DatriseAI-first ETL

kvCORE to Amazon S3 Data Lake Integration

AI-first ETL from kvCORE into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads kvCORE into Amazon S3 Data Lake

Datrise syncs kvCORE's leads, contacts, listings, smart campaigns, property alerts, and agent activity into Amazon S3 Data Lake as columnar Parquet objects partitioned 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 and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

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

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How kvCORE entities map to Amazon S3 Data Lake

kvCORE entityAmazon S3 Data Lake 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 Amazon S3 Data Lake?

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 Amazon S3 Data Lake types.

How does the kvCORE to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

Connect kvCORE to Amazon S3 Data Lake the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.