Deep Lake: a Lakehouse for Deep Learning

Sasun, Hambardzumyan,; Tuli, Abhinav; Ghukasyan, Levon; Rahman, Fariz; Topchyan, Hrant; Isayan, David; Harutyunyan, Mikayel; Hakobyan, Tatevik; Stranic, Ivo; Buniatyan, Davit

doi:10.48550/arxiv.2209.10785

Cited by 1 publication

(1 citation statement)

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“…For instance, according to Schneider (13) conducted a comprehensive analysis of data lakehouse scalability, revealing key considerations for managing large-scale datasets efficiently. Furthermore, recent works by Orescanin (8) and Hambardzumyan et al (14) have delved into the integration of machine learning and data science capabilities within the context of data lakehouses, providing valuable insights into their potential for advanced analytics. Nevertheless, Begoli et al (15) presented a data-driven LH architecture for applications in biological research and health data analytics.…”

Section: Related Workmentioning

confidence: 99%

Data Lakehouse: Next Generation Information System

Cherradi,

El Haddadi

2024

Seminars in Medical Writing and Education

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This paper introduces the Data Lakehouse Architecture, a transformative model in data architecture that seamlessly integrates the analytical strengths of traditional data warehouses with the schema flexibility inherent in data lakes. Departing from current frameworks, this comprehensive approach establishes a unified platform, overcoming limitations of conventional data management. Addressing the critical need for an integrated solution, our primary objective is to set a new standard for sophisticated data management. The distinctiveness of our proposal lies in the seamless fusion of data warehouse analytics and data lake schema flexibility, underscoring its originality. The full article delves into the research methodology, providing a comprehensive understanding of the study's framework proposal. The foundational outcomes showcase the successful implementation of our Data Lakehouse Architecture, revealing enhanced processing capabilities for structured data analysis, complex querying, and high-performance reporting. The conclusion emphasizes the paradigm shift and transformative impact on data management practices, reinforcing the significance of our innovative solution. This research not only contributes a novel technological framework but also highlights the importance of adaptability and performance in the face of evolving data landscapes.

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