Optimizing ETL Processes in Data Pipelines for High-Volume Cybersecurity Data Streams

Abstract

In the domain of cybersecurity, the ability to efficiently process and analyze high-volume data streams is critical for threat detection and response. This paper explores the optimization of Extract, Transform, Load (ETL) processes in data pipelines specifically designed to handle large-scale cybersecurity data. We investigate various strategies for improving ETL performance, including data ingestion techniques, transformation optimization, and loading strategies. Our findings provide insights into best practices and propose a framework for enhancing ETL efficiency in the context of cybersecurity.