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Informative Article | Science and Technology | India | Volume 8 Issue 5, May 2019
Efficient File-Based Data Ingestion for Cloud Analytics: A Framework for Extracting and Converting Non-Traditional Data Sources
Abstract: In the rapidly evolving landscape of cloud computing and big data analytics, efficiently processing and analyzing diverse data formats is crucial for business decision-making. This paper introduces a comprehensive framework designed for the efficient ingestion of non-traditional data sources, specifically XML, PDF, and JSON files, into cloud analytics platforms. By converting these varied formats into structured CSV data, the framework significantly simplifies data analysis tasks, enhancing the utility of valuable customer data. Key features include a multi-layered architecture with specialized processing for each data type, a caching system for improved efficiency, and robust concurrency control for maintaining data integrity in multi-user environments. While highly effective in handling diverse data formats, the framework encounters challenges with complex nested structures and dependency on third-party libraries. Future enhancements focus on refining processing algorithms, reducing dependencies, and expanding capabilities for real-time processing and integration with big data platforms. This innovative approach to data ingestion addresses the pressing need for scalability and adaptability in cloud analytics, aligning with the ongoing digital transformation and the increasing reliance on comprehensive data analytics in various industries.
Keywords: Cloud Analytics, Data Ingestion, Data Transformation, Non-Traditional Data Sources, PDF Processing, Scalability, Unstructured Data
Edition: Volume 8 Issue 5, May 2019,
Pages: 2223 - 2227