DatriseAI-first ETL

Practifi to Databricks SQL Warehouse Integration

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

How Datrise loads Practifi into Databricks SQL Warehouse

Datrise syncs Practifi's clients, households, entities, service requests, opportunities, and review meetings 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

Practifi: Salesforce-based platform for advisory firm operations.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Practifi entities map to Databricks SQL Warehouse

Practifi entityDatabricks SQL Warehouse objectNotes
clientspractifi_clientsid PK · custom fields → VARIANT or STRUCT columns
householdspractifi_householdsid PK · linked to practifi_clients
entitiespractifi_entitiesid PK · linked to practifi_clients
service requestspractifi_service_requestsid PK · linked to practifi_clients

FAQ

How does Datrise handle Practifi'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 Practifi 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.

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