Gong to Amazon S3 Data Lake Integration

How Datrise loads Gong into Amazon S3 Data Lake

Datrise syncs Gong's calls, transcripts, deals, users, trackers, and call topics 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

Gong: Revenue intelligence from recorded sales calls.

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

How Gong entities map to Amazon S3 Data Lake

Gong entity	Amazon S3 Data Lake object	Notes
calls	`gong_calls`	id PK · custom fields → nested struct/map fields in Parquet
transcripts	`gong_transcripts`	id PK · linked to gong_calls
deals	`gong_deals`	id PK · linked to gong_calls
users	`gong_users`	id PK · linked to gong_calls

FAQ

How does Datrise handle Gong'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 Gong 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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