DatriseAI-first ETL

Contentful to Amazon Athena Integration

AI-first ETL from Contentful into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads Contentful into Amazon Athena

Datrise syncs Contentful's entries, content types, assets, spaces, locales, and publish events into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Contentful: Headless CMS for structured content delivery.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Contentful entities map to Amazon Athena

Contentful entityAmazon Athena objectNotes
entriescontentful_entriesid PK · custom fields → struct/map columns in Parquet
content typescontentful_content_typesid PK · linked to contentful_entries
assetscontentful_assetsid PK · linked to contentful_entries
spacescontentful_spacesid PK · linked to contentful_entries

FAQ

How does Datrise handle Contentful's custom fields in Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Contentful to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

Early access

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