Data Lakehouse Architectures: Integrating Traditional ETL with Modern Cloud-Native Frameworks
Sampath Kumar Nitchenametla
Senior Solution Delivery Lead, Deloitte USI Pvt Ltd.
Abstract
A data Lakehouse architecture merges the best of data lakes and data warehouses to bring numerous advantages while working with both structured and unstructured data in modern analytics settings. This paper examines how to combine traditional ETL pipelines with cloud-native frameworks for faster, cheaper, and more flexible data processing. It discusses the evolution of ETL workflows and highlights open formats, separation of storage and compute architectures, and modern cloud platforms such as AWS, Microsoft Azure, and Google Cloud. Through architectural illustrations, case studies, and comparative analysis, this study demonstrates how organizations can modernize their existing data infrastructure while preserving prior investments. The research also explores how to build an enterprise-grade, scalable, resilient, and high-performance data Lakehouse ecosystem while addressing emerging challenges and future technological trends.
Keywords
Data Lakehouse, ETL, Cloud-Native, Data Engineering, Data Lakes, Data Warehousing, Delta Lake, Apache Iceberg, Big Data, Modern Data Stack.
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