Google Pagespeed Insights → Azure Data Lake Storage

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 records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.

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
records	`google_pagespeed_insights_records`	id PK · custom fields → nested struct/map fields in Parquet
events	`google_pagespeed_insights_events`	ISO-8601 timestamp columns events
configuration objects	`google_pagespeed_insights_configuration_objects`	id PK · linked to google_pagespeed_insights_records

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

More sources for Azure Data Lake Storage

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