Google BigQuery
Google BigQuery is a fully managed, serverless data warehouse that enables scalable analysis over petabytes of data using SQL.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Google BigQuery the way a machine reads it — 49 machine-readable artifacts across 11 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 BigQuery scores 61.9/100 (strong), 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 BigQuery
Each block below is one kind of artifact we hold for Google BigQuery. 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 11
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.
BigQuery Connection API
The BigQuery Connection API enables developers to create and manage connections between BigQuery and external data sources such as Cloud SQL, Cloud Spanner, and other databases....
BigQuery Migration API
The BigQuery Migration API provides tools for migrating data warehouse workloads to BigQuery from other platforms. It supports assessment and planning of migration tasks, transl...
BigQuery Reservation API
The BigQuery Reservation API allows developers to manage slot reservations and capacity commitments for BigQuery compute resources. It provides programmatic control over how com...
BigQuery Storage API
The BigQuery Storage API provides high-throughput read and write access to BigQuery managed storage. It enables developers to read data from BigQuery tables using an efficient s...
Google BigQuery Datasets API
Operations for managing BigQuery datasets
Google BigQuery Jobs API
Operations for managing query and load jobs
Google BigQuery Models API
Operations for managing BigQuery ML models
Google BigQuery Projects API
Operations for listing projects and service accounts
Google BigQuery Routines API
Operations for managing routines (functions and procedures)
Google BigQuery Tabledata API
Operations for reading and inserting table rows
Google BigQuery Tables API
Operations for managing tables within datasets
Scroll within the panel for all 11 ·
Open 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 BigQuery API
OPEN COLLECTIONPricing 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 Bigquery 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.
Google Bigquery Finops
FINOPSSemantic 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.
Google Bigquery Context
JSON-LDSpectral 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 BigQuery API Rules
SPECTRALJSON Schema 26
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.
Dataset
JSON SCHEMADatasetList
JSON SCHEMADatasetReference
JSON SCHEMAErrorProto
JSON SCHEMAJob
JSON SCHEMAJobCancelResponse
JSON SCHEMAJobConfiguration
JSON SCHEMAJobList
JSON SCHEMAJobReference
JSON SCHEMAJobStatus
JSON SCHEMAModel
JSON SCHEMAModelList
JSON SCHEMAProjectList
JSON SCHEMAGoogle BigQuery Query Request
JSON SCHEMAQueryRequest
JSON SCHEMAQueryResponse
JSON SCHEMARoutine
JSON SCHEMARoutineList
JSON SCHEMAGoogle BigQuery Table
JSON SCHEMATableDataInsertAllRequest
JSON SCHEMATableDataInsertAllResponse
JSON SCHEMATableDataList
JSON SCHEMATableFieldSchema
JSON SCHEMATableList
JSON SCHEMATableReference
JSON SCHEMATableSchema
JSON SCHEMAScroll within the panel for all 26 ·
JSON Structure 1
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Google Bigquery Structure
JSON STRUCTURESecurity 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 BigQuery — 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 2
Portal, sign-up, and the first successful call
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 1
Pagination, idempotency, versioning, errors, and events
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 5
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
← All providers · Data indexed from github.com/api-evangelist/google-bigquery · machine-readable index on apis.io