Keep clickhouse integration elastic stack queryable

Elasticsearch and Kibana are positioned as the search and operational interface for Elastic data streams, while ClickHouse is presented as a complementary columnar analytics store for long-retention SQL queries, joins, rollups, and APIs. The recommended architecture fans out events at collection time through Elastic Agent, Beats, Logstash, or Kafka rather than repeatedly scrolling Elasticsearch, with Elasticsearch retained for Kibana and security workflows and ClickHouse receiving a parallel stream. Successful integration depends on mapping Elastic Common Schema fields into typed ClickHouse MergeTree columns, partitioning by event time, sorting around common analytical filters such as service and log level, retaining raw JSON for less common fields, and applying TTL policies. The guidance covers Kafka-based ingestion, Logstash dual outputs, controlled Elasticsearch backfills, consumer lag and failure handling, quarantine tables for malformed events, persistent queues, and parity and latency tests before deployment. It also advises using ClickHouse for cross-domain joins and long-range aggregations while keeping Kibana disconnected from ClickHouse, and describes Tinybird as a managed ClickHouse option that can consume the same event fan-out and expose SQL-backed APIs without operating consumer and merge infrastructure.

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