# GoogleCloudPlatform/DataflowTemplates

Cloud Dataflow Google-provided templates for solving in-Cloud data tasks

Repository: https://github.com/GoogleCloudPlatform/DataflowTemplates
Canonical: https://ross.abutalabs.com/products/dataflowtemplates
Homepage: https://cloud.google.com/dataflow/docs/guides/templates/provided-templates
Language: Java
License: Apache-2.0
License Family: permissive
Topics: apache-beam, dataflow-templates, google-cloud-dataflow, google-cloud-storage, google-cloud-spanner, bigquery, bigtable
Last push: 2026-08-26T15:41:15+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 3127, "days_push": 7, "days_rel": 7, "gap_med": 6, "n_releases_24m": 100}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1310, forks 1111 (observed 2026-08-28T04:04:19.935400+00:00)

## What it is
A collection of Google-provided Apache Beam pipeline templates for Google Cloud Dataflow that solve common in-cloud data tasks like import/export, backup/restore, and bulk API operations without writing pipeline code. The templates are open source Java pipelines that can be run as-is or customized and extended by developers.

## Use cases
- stream data from Kafka or Pub/Sub into BigQuery
- run change data capture from MySQL to BigQuery
- migrate data from Datastream into Spanner or BigQuery
- export BigQuery tables to Cloud Storage
- bulk copy or backup data between Google Cloud databases
- mask or tokenize data with Cloud DLP while loading into BigQuery
- process MongoDB or JMS data streams without writing pipeline code

## When to choose
- you need prebuilt, production-ready pipelines for common GCP data movement tasks
- you want to run batch or streaming ETL on Dataflow without a development environment
- you need a customizable starting point for Apache Beam pipelines on Google Cloud

## When to avoid
- your data workloads run outside Google Cloud
- you need highly custom transformation logic that doesn't fit any template
- you prefer lightweight local ETL tools rather than managed cloud dataflow jobs

## Facets
- artifact type: framework
- maturity: active
- function: etl, streaming, data-science, workflow-automation
- domain: big-data, cloud-computing, databases, analytics
- platform: cloud, jvm
- tags: google-cloud-dataflow, apache-beam, pipeline-templates, bigquery, bigtable, cloud-spanner, gcp, batch-processing, change-data-capture, data-engineering, docker

## Member repositories
- GoogleCloudPlatform/DataflowTemplates (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:19.935400+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 2026-08-30T04:49:53.257060+00:00, confidence not recorded.
  - readme: https://github.com/GoogleCloudPlatform/DataflowTemplates (fetched 2026-08-28T04:04:19.935400+00:00, sha ec414145bfb6)
  - homepage: https://cloud.google.com/dataflow/docs/guides/templates/provided-templates (fetched 2026-08-29T12:07:51.063283+00:00, sha 0f2472d7a113)
- Data as of 2026-08-30T08:39:29.467469+00:00.