DatriseAI-first ETL

Fauna to Amazon S3 Data Lake Integration

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

How Datrise loads Fauna into Amazon S3 Data Lake

Datrise syncs Fauna's collections, documents, indexes, functions, database schemas, and document history 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

Fauna: Distributed document-relational database.

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

How Fauna entities map to Amazon S3 Data Lake

Fauna entityAmazon S3 Data Lake objectNotes
collectionsfauna_collectionsid PK · custom fields → nested struct/map fields in Parquet
documentsfauna_documentsid PK · linked to fauna_collections
indexesfauna_indexesid PK · linked to fauna_collections
functionsfauna_functionsid PK · linked to fauna_collections

FAQ

How does Datrise handle Fauna'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 Fauna 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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