexible data processing architectures. The revolution has begun, and Singdata is leading the charge. View original content to download multimedia: SOURCE Singdata Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
13 minutes ago
- Yahoo
China plans network to sell surplus computing power in crackdown on data centre glut
(Reuters) -China is taking steps to build a network to sell computing power and curb the unwieldy growth of data centres after thousands of local government-backed centres that sprouted in the country caused a capacity glut and threatened their viability. The state planner is conducting a nationwide assessment of the sector after a three-year data centre building boom, according to two sources familiar with the matter and a document seen by Reuters. Beijing is also seeking to set up a national, state-run cloud service for harnessing surplus computing power, according to Chinese government policy advisers. The Ministry of Industry and Information Technology (MIIT) is collaborating with China's three state telecoms companies on ways to connect the data centres in a network to create a platform that can sell the computing power, they said. Computing power is a crucial element in the race for technological supremacy between China and the U.S. Besides being an embarrassment for Beijing, unused computing power and financially shaky data centres could hinder China's ambitions in the development of artificial intelligence capabilities. "Everything will be handed over to our cloud to perform unified organisation, orchestration, and scheduling capabilities," Chen Yili, deputy chief engineer at the China Academy of Information and Communications Technology, a think tank affiliated to the industry ministry, told an industry conference in Beijing last month. Chen did not specify details of the cloud service proposal, but his presentation materials showed China was targeting standardised interconnection of public computing power nationwide by 2028, even as some analysts were skeptical about the plan given the technological challenges it posed. China Mobile, China Unicom and China Telecom, the state-run telecoms companies, and MIIT did not respond to requests for comment. The sources did not want to be identified because of the sensitivities of the issue. NATIONWIDE NETWORK China's data centre building boom kickstarted in 2022 after Beijing launched an ambitious infrastructure project called "Eastern Data, Western Computing", aimed at coordinating data centre construction by concentrating facilities in western regions - where energy costs are cheaper - to meet demand from the eastern economic hubs. Chen said at the June event that the industry ministry has so far licensed at least 7,000 computing centres. A Reuters review of government procurement documents for data centres used in computing shows a surge last year in state investment, totalling 24.7 billion yuan ($3.4 billion), compared to over 2.4 billion yuan in 2023. This year, already 12.4 billion yuan has been invested in these centres, most of it in the far-west region of Xinjiang. But while only 11 such data centre-related projects were cancelled in 2023, over 100 cancellations occurred over the past 18 months, pointing to growing concerns among local governments about returns on their investments. And utilisation rates are estimated to be low, with four sources putting them at around 20%-30%. Driven by expectations that government and state-owned firms will act as buyers, investors and local governments tend to build without considering real market needs, said a project manager who works for a server company that provides products for such data centers. "The idea of building data centers in remote western provinces lacks economic justification in the first place," said Charlie Chai, an analyst with 86Research, adding lower operating costs had to be viewed against degradation in performance and accessibility. To regulate the sector's growth, China's state planner National Development and Reform Commission (NDRC) initiated a nationwide assessment earlier this year that has already tightened scrutiny of new data center projects planned after March 20, and banned local governments from participating in small-sized computing infrastructure projects. The NDRC aims to prevent resource wastage by setting specific thresholds - such as requiring a computing power purchase agreement and a minimum utilisation ratio - to filter out unqualified projects, according to a person familiar with the matter, who did not provide details on the thresholds. NDRC did not respond to a request for comment. CHALLENGES Industry sources and Chinese policy advisers said the formation of a computing power network will not be easy, given that the technology for data centers to efficiently transfer the power to users in real-time remains underdeveloped. When the Chinese government rolled out the Eastern Data, Western Computing project, it targeted a maximum latency of 20 milliseconds by 2025, a threshold necessary for real-time applications such as high-frequency trading and financial services. However, many facilities, especially those built in the remote western regions, still have not achieved this standard, the project manager said. Many of the centres also use different chips from Nvidia and local alternatives such as Huawei's Ascend chips, making it difficult to integrate various AI chips with different hardware and software architectures to create a unified cloud service. Chen, however, was optimistic, describing a vision of the cloud bridging the differences in underlying computing power and the physical infrastructure. "Users do not need to worry about what chips are at the bottom layer; they just need to specify their requirements, such as the amount of computing power and network capacity needed," he said. ($1 = 7.1715 Chinese yuan renminbi) Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
13 minutes ago
- Yahoo
EngineAI Raises Nearly RMB 1 Billion in Pre-A++ and A1 Rounds, Led by JD.com
SHENZHEN, China, July 24, 2025 /PRNewswire/ -- After securing investments earlier this year from leading Middle Eastern and South Korean investors, EngineAI has successfully concluded its Pre-A++ and A1 funding rounds. The Pre-A++ round was led by XPeng-backed Rockets Capital, while spearheaded the A1 round with participation from strategic investors CATL Capital (affiliated with CATL) and Yintai Group, as well as institutional investors TH Capital, Guochen Venture Capital (Fortune Capital affiliate), and Huangpu River Capital. Existing shareholders also joined the two rounds. The strong investor confidence allows EngineAI to enter into mass production, further diversify product lines, and achieve breakthroughs in the real-world deployment of embodied intelligence and related technologies. A rising star in humanoid robotics, EngineAI drives innovation through cutting-edge technology, delivering intelligent, hyper-agile robots. Its proprietary joint modules set industry benchmarks for explosive power, torque, and rotational speed, enabling lifelike motion. By solving Sim2Real challenges, the company has carved out a unique tech advantage, achieving millimeter precision in high-dynamic maneuvers like complex dances, front flip, and sprinting. The global humanoid robotics market is forecast to exceed $100 billion by 2030, driven by strong enterprise demand across manufacturing, services, and logistics. EngineAI's "open-source hardware + ecosystem profit-sharing" model accelerates market penetration through strategic partnerships, enabling rapid application diversification and developer engagement. To overcome embodied intelligence hurdles, the company merges traditional control systems with reinforcement learning, boosting efficiency, precision, and reliability. This dual approach not only truly meets market demands but also gradually penetrates into consumer households, forming a unique commercial ecosystem. The newly secured capital will enable rapid advancement of EngineAI's core initiatives in H2 2025: Product Development: EngineAI has built a comprehensive product matrix spanning bipedal and full humanoid robots across performance tiers. The company is now scaling trial production and delivery, targeting a 5x expansion of its production team to meet surging demand. Technology Leadership: Embodied intelligence R&D will see intensified investment to fast-track core technology commercialization, solidifying EngineAI's global market position. EngineAI has established strategic collaborations with industry giants including NVIDIA, Amazon, Tencent, and ByteDance to advance humanoid robotics applications across commercial services, hazardous operations, and cultural tourism. These partnerships are accelerating the path to large-scale commercial adoption. With production accelerating and certain production segments already exceeding capacity targets, EngineAI is on track to complete optimization upgrades ahead of Q4 2025. This expansion ensures reliable delivery of advanced robotic solutions, positioning the company for successful mass-market penetration. EngineAI is expanding its workforce across critical R&D, production, and market expansion roles. The company is simultaneously enhancing its internal training programs to develop employees' technical and leadership capabilities, building a sustainable talent engine for continuous innovation. Looking ahead, EngineAI will intensify efforts in mass production, product diversification, and embodied AI implementation, contributing core strengths to the high-quality development of the humanoid robotics industry. For more information about EngineAI, please visit CONTACT: Zibin Cencenzb@ View original content to download multimedia: SOURCE EngineAI Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
