DatriseAI-first ETL

Apollo Databricks SQL Warehouse

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

How Datrise loads Apollo into Databricks SQL Warehouse

Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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

Apollo: Sales intelligence and engagement platform with account-level activity.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Apollo entities map to Databricks SQL Warehouse

Apollo entityDatabricks SQL Warehouse objectNotes
sales intelligence recordsapollo_sales_intelligence_recordsid PK · custom fields → VARIANT or STRUCT columns
account engagementapollo_account_engagementid PK · linked to apollo_sales_intelligence_records
outbound activityapollo_outbound_activityTIMESTAMP events

FAQ

How does Datrise handle Apollo'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 Apollo 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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