DatriseAI-first ETL

Intercom Databricks SQL Warehouse

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

How Datrise loads Intercom into Databricks SQL Warehouse

Datrise syncs Intercom's conversations, customer attributes, inbox events, and support engagement 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

Intercom: Customer messaging platform with CRM-style account and conversation context.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Intercom entities map to Databricks SQL Warehouse

Intercom entityDatabricks SQL Warehouse objectNotes
conversationsintercom_conversationsid PK · custom fields → VARIANT or STRUCT columns
customer attributesintercom_customer_attributesid PK · linked to intercom_conversations
inbox eventsintercom_inbox_eventsTIMESTAMP events
support engagementintercom_support_engagementid PK · linked to intercom_conversations

FAQ

How does Datrise handle Intercom'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 Intercom 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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