This blog post, co-authored by Christian Dahlqvist and Peter Kim, explores the storage requirements for Elasticsearch through a series of tests analyzing different configurations. The authors discuss how deployment sizes can vary significantly based on use cases, and they emphasize the importance of testing with representative data to estimate disk needs accurately. Key factors that influence disk space include whether fields are analyzed or not, the use of the "_all" field, and the enablement of doc values, each affecting how data is indexed and stored. The post highlights that the storage footprint can range widely depending on these settings, with structured data generally allowing for better compression than semi-structured data. The authors stress the importance of configuring Elasticsearch mapping intentionally, especially for larger deployments, as this can impact storage needs significantly. They also note that while Elasticsearch employs compression, it is designed to minimize query latency. The blog concludes by acknowledging common misconceptions about disk space in ELK-based solutions and encourages further exploration of hardware requirements beyond just storage.