Evaluating Scalability and Performance in Data Lake Architectures: Opportunities and Challenges

Abstract

Data lakes have become a critical solution for managing the exponential growth of data, offering organizations scalable and flexible architectures for large-scale data processing. Unlike traditional storage systems, data lakes store raw data in its native format, providing greater adaptability and accommodating diverse data types. This paper analyzes the impact of data lake architectures on scalability and performance metrics, essential in the context of big data. It examines key features of data lakes, including schema-on-read, support for various data formats, and horizontal scalability, and contrasts them with traditional data warehouses. The study highlights the advantages of data lakes, such as faster data ingestion and efficient scalability to handle increasing data volumes. However, it also addresses challenges, particularly concerning query performance, resource utilization, and governance. Performance evaluation includes metrics such as ingestion speed, query performance, and resource usage. The paper explores common scalability issues, including performance degradation and governance challenges, and discusses optimization strategies like distributed query engines, partitioning, indexing, and dynamic resource allocation. While data lakes provide significant benefits for large-scale data processing, their success relies on effective management and optimization. The findings emphasize the importance of strong governance frameworks and continuous performance monitoring to ensure data lakes meet organizational processing requirements.