The article explores the process of using Elasticsearch's Ingest Node to parse a CSV file containing New York City's subway station data, transforming it into a structured JSON format to be indexed and visualized using Kibana. The example uses an open dataset of subway entrances and exits, illustrating how Ingest Node can replace Logstash for simpler data ingestion tasks without the need for additional software. It details setting up an Elastic Cloud cluster, manipulating CSV data, and using a Grok processor to parse and break down unstructured lines into structured fields. The process includes creating an index template with specific field mappings to facilitate document filtering and geo-location queries. After indexing, the data is visualized in Kibana, where users can create a Tile Map to display station locations and utilize various visualizations to answer questions about station amenities, such as the presence of elevators. The article emphasizes the ease of starting from scratch with Elasticsearch and Kibana to turn a text file into insightful visualizations.