Data Lakehouse Architecture: Merging Data Warehousing and Data Lake Approaches for Cybersecurity Analytics

Abstract

The increasing complexity of cybersecurity threats necessitates advanced analytical capabilities to effectively detect and mitigate risks. Traditional data warehousing solutions and modern data lake approaches each offer distinct advantages, but their integration can provide a more comprehensive framework for cybersecurity analytics. This paper explores the concept of Data Lakehouse Architecture, a hybrid model that combines the strengths of data warehousing and data lakes. By examining the architectural components, integration strategies, and use cases in cybersecurity, this research highlights how a data lakehouse can enhance threat detection, incident response, and overall security posture.