DatriseAI-first ETL

Google Pagespeed Insights Amazon Athena

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

How Datrise loads Google Pagespeed Insights into Amazon Athena

Datrise syncs Google Pagespeed Insights'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

Google Pagespeed Insights: SaaS or API data source for analytics and warehouse sync.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Google Pagespeed Insights entities map to Amazon Athena

Google Pagespeed Insights entityAmazon Athena objectNotes
recordsgoogle_pagespeed_insights_recordsid PK · custom fields → struct/map columns in Parquet
eventsgoogle_pagespeed_insights_eventstimestamp events
configuration objectsgoogle_pagespeed_insights_configuration_objectsid PK · linked to google_pagespeed_insights_records

FAQ

How does Datrise handle Google Pagespeed Insights'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 Google Pagespeed Insights 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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