Google PageSpeed Insights to Amazon S3 Data Lake Integration

How Datrise loads Google PageSpeed Insights into Amazon S3 Data Lake

Datrise syncs Google PageSpeed Insights's audits, lab metrics, field metrics, opportunities, page URLs, and performance scores into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

Google PageSpeed Insights: Page performance and Core Web Vitals audits.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Google PageSpeed Insights entities map to Amazon S3 Data Lake

Google PageSpeed Insights entity	Amazon S3 Data Lake object	Notes
audits	`google_pagespeed_insights_audits`	id PK · custom fields → nested struct/map fields in Parquet
lab metrics	`google_pagespeed_insights_lab_metrics`	id PK · linked to google_pagespeed_insights_audits
field metrics	`google_pagespeed_insights_field_metrics`	id PK · linked to google_pagespeed_insights_audits
opportunities	`google_pagespeed_insights_opportunities`	id PK · linked to google_pagespeed_insights_audits

FAQ

How does Datrise handle Google PageSpeed Insights's custom fields in Amazon S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the Google PageSpeed Insights to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

More destinations for Google PageSpeed Insights

More sources for Amazon S3 Data Lake

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