Apache Ranger
Apache Ranger is a framework to enable, monitor, and manage comprehensive data security across the Hadoop platform. It provides centralized security administration for fine-grained authorization policies across Hadoop ecosystem components.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Apache Ranger the way a machine reads it — 76 machine-readable artifacts across 1 API, 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 — Apache Ranger scores 33.9/100 (thin), with a separate agent-readiness read of 20/100 (agent aware). 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. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-10 · rubric v0.20.0
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How we profile Apache Ranger
Each block below is one kind of artifact we hold for Apache Ranger. 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 5
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.
Apache Ranger Audit API
The Audit API from Apache Ranger — 1 operation(s) for audit.
Apache Ranger Groups API
The Groups API from Apache Ranger — 1 operation(s) for groups.
Apache Ranger Policies API
The Policies API from Apache Ranger — 2 operation(s) for policies.
Apache Ranger Services API
The Services API from Apache Ranger — 2 operation(s) for services.
Apache Ranger Users API
The Users API from Apache Ranger — 1 operation(s) for users.
Open Collections 6
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).
API Collection
OPEN COLLECTIONApache Ranger REST Audit API
OPEN COLLECTIONApache Ranger REST Audit Groups API
OPEN COLLECTIONApache Ranger REST Audit Policies API
OPEN COLLECTIONApache Ranger REST Audit Services API
OPEN COLLECTIONApache Ranger REST Audit Users 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 things the Kin Score reads for access clarity — renamed from commercial clarity in rubric 0.12, because a free statutory interface has access terms and no commercial ones.
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.
Apache Ranger 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.
Apache Ranger Finops
FINOPSFeatures 7
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Centralized Policy Management
Manage security policies for all Hadoop services from a single interface
Fine-Grained Access Control
Column-level, row-level, and data masking policies for Hive and HBase
Attribute-Based Access Control
Context-aware policies based on user attributes and tag classifications
Audit Logging
Comprehensive audit trail of all resource access events
Multi-Service Support
Supports HDFS, Hive, HBase, Kafka, Storm, Solr, Kudu, and more
LDAP/AD Integration
Sync users and groups from Active Directory or LDAP
Security Zones
Delegate policy administration with security zones
Scroll within the panel for all 7 ·
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.
Apache Ranger Context
JSON-LDSpectral Rules 2
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.
Apache Ranger API Rules
SPECTRALApache Ranger API Rules
SPECTRALJSON Schema 13
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.
AccessType
JSON SCHEMAAuditEntry
JSON SCHEMAAuditList
JSON SCHEMAGroupList
JSON SCHEMAPolicyItem
JSON SCHEMAPolicyList
JSON SCHEMAPolicyResource
JSON SCHEMAPolicy
JSON SCHEMARangerGroup
JSON SCHEMARangerService
JSON SCHEMARangerUser
JSON SCHEMAServiceList
JSON SCHEMAUserList
JSON SCHEMAScroll within the panel for all 13 ·
JSON Structure 13
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.
Apache Ranger Access Type Structure
JSON STRUCTUREApache Ranger Audit Entry Structure
JSON STRUCTUREApache Ranger Audit List Structure
JSON STRUCTUREApache Ranger Group List Structure
JSON STRUCTUREApache Ranger Policy Item Structure
JSON STRUCTUREApache Ranger Policy List Structure
JSON STRUCTUREApache Ranger Policy Resource Structure
JSON STRUCTUREApache Ranger Policy Structure
JSON STRUCTUREApache Ranger Ranger Group Structure
JSON STRUCTUREApache Ranger Ranger Service Structure
JSON STRUCTUREApache Ranger Ranger User Structure
JSON STRUCTUREApache Ranger Service List Structure
JSON STRUCTUREApache Ranger User List Structure
JSON STRUCTUREScroll within the panel for all 13 ·
Examples 13
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Apache Ranger Policy Example
EXAMPLEScroll within the panel for all 13 ·
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.
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.
Use Cases 4
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Data Lake Security
Enforce column and row-level security on Hadoop data lake
Regulatory Compliance
Meet GDPR, HIPAA, and SOX requirements with audit logs and masking
Multi-Tenant Authorization
Isolate access between teams and business units
Kafka Topic Authorization
Control which applications can produce and consume Kafka topics
Integrations 5
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Apache Hadoop
Native HDFS and YARN authorization integration
Apache Hive
Column-level and row-level security for Hive queries
Apache HBase
Table and column family security for HBase
Apache Kafka
Topic-level authorization for Kafka producers and consumers
Apache Atlas
Tag-based policies using Atlas data classifications
Resources
Every other property we hold for Apache Ranger — 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
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 3
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
← All providers · Data indexed from github.com/api-evangelist/apache-ranger · machine-readable index on apis.io
This is an independent, third-party profile of Apache Ranger, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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