DatriseAI-first ETL

Marketo to Databricks SQL Warehouse Integration

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

How Datrise loads Marketo into Databricks SQL Warehouse

Datrise syncs Marketo's leads, programs, campaigns, email events, lists, and lead scores 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

Marketo: B2B marketing automation and lead management.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Marketo entities map to Databricks SQL Warehouse

Marketo entityDatabricks SQL Warehouse objectNotes
leadsmarketo_leadsid PK · custom fields → VARIANT or STRUCT columns
programsmarketo_programsid PK · linked to marketo_leads
campaignsmarketo_campaignsid PK · linked to marketo_leads
email eventsmarketo_email_eventsTIMESTAMP events

FAQ

How does Datrise handle Marketo'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 Marketo 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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