DatriseAI-first ETL

EspoCRM Databricks SQL Warehouse

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

How Datrise loads EspoCRM into Databricks SQL Warehouse

Datrise syncs EspoCRM's pipeline entities, custom objects, and process automation events 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

EspoCRM: Open-source CRM for pipeline management and custom entity modeling.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How EspoCRM entities map to Databricks SQL Warehouse

EspoCRM entityDatabricks SQL Warehouse objectNotes
pipeline entitiesespocrm_pipeline_entitiesid PK · custom fields → VARIANT or STRUCT columns
custom objectsespocrm_custom_objectsid PK · linked to espocrm_pipeline_entities
process automation eventsespocrm_process_automation_eventsTIMESTAMP events

FAQ

How does Datrise handle EspoCRM'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 EspoCRM 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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