Home / Current Issue / Paper 1715610
Data Lakehouse Architectures in Modern Software Systems: Bridging Real-Time Streams and Analytical Intelligence
Subject area: Science,Engineering and Technology · Area of research: Software Engineering
DOI: https://doi.org/10.64388/IREV7I12-1715610
Abstract
The rapid growth of digital platforms has dramatically increased the volume, velocity, and variety of data generated by modern software systems. Organizations now collect large streams of information from user interactions, operational logs, sensor networks, and distributed applications. These data flows provide valuable opportunities for analytics and machine learning, yet they also create significant challenges for traditional data management architectures. Conventional data warehouses were designed primarily for structured analytical workloads, while data lakes emerged as scalable storage systems capable of accommodating large volumes of raw data. However, both approaches exhibit limitations when organizations attempt to combine real-time data processing with advanced analytics. In response to these challenges, the data lakehouse architecture has emerged as a unified data platform designed to bridge the gap between large-scale data storage and high-performance analytical processing. The lakehouse model integrates the scalability and flexibility of data lakes with the reliability, governance, and query capabilities traditionally associated with data warehouses. By combining these features, lakehouse systems enable organizations to process both streaming and historical data within a single architectural framework. This paper examines the architectural foundations of data lakehouse systems and explores how they support modern software platforms that require both real-time data processing and analytical intelligence. The study analyzes data ingestion pipelines, storage frameworks, metadata management systems, and analytical processing engines that collectively enable lakehouse architectures to function effectively. It also explores how these systems support machine learning workloads and large-scale data analytics within unified data environments. Through a comprehensive examination of lakehouse architectures, this research provides insights into how modern software systems can integrate streaming data pipelines with advanced analytics infrastructures. The findings highlight the importance of scalable storage systems, distributed processing frameworks, and robust data governance mechanisms in enabling unified data platforms capable of supporting next-generation data-driven applications.
Keywords
Data lakehouse architecture, real-time data processing, streaming analytics, distributed data systems, data engineering, modern data platforms
References
[1] Armbrust, M., Ghodsi, A., Xin, R., Zaharia, M., Franklin, M. J., Stoica, I., & Zaharia, M. (2021). Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics. CIDR Conference on Innovative Data Systems Research.
[2] Abadi, D. J. (2017). Query Execution in Column-Oriented Database Systems. Foundations and Trends in Databases, 7(2–3), 181–353.
[3] Akidau, T., Chernyak, S., & Lax, R. (2018). Streaming Systems: The What, Where, When, and How of Large-Scale Data Processing. Sebastopol, CA: O’Reilly Media.
[4] Chambers, B., & Zaharia, M. (2018). Spark: The Definitive Guide: Big Data Processing Made Simple. Sebastopol, CA: O’Reilly Media.
[5] Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107–113.
[6] Kleppmann, M. (2017). Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems. Sebastopol, CA: O’Reilly Media.
[7] Melnik, S., Gubarev, A., Long, J., Romer, G., Shivakumar, S., Tolton, M., & Vassilakis,
[8] T. (2010). Dremel: Interactive Analysis of Web-Scale Datasets. Proceedings of the VLDB Endowment, 3(1–2), 330–339.
[9] Stonebraker, M., Abadi, D. J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., Lau, E., Lin, A., Madden, S., O’Neil, E., O’Neil, P., Rasin, A., Tran, N., & Zdonik, S. (2005). C-Store: A Column-Oriented DBMS. Proceedings of the VLDB Conference, 553–564.
[10] Stonebraker, M., Çetintemel, U., & Zdonik, S. (2005). The 8 Requirements of Real-Time Stream Processing. ACM SIGMOD Record, 34(4), 42–47.
[11] Zaharia, M., Das, T., Li, H., Hunter, T., Shenker, S., & Stoica, I. (2013). Discretized Streams: Fault-Tolerant Streaming Computation at Scale. Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, 423–438.
[12] Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S., & Stoica, I. (2016). Apache Spark: A Unified Engine for Big Data Processing. Communications of the ACM, 59(11), 56–65.
How to cite this paper
@article{1715610,
author = {Yildirim Adiguzel},
title = {Data Lakehouse Architectures in Modern Software Systems: Bridging Real-Time Streams and Analytical Intelligence},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {12},
pages = {730-740},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715610.pdf},
abstract = {The rapid growth of digital platforms has dramatically increased the volume, velocity, and variety of data generated by modern software systems. Organizations now collect large streams of information from user interactions, operational logs, sensor networks, and distributed applications. These data flows provide valuable opportunities for analytics and machine learning, yet they also create significant challenges for traditional data management architectures. Conventional data warehouses were designed primarily for structured analytical workloads, while data lakes emerged as scalable storage systems capable of accommodating large volumes of raw data. However, both approaches exhibit limitations when organizations attempt to combine real-time data processing with advanced analytics. In response to these challenges, the data lakehouse architecture has emerged as a unified data platform designed to bridge the gap between large-scale data storage and high-performance analytical processing. The lakehouse model integrates the scalability and flexibility of data lakes with the reliability, governance, and query capabilities traditionally associated with data warehouses. By combining these features, lakehouse systems enable organizations to process both streaming and historical data within a single architectural framework. This paper examines the architectural foundations of data lakehouse systems and explores how they support modern software platforms that require both real-time data processing and analytical intelligence. The study analyzes data ingestion pipelines, storage frameworks, metadata management systems, and analytical processing engines that collectively enable lakehouse architectures to function effectively. It also explores how these systems support machine learning workloads and large-scale data analytics within unified data environments. Through a comprehensive examination of lakehouse architectures, this research provides insights into how modern software systems can integrate streaming data pipelines with advanced analytics infrastructures. The findings highlight the importance of scalable storage systems, distributed processing frameworks, and robust data governance mechanisms in enabling unified data platforms capable of supporting next-generation data-driven applications.},
keywords = {Data lakehouse architecture, real-time data processing, streaming analytics, distributed data systems, data engineering, modern data platforms},
month = {June},
doi = {https://doi.org/10.64388/IREV7I12-1715610}
}