Market Overview - The Low-code Development Platform Market is projected to grow from USD 26.30 billion in 2025 to USD 67.12 billion by 2030, with a CAGR of 20.61% during the forecast period [1] - Growth is driven by the demand for rapid application development, modernization of legacy systems, and increased adoption across various industries [1] Key Trends - **Cloud-First Deployment Models**: The shift towards cloud-first deployment models is enhancing flexibility and scalability, with cloud platforms becoming a significant part of low-code adoption due to reduced infrastructure costs and support for remote work [3] - **Integration of Artificial Intelligence**: AI integration in low-code platforms is streamlining development processes, enabling automation of repetitive tasks and empowering citizen developers, thus accelerating delivery timelines [4] - **Regulatory Compliance**: Compliance and regulatory demands are influencing low-code adoption, particularly in industries like BFSI and healthcare, where strong audit trails and adherence to data regulations are required [5][6] - **Modernization of Legacy Systems**: Enterprises are replacing outdated systems with low-code solutions to reduce maintenance costs and improve operational agility, highlighting the role of low-code platforms in maintaining competitive advantage [7] Market Segmentation - The market is segmented by component (platform and services), application type (web-based, mobile-based, desktop/server-based, API-centric & micro-services), deployment mode (cloud and on-premise), organization size (SMEs and large enterprises), and industry verticals including BFSI, healthcare, and IT [10] Key Players - Major players in the market include Salesforce.com Inc., Microsoft Corporation, Appian Corporation, Oracle Corporation, and OutSystems Inc., each offering unique low-code solutions tailored to various business needs [10][13] Conclusion - The Low-code Development Platform industry is experiencing robust growth as organizations seek faster and cost-effective software development options, with trends such as cloud-first models and AI-driven tools shaping the market [11][12]

Core Insights - The article emphasizes that the AI era presents significant opportunities for Oracle, particularly through the amplification of data advantages, as the concept of data has expanded to include multi-modal forms such as spatial, vector, text, and interpersonal relationships [1] - Oracle's cloud business is projected to grow from a 24% growth rate in FY25 to over 40% in FY26, with total revenue expected to reach $57.4 billion, attributed to over 40 years of data understanding and cloud transformation strategy [1] Database Fusion Necessity - The need for fusion databases arises from the challenges posed by traditional database solutions in the AI era, where using multiple heterogeneous databases complicates data integration beyond processing capabilities [3] - Without adopting fusion databases, organizations may face lengthy processes when extracting and integrating data from various sources, which can hinder machine learning training and overall efficiency [3] AI Integration Challenges - Many enterprises mistakenly treat AI projects as standalone initiatives rather than integrating them into the overall system architecture, leading to complexities that hinder AI integration [4] - The fusion of various data types and technology architectures is becoming a trend, with Oracle addressing this through an integrated architecture that supports the fusion of structured and unstructured data [4][5] Data Requirements and Security - The vast amount of data necessitates databases that support vector processing, with Oracle's GoldenGate technology enabling the integration of data across different databases [7] - In building Agent AI, focusing on data access needs and security is crucial, as most enterprise applications revolve around business data rather than communication data streams [8] AI Application Security - The importance of security in AI applications cannot be overstated, as the traditional three-tier architecture is challenged by the complexity of AI-generated code [9] - The phenomenon of "AI hallucination" can be mitigated by combining multi-disciplinary analyses with AI-generated content, potentially increasing accuracy from 70% to 90% in enterprise applications [9][10]