DatriseAI-first ETL

Elasticsearch to Chartio Integration

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

How Datrise loads Elasticsearch into Chartio

Datrise syncs Elasticsearch's indices, documents, mappings, fields, search queries, and aliases into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.

Ideal for drag-and-drop charting over a database.

Endpoints

Elasticsearch: Search and analytics engine over document indices.

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Elasticsearch entities map to Chartio

Elasticsearch entityChartio objectNotes
indiceselasticsearch_indicesid PK · custom fields → flattened columns for visual SQL
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 Chartio?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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