Serverless SQL: Querying Cloud Data Lakes with On-Demand Engines

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Areej Mustafa
Zilly Huma

Abstract

Serverless SQL engines are revolutionizing the way organizations query massive datasets stored in cloud data lakes by eliminating the need for persistent infrastructure and allowing on-demand, scalable data access. This paradigm enables users to run SQL queries directly on cloud-based storage systems like Amazon S3, Azure Data Lake, or Google Cloud Storage without provisioning servers or managing clusters. As businesses increasingly adopt cloud-native architectures, serverless SQL offers a compelling alternative to traditional data warehouses, particularly for exploratory analytics, ad-hoc reporting, and cost-efficient big data processing. This paper explores the principles, architecture, and use cases of serverless SQL engines, with a focus on their integration with data lake storage. It further examines performance considerations, data governance, cost models, and security strategies. By analyzing key technologies such as AWS Athena, Google BigQuery, and Azure Synapse Serverless, this paper highlights the opportunities and challenges associated with this modern approach to querying data at scale.

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How to Cite
Areej Mustafa, & Zilly Huma. (2025). Serverless SQL: Querying Cloud Data Lakes with On-Demand Engines. Pioneer Research Journal of Computing Science, 2(2), 170–178. Retrieved from http://prjcs.com/index.php/prjcs/article/view/81

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