DatriseAI-first ETL

Source connector

PyPI connector

PyPI integration for databases, warehouses and BI tools

Python package registry metadata and downloads. Datrise captures PyPI entities such as packages, releases, versions, download counts, maintainers and dependencies, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.

Of the 6 objects, 6 sync as upserts keyed on the primary key and 0 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `pypi_packages`) with a schema derived from the PyPI API rather than a flattened CSV, in any of the 43 supported destinations.

Datrise is in early access: pick a destination below to open the PyPI pipeline page, and join the waitlist to get the PyPI connector with guided onboarding.

Incremental syncSchema-aware mapping43 destinations available

What PyPI data Datrise extracts

Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.

ObjectLanding tableSync mode
packagespypi_packagesUpsert by primary key, incremental on updated-at
releasespypi_releasesUpsert by primary key, incremental on updated-at
versionspypi_versionsUpsert by primary key, incremental on updated-at
download countspypi_download_countsUpsert by primary key, incremental on updated-at
maintainerspypi_maintainersUpsert by primary key, incremental on updated-at
dependenciespypi_dependenciesUpsert by primary key, incremental on updated-at

Popular destinations for PyPI

Choose a destination below to open the ready pipeline page.

  • A
    Airtable

    Relational spreadsheet destination for ops and go-to-market teams.

    View pipeline
  • A
    Amazon Athena

    Serverless SQL over S3 data lake tables.

    View pipeline
  • A
    Amazon DynamoDB

    Serverless key-value and document store on AWS.

    View pipeline
  • A
    Amazon QuickSight

    AWS serverless BI with SPICE and embedded analytics.

    View pipeline
  • A
    Amazon Redshift

    AWS petabyte-scale warehouse with Spectrum.

    View pipeline
  • A
    Amazon S3 Data Lake

    Object storage landing zone for parquet and snapshots.

    View pipeline
  • A
    Apache Superset

    Open-source BI for SQL exploration, charts, and dashboard publishing.

    View pipeline
  • A
    Azure Data Lake Storage

    ADLS Gen2 object storage for analytics workloads.

    View pipeline
  • A
    Azure Synapse

    Microsoft analytics workspace with SQL pools.

    View pipeline

PyPI connector FAQ

What PyPI data does the connector extract?
6 objects: packages, releases, versions, download counts, maintainers and dependencies. Each lands in its own table with types preserved; history-style objects (0) load as append-only streams.
Is the PyPI integration incremental?
Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
Where can I load PyPI data?
Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each PyPI → destination pair has its own page with the object-to-table mapping.

Related sources

Other sources in the same category, each with its own connector page.

Early access

Launch your PyPI pipeline with guided onboarding

Join the waitlist to get priority access for PyPI integrations with guided setup, schema-aware mapping, and production-grade incremental sync.