DatriseAI-first ETL

PipeRun Databricks SQL Warehouse

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

How Datrise loads PipeRun into Databricks SQL Warehouse

Datrise syncs PipeRun's contacts, accounts, deals, activities, and lifecycle 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

PipeRun: CRM widely used in Latin America for sales pipeline and customer ops.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How PipeRun entities map to Databricks SQL Warehouse

PipeRun entityDatabricks SQL Warehouse objectNotes
contactspiperun_contactsid PK · custom fields → VARIANT or STRUCT columns
accountspiperun_accountsid PK · linked to piperun_contacts
dealspiperun_dealsid PK · linked to piperun_contacts
activitiespiperun_activitiesTIMESTAMP events

FAQ

How does Datrise handle PipeRun'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 PipeRun 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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