DatriseAI-first ETL

Keap Azure Data Lake Storage

AI-first ETL from Keap into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads Keap into Azure Data Lake Storage

Datrise syncs Keap's SMB contacts, opportunities, automations, and appointment workflows into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

Keap: SMB CRM with pipeline automation, email, and appointment flows.

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How Keap entities map to Azure Data Lake Storage

Keap entityAzure Data Lake Storage objectNotes
SMB contactskeap_smb_contactsid PK · custom fields → nested struct/map fields in Parquet
opportunitieskeap_opportunitiesid PK · linked to keap_smb_contacts
automationskeap_automationsid PK · linked to keap_smb_contacts
appointment workflowskeap_appointment_workflowsid PK · linked to keap_smb_contacts

FAQ

How does Datrise handle Keap's custom fields in Azure Data Lake Storage?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.

How does the Keap to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

Related pipelines

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