Google PageSpeed Insights to Azure Data Lake Storage Integration
AI-first ETL from Google PageSpeed Insights into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google PageSpeed Insights into Azure Data Lake Storage
Datrise syncs Google PageSpeed Insights's audits, lab metrics, field metrics, opportunities, page URLs, and performance scores into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 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 to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.
Ideal for Azure lakehouse storage shared across Synapse and Databricks.
Endpoints
Google PageSpeed Insights: Page performance and Core Web Vitals audits.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Google PageSpeed Insights entities map to Azure Data Lake Storage
| Google PageSpeed Insights entity | Azure Data Lake Storage 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 Azure Data Lake Storage?
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 Azure Data Lake Storage types.
How does the Google PageSpeed Insights to Azure Data Lake Storage sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.
Related pipelines
More destinations for Google PageSpeed Insights
- Google PageSpeed Insights → Azure Synapse
- Google PageSpeed Insights → Spreadsheets
- Google PageSpeed Insights → Airtable
- Google PageSpeed Insights → CSV Files
- Google PageSpeed Insights → MongoDB
- Google PageSpeed Insights → Supabase
- Google PageSpeed Insights → Neon
- Google PageSpeed Insights → PlanetScale
- Google PageSpeed Insights → Amazon DynamoDB
- Google PageSpeed Insights → Looker
- Google PageSpeed Insights → Looker Studio
- Google PageSpeed Insights → Microsoft Power BI
More sources for Azure Data Lake Storage
- Greenhouse → Azure Data Lake Storage
- Gridly → Azure Data Lake Storage
- Harness → Azure Data Lake Storage
- Harvest Forecast → Azure Data Lake Storage
- Heap → Azure Data Lake Storage
- Hellobaton → Azure Data Lake Storage
- Heroku → Azure Data Lake Storage
- PostgreSQL → Azure Data Lake Storage
- Hubplanner → Azure Data Lake Storage
- Impact → Azure Data Lake Storage
- Import API → Azure Data Lake Storage
- Instagram → Azure Data Lake Storage
Early access
Connect Google PageSpeed Insights to Azure Data Lake Storage the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.