DatriseAI-first ETL

Elasticsearch MySQL

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

How Datrise loads Elasticsearch into MySQL

Datrise syncs Elasticsearch's records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.

MySQL: Widely used OSS relational engine (InnoDB).

How Elasticsearch entities map to MySQL

Elasticsearch entityMySQL objectNotes
recordselasticsearch_recordsid PK · custom fields → JSON columns
eventselasticsearch_eventsDATETIME/TIMESTAMP events
configuration objectselasticsearch_configuration_objectsid PK · linked to elasticsearch_records

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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