Analytics
A curated index of analytics platforms, SDKs, and open source solutions spanning the full analytics spectrum — from web and product analytics (Google Analytics, Mixpanel, Amplitude, PostHog, Plausible, Matomo, Heap) to customer data platforms (Segment, mParticle, RudderStack), mobile analytics (Firebase Analytics, Adjust, AppsFlyer, Braze), business intelligence (Looker, Tableau, Metabase, Redash), event streaming (Kafka, Kinesis), and real-time analytics infrastructure (ClickHouse, Druid, Pinot). Covers both SaaS and self-hosted, open source and commercial offerings.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Analytics the way a machine reads it — 3 machine-readable artifacts, 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 — Analytics scores 20.9/100 (emerging), with a separate agent-readiness read of 0/100 (human only). 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.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Analytics
Each block below is one kind of artifact we hold for Analytics. 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.
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.
Analytics 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.
Analytics API Rules
SPECTRALJSON Schema 1
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.
Analytics Platform
JSON SCHEMAResources
Every other property we hold for Analytics — 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.
Documentation 1
Reference material describing how the API behaves
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/analytics · machine-readable index on apis.io