DatriseAI-first ETL

Nutshell Databricks SQL Warehouse

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

How Datrise loads Nutshell into Databricks SQL Warehouse

Datrise syncs Nutshell's pipeline records, activity history, and conversion-focused sales metrics 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

Nutshell: Sales CRM for teams that need simple reporting and pipeline velocity.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Nutshell entities map to Databricks SQL Warehouse

Nutshell entityDatabricks SQL Warehouse objectNotes
pipeline recordsnutshell_pipeline_recordsid PK · custom fields → VARIANT or STRUCT columns
activity historynutshell_activity_historyTIMESTAMP events
conversion-focused sales metricsnutshell_conversion_focused_sales_metricsid PK · linked to nutshell_pipeline_records

FAQ

How does Datrise handle Nutshell'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 Nutshell 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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