DatriseAI-first ETL

Mailchimp Amazon S3 Data Lake

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

How Datrise loads Mailchimp into Amazon S3 Data Lake

Datrise syncs Mailchimp's audiences, campaigns, automations, subscribers, and engagement metrics 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

Mailchimp: Email marketing platform with audiences and campaign analytics.

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

How Mailchimp entities map to Amazon S3 Data Lake

Mailchimp entityAmazon S3 Data Lake objectNotes
audiencesmailchimp_audiencesid PK · custom fields → nested struct/map fields in Parquet
campaignsmailchimp_campaignsid PK · linked to mailchimp_audiences
automationsmailchimp_automationsid PK · linked to mailchimp_audiences
subscribersmailchimp_subscribersid PK · linked to mailchimp_audiences

FAQ

How does Datrise handle Mailchimp'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 Mailchimp 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.

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