Configuration-Driven No-Code Data Ingestion Engine with RuleBased Processing

Abstract

The efficient management of batch processes is critical for modern data-driven organizations, especially within the context of complex data ingestion and processing workflows. This abstract explores the essential components and functionalities required to streamline batch job execution within such environments. Firstly, a robust batch management system is essential, providing capabilities for job scheduling, dependency management, and error handling. Such a system orchestrates the execution of batch jobs, ensuring they run in the appropriate sequence and handle dependencies effectively. Additionally, real-time event-driven triggers enable immediate batch job initiation upon file drops, ensuring timely processing of incoming data and swift response to business needs. Conversely, scheduled batch executions provide predictability and automation, allowing organizations to optimize resource utilization and maintain operational efficiency by running batches at predetermined intervals or specific times. Furthermore, the abstract delves into the necessity of a comprehensive configuration setup to support various data formats, transformation requirements, and validation criteria. A rich configuration engine allows for seamless mapping, transformation, and validation of incoming data, enabling flexibility and adaptability to diverse data sources and requirements. It also emphasizes the importance of containerization for batch job execution, ensuring scalability, resource isolation, and efficient utilization of computing resources. Lastly, the provision of job IDs facilitates seamless invocation of batch jobs by external systems, enabling streamlined interaction and integration across different platforms. Together, these components form a cohesive framework for efficient batch job management, empowering organizations to optimize data processing workflows and derive actionable insights from their data assets.