DatriseAI-first ETL

Outreach Amazon S3 Data Lake

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

How Datrise loads Outreach into Amazon S3 Data Lake

Datrise syncs Outreach's sequence activity, pipeline execution metrics, and sales engagement 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

Outreach: Sales execution platform for sequence activity and pipeline outcomes.

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

How Outreach entities map to Amazon S3 Data Lake

Outreach entityAmazon S3 Data Lake objectNotes
sequence activityoutreach_sequence_activityISO-8601 timestamp columns events
pipeline execution metricsoutreach_pipeline_execution_metricsid PK · linked to outreach_sequence_activity
sales engagement eventsoutreach_sales_engagement_eventsISO-8601 timestamp columns events

FAQ

How does Datrise handle Outreach'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 Outreach 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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