DatriseAI-first ETL

Streak Databricks SQL Warehouse

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

How Datrise loads Streak into Databricks SQL Warehouse

Datrise syncs Streak'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

Streak: CRM for SMB teams managing pipeline, contacts, and customer activity.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Streak entities map to Databricks SQL Warehouse

Streak entityDatabricks SQL Warehouse objectNotes
contactsstreak_contactsid PK · custom fields → VARIANT or STRUCT columns
accountsstreak_accountsid PK · linked to streak_contacts
dealsstreak_dealsid PK · linked to streak_contacts
activitiesstreak_activitiesTIMESTAMP events

FAQ

How does Datrise handle Streak'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 Streak 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

Early access

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