Data Lakehouse for Time Series Data: A Systematic Literature Review

Abstract

As data continues to grow exponentially, the fields of data management and analytics must evolve to ensure efficient data ingestion, knowledge extraction, and scalability. The Data Lakehouse architecture, which combines the best features of Data Warehouses and Data Lakes, has emerged as a potential solution. However, to fully leverage the capabilities of Data Lakehouses for time series data, it is crucial to understand the unique challenges and opportunities they present. This literature review examines proposed Data Lakehouse architectures specifically for time series data, exploring their implementation, the software technologies used, and potential real-world applications. The focus is on comparing these architectures to identify the most suitable technologies for similar implementations. Through an in-depth analysis, this study emphasizes the importance of optimizing configurations to enhance system performance and scalability, particularly for data analysis and artificial intelligence (AI) workloads.