DatriseAI-first ETL

Clay to Amazon Athena Integration

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

How Datrise loads Clay into Amazon Athena

Datrise syncs Clay's tables, rows, enrichment runs, waterfalls, data sources, and exported lists 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

Clay: Data enrichment tables that build outbound lists.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Clay entities map to Amazon Athena

Clay entityAmazon Athena objectNotes
tablesclay_tablesid PK · custom fields → struct/map columns in Parquet
rowsclay_rowsid PK · linked to clay_tables
enrichment runsclay_enrichment_runsid PK · linked to clay_tables
waterfallsclay_waterfallsid PK · linked to clay_tables

FAQ

How does Datrise handle Clay'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 Clay 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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