DatriseAI-first ETL

Wealthbox Amazon S3 Data Lake

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

How Datrise loads Wealthbox into Amazon S3 Data Lake

Datrise syncs Wealthbox'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

Wealthbox: Financial advisor CRM for clients, households, and compliance workflows.

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

How Wealthbox entities map to Amazon S3 Data Lake

Wealthbox entityAmazon S3 Data Lake objectNotes
contactswealthbox_contactsid PK · custom fields → nested struct/map fields in Parquet
accountswealthbox_accountsid PK · linked to wealthbox_contacts
dealswealthbox_dealsid PK · linked to wealthbox_contacts
activitieswealthbox_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle Wealthbox'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 Wealthbox 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

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