build data pipelines

Existing data engineering pipelines exhibit unstable data quality, delayed responsiveness, and poor fault tolerance in dynamic data environments, often degrading or failing due to data distribution shifts. To address these challenges, this paper proposes a three-level evolutionary data pipeline framework—progressing from *optimization* to *self-awareness* to *self-adaptation*—integrating operator composition optimization, online parameter tuning, real-time state monitoring, and feedback control. The framework enables autonomous pipeline diagnosis, dynamic parameter adjustment, and closed-loop environmental response. Its core innovation lies in transforming conventional static pipelines into intelligent systems endowed with perception–decision–execution capabilities. Experimental evaluation demonstrates significant improvements: data quality stability increases markedly, with error fluctuation reduced by 42%, and environmental adaptability is substantially enhanced. The framework establishes a deployable, automation-ready paradigm for next-generation data engineering.

In highly regulated domains such as finance, data quality control (QC) is often fragmented into isolated preprocessing steps, undermining end-to-end trustworthy AI pipelines. To address this, we propose the first AI-driven DataOps framework that embeds QC as a system-level core component. Our framework deeply integrates rule-based engines, statistical analysis, and custom AI-powered anomaly detection across the entire data lifecycle—from ingestion and transformation to model deployment—enabling dynamic remediation, policy-configurable workflows, and end-to-end auditability. Technically, it unifies data profiling, stream processing, cloud-native storage interfaces, and a proprietary AI detection module. Evaluated in a real-world financial production environment, the framework achieves significantly improved anomaly recall, reduces manual intervention by 42%, and ensures audit completeness and full data traceability under high-throughput conditions, fully satisfying regulatory compliance requirements.

Data cleaning remains highly manual, inefficient, and error-prone. This paper proposes the first goal-driven LLM-based framework for automatic workflow generation: given a dirty table and a target query, it end-to-end generates a minimal viable clean table along with executable cleaning steps—including deduplication, missing-value imputation, and format standardization. Our contributions are threefold: (1) We introduce the first benchmark dataset comprising annotated quadruples of (goal, dirty table, cleaning workflow, cleaned answer); (2) We design a zero-shot, multi-stage prompting framework—requiring no fine-tuning—that decomposes the task into goal column identification, data quality diagnosis, and operation-parameter generation; (3) We empirically validate that off-the-shelf LLMs possess inherent reasoning capabilities sufficient to generate high-quality, executable cleaning workflows across three major LLM families, significantly reducing human intervention.

Data standardization is critical in the data science lifecycle, yet existing tools (e.g., Pandas) require manual, error-prone coding, while LLM-based automation still demands expert prompt engineering and iterative interaction. To address this, we propose a declarative API-driven LLM-Agent framework that introduces *Dataprep.Clean*—a novel, column-type-aware standardization component enabling end-to-end automation via a single-line operation and one-shot natural language input. Our method integrates domain knowledge modeling with lightweight agent orchestration, eliminating programming prerequisites and enabling semantic cleaning of heterogeneous columns. Evaluated on real-world datasets, the approach achieves high accuracy and robustness across diverse standardization tasks. Deployed as an interactive web tool, it substantially lowers the barrier to entry for data practitioners. This work advances data preprocessing toward declarative, intelligent automation—bridging the gap between domain expertise and scalable, user-friendly tooling.

This work addresses the challenge of reconciling high throughput and low query latency in traditional ETL pipelines when processing continuously arriving fresh data, where unpredictable preprocessing operations often create bottlenecks. The authors propose Fluid ETL Pipelines, which introduce, for the first time, an elastic and non-blocking preprocessing mechanism that decouples data ingestion from transformation. By dynamically scheduling preprocessing tasks based on resource availability and user interest—without blocking data ingestion—and leveraging preemptible computing resources such as Amazon Spot instances, the approach significantly reduces operational costs. Experimental results demonstrate that Fluid ETL Pipelines substantially improve the efficiency of exploring fresh data, offering a novel direction for accelerating real-time queries and enabling adaptive preprocessing management.

This study addresses a critical gap in data cleaning research—the lack of large-scale, real-world dirty datasets that hinder the effective evaluation of methods in practical settings. To bridge this gap, the authors construct the first large-scale dirty dataset comprising real postal addresses paired with their ground-truth counterparts. Leveraging this dataset, they conduct a systematic benchmarking study of state-of-the-art data cleaning approaches. Their experiments reveal substantial limitations of current methods when applied to real-world data, underscoring the need for more robust and context-aware techniques. The dataset and empirical findings not only establish a reliable benchmark for future research but also provide actionable insights to guide the development of data cleaning solutions tailored to real-world applications.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

This work proposes an AI agent–driven workflow to address the high costs of reproducing large-scale empirical studies, which often stem from discrepancies in computational environments, code, and documentation. The approach decouples scientific reasoning from computational execution: researchers supply standardized diagnostic templates, and the system automatically retrieves and orchestrates reproduction materials within a version-controlled environment. A structured knowledge layer captures failure patterns, enabling adaptive reproduction across heterogeneous studies while ensuring transparency and stability of the analytical pipeline. Evaluated on 92 instrumental variable studies, the method achieves an 87% end-to-end reproduction success rate; when data and code are available, it attains 100% success at both the paper and model levels.

This work addresses the widespread irreproducibility of academic Jupyter Notebooks caused by environment drift, missing dependencies, and implicit execution assumptions. The authors propose the first web-oriented, automated reproducibility engineering pipeline that systematically reconstructs and evaluates repository-level execution environments for notebooks hosted on GitHub. By leveraging dependency inference, auto-generated Docker containers, and isolated execution, the pipeline enables large-scale assessment of reproducibility. A novel four-category execution outcome framework is introduced to quantify reproduction fidelity. Evaluation on 443 real-world notebooks shows that containerization resolves 66.7% of dependency-related failures; however, only 46.3% achieve high output fidelity, demonstrating that while containerization is necessary, it is insufficient for bit-for-bit reproducibility. These findings underscore the critical need for systematic reproducibility evaluation in computational research.