DatriseAI-first ETL

Rss Amazon Athena

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

How Datrise loads Rss into Amazon Athena

Datrise syncs Rss's records, events, and configuration objects 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

Rss: SaaS or API data source for analytics and warehouse sync.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Rss entities map to Amazon Athena

Rss entityAmazon Athena objectNotes
recordsrss_recordsid PK · custom fields → struct/map columns in Parquet
eventsrss_eventstimestamp events
configuration objectsrss_configuration_objectsid PK · linked to rss_records

FAQ

How does Datrise handle Rss'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 Rss 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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