DatriseAI-first ETL

Intruder to Amazon Athena Integration

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

How Datrise loads Intruder into Amazon Athena

Datrise syncs Intruder's scans, issues, targets, severities, occurrences, and remediation statuses 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

Intruder: Continuous vulnerability scanning for internet assets.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Intruder entities map to Amazon Athena

Intruder entityAmazon Athena objectNotes
scansintruder_scansid PK · custom fields → struct/map columns in Parquet
issuesintruder_issuesid PK · linked to intruder_scans
targetsintruder_targetsid PK · linked to intruder_scans
severitiesintruder_severitiesid PK · linked to intruder_scans

FAQ

How does Datrise handle Intruder'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 Intruder 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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