The blog post delves into how Kibana, a data visualization tool, manages object persistence by storing saved searches, visualizations, dashboards, and other objects within Elasticsearch. These objects are stored as documents in a special index called .kibana, which is created when Kibana is first installed and started. The post explains how different types of documents are created in this index, such as config for storing version information, index patterns for user-defined data structures, and the specific fields each document type contains. It also covers the processes involved in saving searches, visualizations, and dashboards, including the fields stored in each type of document. The post highlights that these internal data structures may evolve, with future versions potentially offering REST APIs for safer manipulation. Additionally, it notes the current limitations and future-proofing measures in place, such as reserved schema version fields and non-implemented features like popularity tracking. The post aims to assist both in debugging and in deploying Kibana in a consistent and automated way, and it acknowledges the contributions of Rashid Khan and Spencer Alger in designing the document structures.