Adrien Grand discusses the suitability of Elasticsearch as a column store, particularly highlighting its strengths in storage efficiency and query performance due to its columnar data handling. While it does not serve as a general-purpose replacement for traditional column-stores, Elasticsearch leverages its indexing capabilities, particularly through doc values, to efficiently manage and query large datasets. Doc values, introduced in Lucene and later integrated into Elasticsearch, offer a low-memory, compressed, column-oriented view of data, made efficient by indexing at the document ID level. This allows Elasticsearch to excel in analytics on smaller subsets of data and supports features like sorting and aggregation, despite not being entirely space-efficient for sparse fields. The continuous development of Elasticsearch, with a focus on enhancing compression techniques and handling sparse data more efficiently, reflects its evolution from relying on memory-intensive fielddata to using doc values as the default columnar storage mechanism.