Enterprise Data Lakehouse Adoption: Challenges, Solutions, and Best Practices

Pramod Raja Konda

Abstract


The rapid growth of enterprise data, combined with the need for real-time analytics and scalable data architectures, has positioned the lakehouse paradigm as a transformative solution for modern organizations. A data lakehouse integrates the reliability and schema governance of data warehouses with the flexibility and cost efficiency of data lakes, enabling unified storage, advanced analytics, and machine learning at scale. Despite its growing adoption, enterprises face significant challenges during implementation, including architectural complexity, data quality inconsistencies, governance limitations, integration issues, skill gaps, and migration risks from traditional systems. This paper examines the critical barriers enterprises encounter while transitioning to lakehouse environments and analyzes the emerging solutions that address these challenges. It explores best practices encompassing metadata-driven governance, multi-layered storage design, workload optimization, security automation, and cloud-native orchestration. By synthesizing insights from current industry frameworks and real-world deployments, the paper provides a comprehensive roadmap to guide organizations through successful adoption of the lakehouse model. The findings aim to support enterprises in achieving scalable, cost-effective, and AI-enabled data ecosystems that enhance business agility and innovation

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