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Elasticsearch

As explained in Kafka for Architects

Elasticsearch is a distributed search and analytics engine that indexes and queries structured and unstructured data.

Elasticsearch is a distributed search and analytics engine for indexing and querying structured or unstructured data. In centralized logging, applications emit standardized log events to Kafka, which transfers them to Elasticsearch for indexing and analysis. section 5.1.3

Elasticsearch handles large log volumes, indexes new data within seconds, and supports complex searches. Users access indexed events through its REST API or Kibana, which provides SQL-like queries and real-time dashboards. OpenSearch and OpenSearch Dashboards offer analogous alternatives. section 5.1.3

Figure 5.6 Conceptual flow for log data collection: log data is sent from the application to Kafka for processing, then indexed in Elasticsearch, and finally visualized in Kibana.
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Description automatically generated

Follow the conceptual flow from application log events through Kafka and Elasticsearch to Kibana visualization. section 5.1.3

How Kafka for Architects covers it

How deep it goes
Defines the high-level logging flow and user interfaces but stops short of implementation details.
Examples
Uses multi-service application logs, Kafka-to-Elasticsearch transfer, REST queries, and Kibana dashboards.
What it assumes
Assumes applications generate events and teaches how Kafka-mediated collection supports centralized log analysis.
What you can do after
Operate log analysis through Elasticsearch’s REST API or Kibana dashboards after events are indexed.
How it connects
Connects log events with Kafka, Kibana, REST APIs, OpenSearch, and centralized logging.

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