Elasticsearch's point-in-time (PIT) reader, introduced in version 7.10, provides a method to query data as it existed at a specific moment, offering a consistent view across multiple queries, unlike the scroll API, which is less efficient for deep pagination and relies on a single query context. While the scroll API temporarily holds data in a frozen state to ensure consistency during a search, it requires resource management and is not ideal for dynamic datasets. PIT, on the other hand, allows users to execute various queries against a fixed dataset in time, making it particularly useful for scenarios requiring stateful queries, such as using the Event Query Language (EQL) for time series data. It achieves this by decoupling the search request from the creation of the PIT, which is managed through an API that maintains the data's consistency across search requests, even if modifications like deletions occur after the PIT is established. Although PIT is advantageous for maintaining data consistency, especially in analytics, it requires careful resource management to avoid excessive load on frequently updated indices, with recommendations to periodically refresh the PIT id to balance resource usage and data freshness. Future enhancements are anticipated, including integration with slice queries to further optimize data retrieval.