Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for executing Apache Beam pipelines for batch and streaming data processing. It provides a serverless, fast, and cost-effective way to process data at scale.
Reference-quality API operations across every facet — a rich contract, published governance, transparent operations, and machine-readable commercial terms.
API Evangelist profiles Google Cloud Dataflow the way a machine reads it — 37 machine-readable artifacts across 8 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Google Cloud Dataflow scores 74.6/100 (exemplar), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
How we profile Google Cloud Dataflow
Each block below is one kind of artifact we hold for Google Cloud Dataflow. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 8
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Google Cloud Dataflow Debug API
Operations for retrieving debug configuration and submitting debug captures.
Google Cloud Dataflow Flex Templates API
Operations for launching Dataflow Flex Templates.
Google Cloud Dataflow Jobs API
Operations for creating, managing, and monitoring Dataflow jobs.
Google Cloud Dataflow Messages API
Operations for retrieving job status messages and logs.
Google Cloud Dataflow Metrics API
Operations for obtaining job and pipeline execution metrics.
Google Cloud Dataflow Snapshots API
Operations for creating, listing, getting, and deleting job snapshots.
Google Cloud Dataflow Stages API
Operations for retrieving stage-level execution details.
Google Cloud Dataflow Templates API
Operations for working with Dataflow classic templates.
Scroll within the panel for all 8 ·
Postman Collections 1
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Google Cloud Dataflow API
POSTMANOpen Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Google Cloud Dataflow API
OPEN COLLECTIONArazzo Workflows 10
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Google Cloud Dataflow Cancel Running Job
Confirm a job is running, request cancellation, then poll until it is cancelled.
ARAZZOGoogle Cloud Dataflow Capture Worker Debug Data
Confirm a job, fetch a worker component's debug config, then send a debug capture.
ARAZZOGoogle Cloud Dataflow Cleanup Job Snapshots
List snapshots for a job, inspect the oldest one, then delete it.
ARAZZOGoogle Cloud Dataflow Create Job From Template and Track
Inspect a classic template's metadata, create a job from it, then confirm the job exists.
ARAZZOGoogle Cloud Dataflow Diagnose Job
Read a job's state, pull its error-level messages, then inspect stage execution details.
ARAZZOGoogle Cloud Dataflow Drain Running Job
Confirm a streaming job is running, request a drain, then poll until it is drained.
ARAZZOGoogle Cloud Dataflow Launch Classic Template and Monitor
Launch a job from a classic Dataflow template, poll it to completion, then read its metrics.
ARAZZOGoogle Cloud Dataflow Launch Flex Template and Monitor
Launch a containerized Flex Template job, poll it to completion, then read its metrics.
ARAZZOGoogle Cloud Dataflow List Jobs and Snapshot
List jobs in a region, inspect the first job, then snapshot it.
ARAZZOGoogle Cloud Dataflow Snapshot Streaming Job
Confirm a streaming job is running, take a snapshot, then read the snapshot back.
ARAZZOScroll within the panel for all 10 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Google Cloud Dataflow Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Spectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Google Cloud Dataflow API Rules
SPECTRALJSON Schema 7
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Google Cloud Dataflow Environment
JSON SCHEMAGoogle Cloud Dataflow Job
JSON SCHEMAGoogle Cloud Dataflow Job Metrics
JSON SCHEMAGoogle Cloud Dataflow Pipeline Description
JSON SCHEMAGoogle Cloud Dataflow Snapshot
JSON SCHEMAGoogle Cloud Dataflow Template
JSON SCHEMAGoogle Cloud Dataflow Worker Pool
JSON SCHEMAScroll within the panel for all 7 ·
Security Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Scopes 1
OAuth scopes are the vocabulary of least-privilege access. Profiling them shows exactly what an integration — or an agent acting on a user's behalf — is allowed to do.
OAuth scopes governing access to this provider's APIs.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Resources
Every other property we hold for Google Cloud Dataflow — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 4
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 10
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 10 ·
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 6
Authentication, authorization, and security posture
Learn 2
Tutorials, courses, talks, and written guidance
Operate 4
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
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