DatriseAI-first ETL

Reply.io to Databricks SQL Warehouse Integration

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

How Datrise loads Reply.io into Databricks SQL Warehouse

Datrise syncs Reply.io's campaigns, prospects, sequences, email events, calls, and replies 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

Reply.io: Multichannel sales engagement sequences.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Reply.io entities map to Databricks SQL Warehouse

Reply.io entityDatabricks SQL Warehouse objectNotes
campaignsreply_io_campaignsid PK · custom fields → VARIANT or STRUCT columns
prospectsreply_io_prospectsid PK · linked to reply_io_campaigns
sequencesreply_io_sequencesid PK · linked to reply_io_campaigns
email eventsreply_io_email_eventsTIMESTAMP events

FAQ

How does Datrise handle Reply.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 Reply.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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