DatriseAI-first ETL

Elasticsearch to Looker Integration

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

How Datrise loads Elasticsearch into Looker

Datrise syncs Elasticsearch's indices, documents, mappings, fields, search queries, and aliases into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Elasticsearch: Search and analytics engine over document indices.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Elasticsearch entities map to Looker

Elasticsearch entityLooker objectNotes
indiceselasticsearch_indicesid PK · custom fields → flattened columns (nested fields expanded for modeling)
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 Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

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

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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