DatriseAI-first ETL

Elasticsearch to CSV Files Integration

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

How Datrise loads Elasticsearch into CSV Files

Datrise syncs Elasticsearch's indices, documents, mappings, fields, search queries, and aliases into CSV Files as one CSV per source entity. Flexible or custom fields land in JSON-encoded strings for nested fields, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes a fresh, fully-typed CSV per entity each run, so re-runs update only what changed. Optional date-suffixed files for change tracking. CSV has no types, so Datrise emits a companion schema and quotes/escapes consistently so downstream loaders don't misparse commas and newlines.

Ideal for portable hand-off into any tool that ingests delimited files.

Endpoints

Elasticsearch: Search and analytics engine over document indices.

CSV Files: Flat-file destination for exports and lightweight data sharing.

How Elasticsearch entities map to CSV Files

Elasticsearch entityCSV Files objectNotes
indiceselasticsearch_indicesid PK · custom fields → JSON-encoded strings for nested fields
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 CSV Files?

Flexible values are stored as JSON-encoded strings for nested fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native CSV Files types.

How does the Elasticsearch to CSV Files sync stay up to date?

It runs incrementally — Datrise uses writes a fresh, fully-typed CSV per entity each run.

Related pipelines

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