DatriseAI-first ETL

Customer.io Databricks SQL Warehouse

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

How Datrise loads Customer.io into Databricks SQL Warehouse

Datrise syncs Customer.io's profiles, segments, campaigns, deliveries, and conversion 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

Customer.io: Messaging automation based on product and behavioral data.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Customer.io entities map to Databricks SQL Warehouse

Customer.io entityDatabricks SQL Warehouse objectNotes
profilescustomer_io_profilesid PK · custom fields → VARIANT or STRUCT columns
segmentscustomer_io_segmentsid PK · linked to customer_io_profiles
campaignscustomer_io_campaignsid PK · linked to customer_io_profiles
deliveriescustomer_io_deliveriesid PK · linked to customer_io_profiles

FAQ

How does Datrise handle Customer.io'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 Customer.io 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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