Data Lakes vs. Data Warehouses in Library Analytics: A Strategic Comparison

Abstract

This paper examines the key similarities and differences between data lakes and data warehouses in relation to library analytics. With libraries beginning to embrace data-informed cultures, it is important to understand the potential benefits and challenges of each data architecture to select the best fit. Data lakes are known for the easy, scalable storage of unrelated, unstructured, and/or semi-structured data for analysis and machine learning applications. Data lakes are also capable of supporting real-time exploratory analytics and can merge different types of data, such as user interactions and content, as well as available data from social media. One of the challenges of a data lake is the requirement of knowledge and expertise on data governance, or the potential risk of becoming a "data swamp," otherwise known as unorganized data with no context or metadata. Conversely, data warehouses are a structured, optimized storage solution for clean, organized data. Data warehouses are ideal for some reporting solutions, tracking performance, and analyzing historical trends. They exceed query performance and reliability for everyday data functionalities but may lack flexibility for unstructured data or real-time analytics, the paper analyzes data warehouses and data lakes according to cost, scalability, governance, and usability, the analysis finds data lakes are more suited for libraries emphasizing innovation and research, while data warehouses remain prepared choices and practical implementation strategy for libraries emphasizing operational efficiency and standardized reporting. This comparison provides insights that assist library directors and decision-makers in aligning data and business intelligence strategies with institutional priorities and technological infrastructure.