develop data connectors

This study addresses the proliferation of functional redundancy in service-oriented architectures caused by heterogeneous clients, which undermines system evolvability and maintainability. To mitigate this issue, the authors propose a novel reference architecture that synergistically integrates metadata-driven mechanisms with pattern languages. By leveraging metadata management and a plugin-based design, the approach effectively constrains service redundancy while enhancing reuse capabilities. The work innovatively combines metadata mechanisms and pattern languages in architectural construction and validates its efficacy through a triangulated evaluation method incorporating scenario-based assessment and real-world case studies. Empirical results demonstrate that the majority of system changes during evolution require no code modifications—only configuration adjustments or the addition of pluggable components—thereby significantly improving architectural stability and reuse efficiency.

Existing data pipelines often suffer from weak governance, leading to delayed schema validation, inconsistent cross-language execution, and misalignment with business semantics. This work proposes treating data contracts as types, leveraging the “everything-as-code” paradigm to inject schema annotations—encompassing column types, constraints, documentation, and lineage—into input and output tables within a lakehouse architecture via multi-language SDKs. These annotations are parsed across multiple phases of the execution lifecycle, deeply integrating data contracts into the type system. The approach enables both deterministic and non-deterministic reasoning over data flows across languages and execution engines, significantly enhancing the reliability of production data pipelines and ensuring consistent interoperability across systems.

Addressing the challenges of architectural design misalignment and empirically unsupported technology selection in large-scale, highly dynamic, multi-source heterogeneous data environments, this paper proposes a semi-automated data-intensive architecture design methodology. The approach comprises three foundational mechanisms: (1) a formal service-oriented application scenario description language; (2) an architecture description language (ADL) supporting mainstream paradigms—including stream processing, batch processing, and graph computation; and (3) a system taxonomy integrating functional and performance trade-offs. Leveraging formal modeling and case-driven validation, the methodology is evaluated across multiple real-world literature cases. Results demonstrate significant improvements in architecture customization accuracy and technology selection rationality, while simultaneously optimizing service quality and resource utilization efficiency.

本文探讨了通过整合数据工程和软件工程实践（如DataOps、MLOps等）来重塑面向数据和AI系统的软件开发生命周期，以应对传统SDLC在处理这些系统时遇到的挑战。

This study addresses the lack of systematic methodologies for selecting data architectures in modern organizations grappling with vast, heterogeneous data environments. To this end, it proposes the DATER conceptual framework, which establishes a unified taxonomy of technical requirements and systematically examines the historical evolution, core characteristics, and applicability boundaries of six prominent data architectures: data warehouses, data lakes, lakehouses, data fabrics, and data meshes. Through conceptual modeling and multidimensional comparative analysis, the framework clarifies overlaps and distinctions among these architectures, articulating their respective strengths and limitations. By offering a structured evaluation tool, DATER significantly enhances the strategic alignment and contextual appropriateness of data architecture design for both researchers and practitioners.

本文提出了一种基于仓库的实现方法，通过自动接口更新和一致性检查减少有人和无人飞行器软件开发中跨域不一致问题。

本文针对联邦环境中数据共享管道的冗余问题，提出通过系统复用现有数据资产和转换逻辑来简化设计与维护。

This work addresses the challenge of effectively evaluating the trade-offs between data consistency and coordination overhead among distributed transaction patterns—such as Saga and TCC—in business logic-intensive microservice systems prior to production deployment. The authors propose a lightweight microservice simulator grounded in Domain-Driven Design (DDD), which, for the first time, integrates DDD aggregate root modeling with multiple transaction models to decouple business logic from communication and transactional infrastructure. The framework supports configurable deployment topologies and network constraints, enabling seamless transitions from centralized to fully distributed architectures while providing a deterministic verification environment. Empirical evaluation on complex multi-aggregate systems quantifies the performance, coordination overhead, and resilience of different transaction models, substantially reducing development costs and facilitating left-shifted architectural validation.

研究通过定义spec-delta概念并设计实验，对比了基于spec-delta的数据治理方法与传统代码变更工作流在数据平台中的效果。

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.