# GoogleCloudPlatform/professional-services

Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.

Repository: https://github.com/GoogleCloudPlatform/professional-services
Canonical: https://ross.abutalabs.com/products/professional-services
Language: Python
License: Apache-2.0
License Family: permissive
Topics: google-cloud-platform, google-cloud-dataflow, google-cloud-ml, google-cloud-compute, gke, bigquery, solutions, tools, examples
Last push: 2026-08-21T16:22:36+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 100
- inputs: {"age_days": 3394, "days_push": 12, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3065, forks 1469 (observed 2026-08-28T04:07:41.320898+00:00)

## What it is
A collection of example solutions, tools, and reference architectures developed by Google Cloud's Professional Services team, primarily in Python. It is not an officially supported Google product but serves as a reference for building on GCP products like BigQuery, Dataflow, and GKE.

## Use cases
- find example architectures for deploying on google cloud
- learn how to analyze reddit data in bigquery in realtime
- generate large synthetic datasets to stress-test bigquery
- automate bigquery schema and dataset management from the cli
- set up cicd on gke with gitlab
- build a pipeline to moderate audio content with ml apis
- detect anomalies in bigquery audit logs

## When to choose
- you want copyable reference implementations for common Google Cloud patterns
- you need starting points for BigQuery, Dataflow, or GKE solutions
- you are learning GCP through practical, production-oriented examples

## When to avoid
- you need officially supported, production-grade Google products with SLAs
- you are not using Google Cloud Platform
- you need a single cohesive tool rather than a grab-bag of independent examples

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools, etl, machine-learning, data-science, infrastructure-as-code, ci-cd
- domain: cloud-computing, big-data, machine-learning, developer-tools
- platform: python, cloud, cli
- tags: google-cloud-platform, bigquery, dataflow, gke, example-solutions, reference-architectures, sample-code, data-engineering, devops, docker, kubernetes

## Member repositories
- GoogleCloudPlatform/professional-services (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:41.320898+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:28:13.817356+00:00, confidence not recorded.
  - readme: https://github.com/GoogleCloudPlatform/professional-services (fetched 2026-08-28T04:07:41.320898+00:00, sha 70e2f068a280)
- Data as of 2026-08-30T08:39:29.467469+00:00.