DatriseAI-first ETL

Elasticsearch to MySQL Integration

AI-first ETL from Elasticsearch into MySQL. Governed entities, incremental sync, typed landing tables.

How Datrise loads Elasticsearch into MySQL

Datrise syncs Elasticsearch's indices, documents, mappings, fields, search queries, and aliases into MySQL as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME/TIMESTAMP.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Optional RANGE partitioning by load date. MySQL collation matters for CRM text, so Datrise lands utf8mb4 to preserve emoji and non-Latin characters.

Ideal for operational reporting and app databases already standardized on MySQL.

Endpoints

Elasticsearch: Search and analytics engine over document indices.

MySQL: Widely used OSS relational engine (InnoDB).

How Elasticsearch entities map to MySQL

Elasticsearch entityMySQL objectNotes
indiceselasticsearch_indicesid PK · custom fields → JSON columns
documentselasticsearch_documentsid PK · linked to elasticsearch_indices
mappingselasticsearch_mappingsid PK · linked to elasticsearch_indices
fieldselasticsearch_fieldsid PK · linked to elasticsearch_indices

FAQ

How does Datrise handle Elasticsearch's custom fields in MySQL?

Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native MySQL types.

How does the Elasticsearch to MySQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.

Related pipelines

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