Why did you choose Elasticsearch? What other options were there? Did you choose something or was ELK already there?

The choice of Elasticsearch is usually driven by its full-text search capabilities, scalability, and flexibility in working with unstructured data. In projects where fast search over large volumes of logs or documents is required, Elasticsearch often becomes the optimal solution.

Alternatives could include:

• Solr — also a search engine based on Lucene, but with more complex configuration.
• Databases supporting full-text search (e.g., PostgreSQL with pg_trgm).
• Specialized logging systems, such as Graylog or Splunk.

If the project already used the ELK stack (Elasticsearch, Logstash, Kibana), then choosing Elasticsearch was natural, as it integrates well with the other components and provides powerful data search and visualization.