Apify to Amazon Athena Integration

How Datrise loads Apify into Amazon Athena

Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs 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

Apify: Web scraping and browser automation platform.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Apify entities map to Amazon Athena

Apify entity	Amazon Athena object	Notes
actors	`apify_actors`	id PK · custom fields → struct/map columns in Parquet
runs	`apify_runs`	id PK · linked to apify_actors
datasets	`apify_datasets`	id PK · linked to apify_actors
key-value stores	`apify_key_value_stores`	id PK · linked to apify_actors

FAQ

How does Datrise handle Apify'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 Apify 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

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