Iterable → Amazon S3 Data Lake
AI-first ETL from Iterable into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Iterable into Amazon S3 Data Lake
Datrise syncs Iterable's users, campaigns, journeys, message events, and experiments 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
Iterable: Cross-channel marketing automation and journeys.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Iterable entities map to Amazon S3 Data Lake
| Iterable entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| users | iterable_users | id PK · custom fields → nested struct/map fields in Parquet |
| campaigns | iterable_campaigns | id PK · linked to iterable_users |
| journeys | iterable_journeys | id PK · linked to iterable_users |
| message events | iterable_message_events | ISO-8601 timestamp columns events |
FAQ
How does Datrise handle Iterable'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 Iterable 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.
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Early access
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