Automating Data Pipelines with AI: From Data Engineering to Intelligent Systems

Abstract

In the era of big data, the automation of data pipelines has become pivotal for efficient data management and actionable insights. This paper explores the integration of Artificial Intelligence (AI) into data engineering workflows, focusing on the transition from traditional data engineering methods to intelligent systems that leverage AI for automation. We propose a framework that integrates AI-driven techniques for data ingestion, processing, and analysis, aiming to enhance efficiency, accuracy, and scalability in data pipeline management. By employing machine learning algorithms, natural language processing, and automated data cleansing tools, the framework is designed to handle complex and voluminous data sources with minimal human intervention. We present case studies demonstrating the practical application of this framework in various sectors, including finance, healthcare, and e-commerce, highlighting its impact on reducing manual effort, improving data quality, and accelerating decision-making processes. The results indicate that AIpowered automation can significantly streamline data workflows, making them more adaptive to dynamic data environments and capable of supporting real-time analytics. This paper contributes to the field by providing a comprehensive overview of AI applications in data engineering and proposing best practices for implementing intelligent data pipelines.