PyPI to Looker Studio Integration
AI-first ETL from PyPI into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads PyPI into Looker Studio
Datrise syncs PyPI's packages, releases, versions, download counts, maintainers, and dependencies into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
PyPI: Python package registry metadata and downloads.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How PyPI entities map to Looker Studio
| PyPI entity | Looker Studio object | Notes |
|---|---|---|
| packages | pypi_packages | id PK · custom fields → flattened columns for chart fields |
| releases | pypi_releases | id PK · linked to pypi_packages |
| versions | pypi_versions | id PK · linked to pypi_packages |
| download counts | pypi_download_counts | id PK · linked to pypi_packages |
FAQ
How does Datrise handle PyPI's custom fields in Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the PyPI to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect PyPI to Looker Studio the easy way
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