Abstract

As organizations increasingly shift towards cloud-native data platforms, balancing cost efficiency and query performance has become a central challenge for data engineering teams. Cloud data lakes, especially those built using Amazon S3 for storage and Snowflake for computing, offer immense scalability and flexibility—but at scale, they also expose inefficiencies that can silently drive up operational costs and hinder performance. This paper presents lessons learned from designing, maintaining, and optimizing large-scale data pipelines that process billions of records across Amazon S3 and Snowflake. Drawing from real-world implementation experience, we explore common pitfalls such as suboptimal file sizing, inefficient warehouse usage, and schema design flaws that directly impact cost and performance. We detail practical strategies to address these challenges, including S3 lifecycle management, Snowflake clustering, workload-aware warehouse sizing, and cost-conscious modeling in dbt. Beyond optimization techniques, this article emphasizes the role of data engineers in making architectural decisions that balance performance with budget constraints. The goal is to make pipelines faster or cheaper in isolation and to create sustainable, scalable data systems that deliver value to technical and business stakeholders. Through this exploration, we contribute actionable insights to the data engineering community navigating the evolving landscape of cloud data lakes.

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