Salesforce Marketing Cloud to Amazon S3 Data Lake Integration
AI-first ETL from Salesforce Marketing Cloud into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesforce Marketing Cloud into Amazon S3 Data Lake
Datrise syncs Salesforce Marketing Cloud's subscribers, data extensions, journeys, sends, email events, and lists 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
Salesforce Marketing Cloud: Enterprise email, journeys, and cross-channel messaging.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Salesforce Marketing Cloud entities map to Amazon S3 Data Lake
| Salesforce Marketing Cloud entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| subscribers | salesforce_marketing_cloud_subscribers | id PK · custom fields → nested struct/map fields in Parquet |
| data extensions | salesforce_marketing_cloud_data_extensions | id PK · linked to salesforce_marketing_cloud_subscribers |
| journeys | salesforce_marketing_cloud_journeys | id PK · linked to salesforce_marketing_cloud_subscribers |
| sends | salesforce_marketing_cloud_sends | id PK · linked to salesforce_marketing_cloud_subscribers |
FAQ
How does Datrise handle Salesforce Marketing Cloud'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 Salesforce Marketing Cloud 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
More destinations for Salesforce Marketing Cloud
- Salesforce Marketing Cloud → Azure Data Lake Storage
- Salesforce Marketing Cloud → Azure Synapse
- Salesforce Marketing Cloud → Spreadsheets
- Salesforce Marketing Cloud → Airtable
- Salesforce Marketing Cloud → CSV Files
- Salesforce Marketing Cloud → MongoDB
- Salesforce Marketing Cloud → Supabase
- Salesforce Marketing Cloud → Neon
- Salesforce Marketing Cloud → PlanetScale
- Salesforce Marketing Cloud → Amazon DynamoDB
- Salesforce Marketing Cloud → Looker
- Salesforce Marketing Cloud → Looker Studio
More sources for Amazon S3 Data Lake
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- Selligent → Amazon S3 Data Lake
- SendGrid → Amazon S3 Data Lake
- Sendwithus → Amazon S3 Data Lake
- Senseforce → Amazon S3 Data Lake
- Sentry → Amazon S3 Data Lake
- SFTP Bulk → Amazon S3 Data Lake
- Shiphero → Amazon S3 Data Lake
- Shippo → Amazon S3 Data Lake
- Short.io → Amazon S3 Data Lake
- Slack → Amazon S3 Data Lake
Early access
Connect Salesforce Marketing Cloud to Amazon S3 Data Lake the easy way
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