DatriseAI-first ETL

ChartMogul to Databricks SQL Warehouse Integration

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

How Datrise loads ChartMogul into Databricks SQL Warehouse

Datrise syncs ChartMogul's customers, subscriptions, invoices, MRR movements, plans, and churn 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

ChartMogul: Subscription analytics and revenue reporting.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How ChartMogul entities map to Databricks SQL Warehouse

ChartMogul entityDatabricks SQL Warehouse objectNotes
customerschartmogul_customersid PK · custom fields → VARIANT or STRUCT columns
subscriptionschartmogul_subscriptionsid PK · linked to chartmogul_customers
invoiceschartmogul_invoicesid PK · linked to chartmogul_customers
MRR movementschartmogul_mrr_movementsTIMESTAMP events

FAQ

How does Datrise handle ChartMogul'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 ChartMogul 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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