Data Lakehouse Architectures: Bridging Structured and Unstructured Data

Abstract

The surge in data generation, spanning structured, semi-structured, and unstructured formats, challenges traditional data management frameworks. Data lakehouses, a hybrid architecture combining elements of data lakes and data warehouses, have emerged in 2023 as a promising paradigm to unify these diverse data types while enabling efficient analytics and governance. This paper explores recent advancements in data lakehouse architectures that seamlessly bridge the gap between structured and unstructured data. We analyze how modern lakehouse solutions integrate schema enforcement, metadata management, and ACID transaction capabilities traditionally associated with data warehouses, while retaining the scalability and flexibility of data lakes. Emphasis is placed on open-source implementations such as Delta Lake, Apache Iceberg, and Apache Hudi, which provide strong consistency and incremental processing for real-time analytics. Our research synthesizes findings from recent academic and industry publications, demonstrating how lakehouses enable efficient storage, query optimization, and governance across heterogeneous data. Key technical challenges include schema evolution, data quality management, query performance on unstructured datasets, and metadata scalability. Emerging strategies to address these involve hybrid storage formats, columnar data layouts, and ML-powered metadata indexing. Experimental insights highlight improvements in query latency, transactional consistency, and data lifecycle management, confirming lakehouses as versatile platforms for modern analytics workloads. Furthermore, the architecture supports multi-modal data pipelines by combining batch and streaming data processing, facilitating advanced use cases like AI model training and real-time business intelligence. In conclusion, data lakehouse architectures present a compelling solution to the growing complexity of data ecosystems, offering a unified platform that balances agility, reliability, and performance. Future research should focus on further optimizing metadata services, enhancing support for diverse data types, and extending governance frameworks to meet compliance requirements in increasingly regulated environments.