@article{espinoza2026from,
  title = {From Data Warehouses to the Lakehouse},
  author = {Felix ESPINOZA and Lea Nedomova and Milos MARYSKA},
  year = 2026,
  url = {https://ibimapublishing.com/articles/JAST/2026/558858/},
  journal = {Journal of Administrative Sciences and Technology},
  volume = 2026,
  pages = 7,
  doi = doi.org/10.5171/2026.558858,
  abstract = {This paper reviews the evolution of data architectures through the lens of enterprise architecture, focusing on the Data Warehouse (DWH), Data Lake (DL), and Data Lakehouse (DLH). It positions data architecture as a core element of enterprise architecture and outlines how frameworks such as Zachman, TOGAF, and the Gartner EA approach frame governance, integration, and strategic alignment of the data layer. Methodologically, the study follows a literature-based analysis and synthesis, aligned with the objective of describing the evolution of data architectures and providing a comparative view. The paper characterizes DWH as a structured, schema-on-write, multi-layer repository (stage/core/mart) with strong data quality but slower onboarding; DL as an object-storage approach emphasizing schema-on-read and ELT, high flexibility, and risks related to metadata/catalog management and transactional guarantees; and DLH as a hybrid that adds a metadata and transactional layer (ACID) while preserving DL flexibility, albeit with higher architectural complexity and skill demands. The paper also situates related concepts (modern cloud DWH, data fabric, data mesh) and presents a comparative table to summarize trade-offs. Overall, it offers a practitioner-oriented synthesis that clarifies where each paradigm fits and highlights the central role of governance and metadata stewardship in avoiding “data swamps” and sustaining value creation.},
  keywords = {data architecture; data warehouse; lakehouse; governance.},
  note = Article ID: 558858
}