The blog post by Alex Marquardt addresses the issue of duplicate documents in Elasticsearch, particularly when auto-generated IDs are used, leading to the same document being stored multiple times with different IDs. To tackle this, the post outlines two methods for detecting and removing duplicates. The first method utilizes Logstash, employing a fingerprint filter to generate unique IDs based on specific field values, thereby preventing duplicates by ensuring documents with identical content share the same ID. The second method involves a custom Python script that calculates hashes for specified fields and uses these hashes to identify duplicates by storing document IDs in a dictionary. This approach is memory-efficient and can be enhanced to handle time-series data more effectively by processing subsets of documents within specific time frames. The blog also considers potential issues like hash collisions and provides insights into optimizing deduplication processes for Elasticsearch users.