Apache ORC
Apache ORC is a self-describing, type-aware columnar file format designed for Hadoop workloads. It provides high compression ratios and fast read performance for large-scale data processing with support for complex data types.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Apache ORC the way a machine reads it — 63 machine-readable artifacts across 3 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 — Apache ORC scores 49.5/100 (developing), with a separate agent-readiness read of 39/100 (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.
How we profile Apache ORC
Each block below is one kind of artifact we hold for Apache ORC. 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 3
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 ORC Conversion API
The Conversion API from Apache ORC — 1 operation(s) for conversion.
Apache ORC Files API
The Files API from Apache ORC — 4 operation(s) for files.
Apache ORC Operations API
The Operations API from Apache ORC — 1 operation(s) for operations.
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.
Apache Orc 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 Orc Finops
FINOPSFeatures 6
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.
Columnar Storage
Stores data by column for efficient compression and query performance
Predicate Pushdown
Skip reading data that does not match query predicates
Column Projection
Read only the columns needed for a query
ACID Support
Full ACID transactional support when used with Apache Hive
Schema Evolution
Add, rename, and remove columns while preserving backward compatibility
Compression
Supports ZLIB, Snappy, LZO, LZ4, and ZSTD compression codecs
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 Orc 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 ORC API Rules
SPECTRALApache ORC API Rules
SPECTRALJSON Schema 12
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.
ColumnStatisticsResponse
JSON SCHEMAColumnStatistics
JSON SCHEMAColumnType
JSON SCHEMAConversionRequest
JSON SCHEMAConversionResult
JSON SCHEMAFileInfo
JSON SCHEMAFileList
JSON SCHEMAFileMetadata
JSON SCHEMAMergeRequest
JSON SCHEMAOperationResult
JSON SCHEMAOrcSchema
JSON SCHEMAStripeInfo
JSON SCHEMAScroll within the panel for all 12 ·
JSON Structure 12
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 Orc Column Statistics Response Structure
JSON STRUCTUREApache Orc Column Statistics Structure
JSON STRUCTUREApache Orc Column Type Structure
JSON STRUCTUREApache Orc Conversion Request Structure
JSON STRUCTUREApache Orc Conversion Result Structure
JSON STRUCTUREApache Orc File Info Structure
JSON STRUCTUREApache Orc File List Structure
JSON STRUCTUREApache Orc File Metadata Structure
JSON STRUCTUREApache Orc Merge Request Structure
JSON STRUCTUREApache Orc Operation Result Structure
JSON STRUCTUREApache Orc Orc Schema Structure
JSON STRUCTUREApache Orc Stripe Info Structure
JSON STRUCTUREScroll within the panel for all 12 ·
Examples 12
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 Orc File Info Example
EXAMPLEApache Orc File List Example
EXAMPLEScroll within the panel for all 12 ·
Security Posture 2
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.
Hive Data Warehousing
Store Hive tables in highly efficient ORC format
Spark Analytics
Process large ORC datasets with Apache Spark SQL
Presto/Trino Queries
Fast analytical queries over ORC files with Presto or Trino
Data Lake Storage
Efficient columnar storage for data lake architectures
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 Hive
Native ORC support as default Hive storage format
Apache Spark
ORC data source support in Spark SQL
Presto/Trino
Fast ORC reading with native vectorized reader
Apache Flink
ORC file format support for batch and streaming
Apache Arrow
ORC to Arrow conversion for in-memory analytics
Resources
Every other property we hold for Apache ORC — 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 2
Authentication, authorization, and security posture
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/apache-orc · machine-readable index on apis.io