DatriseAI-first ETL

Real Geeks Amazon S3 Data Lake

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

How Datrise loads Real Geeks into Amazon S3 Data Lake

Datrise syncs Real Geeks's contacts, accounts, deals, activities, and lifecycle events 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

Real Geeks: Real estate CRM for leads, listings, and agent follow-up.

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

How Real Geeks entities map to Amazon S3 Data Lake

Real Geeks entityAmazon S3 Data Lake objectNotes
contactsreal_geeks_contactsid PK · custom fields → nested struct/map fields in Parquet
accountsreal_geeks_accountsid PK · linked to real_geeks_contacts
dealsreal_geeks_dealsid PK · linked to real_geeks_contacts
activitiesreal_geeks_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle Real Geeks'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 Real Geeks 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

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