DatriseAI-first ETL

Segment Amazon S3 Data Lake

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

How Datrise loads Segment into Amazon S3 Data Lake

Datrise syncs Segment's sources, destinations, track events, identify calls, and schema catalog 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

Segment: Customer data platform routing events to warehouses.

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

How Segment entities map to Amazon S3 Data Lake

Segment entityAmazon S3 Data Lake objectNotes
sourcessegment_sourcesid PK · custom fields → nested struct/map fields in Parquet
destinationssegment_destinationsid PK · linked to segment_sources
track eventssegment_track_eventsISO-8601 timestamp columns events
identify callssegment_identify_callsid PK · linked to segment_sources

FAQ

How does Datrise handle Segment'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 Segment 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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