LAS VEGAS--([BUSINESS WIRE](https://www.businesswire.com))--At AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and Supabase, the Postgres development platform, today announced two new[Amazon Simple Storage Service (Amazon S3)](https://cts.businesswire.com/ct/CT? id=smartlink&url=https://aws.amazon.com/s3&esheet=54366254&newsitemid=20251201655323&lan=en- US&anchor=Amazon+Simple+Storage+Service+(Amazon+S3)&index=1&md5=e9b18f2160f1e4af7af770d7b2e52f5e)-based storage ...

Core Insights - Data has become one of the most valuable assets for organizations, playing a crucial role in strategic decision-making, operational optimization, and gaining competitive advantages [1] - Data engineering is a key discipline that manages the entire process from data collection to transformation, storage, and access [1] - Organizations are shifting towards architectures that can respond to various data needs, with data management strategies like data warehouses, data lakes, data lakehouses, and data meshes playing significant roles [1] Group 1: Data Management Strategies - Data warehouses focus on structured data and are optimized for reporting and analysis, allowing for easy data retrieval and high-performance reporting [12][15] - Data lakes provide a flexible structure for storing structured, semi-structured, and unstructured data, making them suitable for big data projects and advanced analytics [21][24] - Data lakehouses combine the flexibility of data lakes with the structured data management capabilities of data warehouses, allowing for efficient analysis of various data types [27][30] Group 2: Data Architecture Design - A solid data architecture design is critical for the success of data warehouse projects, defining how data is processed, integrated, stored, and accessed [9] - The choice of data architecture design method should align with project goals, data types, and expected use cases, as each method has its advantages and challenges [10][43] - The Medallion architecture is a modern data warehouse design that organizes data processing into three layers: bronze (raw data), silver (cleaned data), and gold (business-ready data) [57][65] Group 3: Implementation Considerations - Effective demand analysis is essential for avoiding resource and time wastage, ensuring that the specific needs of the organization are clearly understood before starting a data architecture project [3][8] - The integration of data from various sources, such as ERP and CRM systems, requires careful planning and robust data control throughout the ETL process [4][6] - Documentation of the data model is crucial for ensuring that both technical teams and business users can easily adapt to the system, impacting the project's sustainability [5][6]

Core Viewpoint - The integration of AI and cloud computing is creating a new paradigm known as "AI cloud," which is driving significant growth in the cloud computing industry and reshaping its competitive landscape [1][2]. Group 1: AI and Cloud Computing Dynamics - In 2024, China is expected to add 50 large models with over 100 billion parameters, leading to a 74.1% year-on-year growth in intelligent computing power, significantly outpacing general computing power growth [1]. - AI is fundamentally altering the architecture and value logic of cloud services, pushing the demand for comprehensive end-to-end solutions rather than just basic AI computing infrastructure [1][9]. - The relationship between AI and cloud computing is redefining the boundaries of technology and business, creating a new starting line for competition in the cloud computing sector [1]. Group 2: Market Trends and Competitive Landscape - In 2020, the Chinese cloud computing market grew by 33.6% to reach 178.1 billion RMB, making it one of the fastest-growing regions globally, despite facing intense price wars and homogenization challenges [5]. - Major cloud providers like Amazon AWS, Microsoft Azure, and Google Cloud engaged in aggressive price cuts, with reductions of up to 60% in some services, intensifying competition [5][8]. - The shift from standardized cloud resources to customized solutions is seen as a potential remedy for the homogenization issue, although it has led to fragmentation challenges [8]. Group 3: Baidu's Strategic Positioning - Baidu has established a complete AI technology stack from hardware to applications, which is a competitive advantage not shared by its rivals [10][11]. - The "cloud-intelligence integration" strategy initiated by Baidu aims to transform the AI development paradigm and lead the evolution of the Chinese cloud computing industry [11]. - Baidu's intelligent cloud revenue grew by 27% year-on-year, with AI new business surpassing 10 billion RMB for the first time, indicating strong market performance [21]. Group 4: Future Outlook and Innovations - Baidu's intelligent cloud is set to upgrade its strategy to focus on intelligent-first AI cloud infrastructure, aiming to enhance the capabilities and penetration of AI technologies [18]. - The Baidu Baige AI heterogeneous computing platform has achieved significant performance improvements, allowing for efficient training and inference of large models [19]. - The introduction of the Agent-based AI development framework in Baidu's Qianfan platform aims to lower development barriers and accelerate application deployment across various industries [20].