DatriseAI-first ETL

Webflow to Amazon S3 Data Lake Integration

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

How Datrise loads Webflow into Amazon S3 Data Lake

Datrise syncs Webflow's collections, collection items, sites, fields, form submissions, and publish events 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

Webflow: Visual website builder with a structured CMS.

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

How Webflow entities map to Amazon S3 Data Lake

Webflow entityAmazon S3 Data Lake objectNotes
collectionswebflow_collectionsid PK · custom fields → nested struct/map fields in Parquet
collection itemswebflow_collection_itemsid PK · linked to webflow_collections
siteswebflow_sitesid PK · linked to webflow_collections
fieldswebflow_fieldsid PK · linked to webflow_collections

FAQ

How does Datrise handle Webflow'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 Webflow 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

Early access

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