Codat to Databricks SQL Warehouse Integration

How Datrise loads Codat into Databricks SQL Warehouse

Datrise syncs Codat's companies, accounts, invoices, bills, journal entries, and payments 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

Codat: Unified API for accounting and banking data.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Codat entities map to Databricks SQL Warehouse

Codat entity	Databricks SQL Warehouse object	Notes
companies	`codat_companies`	id PK · custom fields → VARIANT or STRUCT columns
accounts	`codat_accounts`	id PK · linked to codat_companies
invoices	`codat_invoices`	id PK · linked to codat_companies
bills	`codat_bills`	id PK · linked to codat_companies

FAQ

How does Datrise handle Codat'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 Codat 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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