DatriseAI-first ETL

Salesloft Amazon S3 Data Lake

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

How Datrise loads Salesloft into Amazon S3 Data Lake

Datrise syncs Salesloft's cadence activity, conversation signals, and revenue workflow execution 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

Salesloft: Revenue workflow platform for cadences, conversations, and coaching signals.

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

How Salesloft entities map to Amazon S3 Data Lake

Salesloft entityAmazon S3 Data Lake objectNotes
cadence activitysalesloft_cadence_activityISO-8601 timestamp columns events
conversation signalssalesloft_conversation_signalsid PK · linked to salesloft_cadence_activity
revenue workflow executionsalesloft_revenue_workflow_executionid PK · linked to salesloft_cadence_activity

FAQ

How does Datrise handle Salesloft'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 Salesloft 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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