Optimizing Data Ingestion and Processing: A Study of Snowpipe Streaming and Data Lake Architectures

Abstract

Data ingestion and processing are critical components in modern data management systems, particularly in the context of real-time and near-real-time analytics. This paper explores the optimization of data ingestion and processing through a comparative study of Snowpipe Streaming and Data Lake architectures. Snowpipe Streaming, a cloud-native service provided by Snowflake, offers a seamless and scalable solution for ingesting and processing streaming data. On the other hand, Data Lake architectures, which are highly flexible and cost-effective, provide a robust framework for storing and processing large volumes of diverse data. This study evaluates the performance, scalability, cost, and ease of use of both approaches, providing insights into their strengths and weaknesses. We also present a detailed algorithm for optimizing data ingestion and processing in a Data Lake architecture, along with empirical results from a series of experiments. The findings of this study can help organizations make informed decisions when choosing the most suitable data ingestion and processing solution for their specific needs