DatriseAI-first ETL

Elasticsearch to Databricks SQL Warehouse Integration

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

How Datrise loads Elasticsearch into Databricks SQL Warehouse

Datrise syncs Elasticsearch's indices, documents, mappings, fields, search queries, and aliases into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Elasticsearch: Search and analytics engine over document indices.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Elasticsearch entities map to Databricks SQL Warehouse

Elasticsearch entityDatabricks SQL Warehouse objectNotes
indiceselasticsearch_indicesid PK · custom fields → VARIANT or STRUCT 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 Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Elasticsearch to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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