DatriseAI-first ETL

kvCORE to Azure Data Lake Storage Integration

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

How Datrise loads kvCORE into Azure Data Lake Storage

Datrise syncs kvCORE's leads, contacts, listings, smart campaigns, property alerts, and agent activity 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

kvCORE: Real estate platform for lead generation and agent websites.

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

How kvCORE entities map to Azure Data Lake Storage

kvCORE entityAzure Data Lake Storage objectNotes
leadskvcore_leadsid PK · custom fields → nested struct/map fields in Parquet
contactskvcore_contactsid PK · linked to kvcore_leads
listingskvcore_listingsid PK · linked to kvcore_leads
smart campaignskvcore_smart_campaignsid PK · linked to kvcore_leads

FAQ

How does Datrise handle kvCORE'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 kvCORE 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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