DatriseAI-first ETL

Salesflare Databricks SQL Warehouse

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

How Datrise loads Salesflare into Databricks SQL Warehouse

Datrise syncs Salesflare's B2B account intelligence, interactions, and automated relationship timelines 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

Salesflare: B2B CRM focused on automation and relationship timeline data.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Salesflare entities map to Databricks SQL Warehouse

Salesflare entityDatabricks SQL Warehouse objectNotes
B2B account intelligencesalesflare_b2b_account_intelligenceid PK · custom fields → VARIANT or STRUCT columns
interactionssalesflare_interactionsid PK · linked to salesflare_b2b_account_intelligence
automated relationship timelinessalesflare_automated_relationship_timelinesTIMESTAMP events

FAQ

How does Datrise handle Salesflare'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 Salesflare 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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