Apache Pig
Apache Pig is a platform for analyzing large data sets that provides a high-level language (Pig Latin) for expressing data analysis programs. It compiles Pig Latin programs into MapReduce/Tez jobs and runs them on Hadoop clusters.
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 Apache Pig the way a machine reads it — 50 machine-readable artifacts across 2 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 Pig scores 19.9/100 (emerging), with a separate agent-readiness read of 17/100 (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 Pig
Each block below is one kind of artifact we hold for Apache Pig. 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 2
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 Pig Jobs API
The Jobs API from Apache Pig — 3 operation(s) for jobs.
Apache Pig Scripts API
The Scripts API from Apache Pig — 1 operation(s) for scripts.
Open Collections 3
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 Pig Jobs API
OPEN COLLECTIONApache Pig Jobs Scripts 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 Pig 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 Pig 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.
Pig Latin Language
High-level dataflow language for expressing data transformations
MapReduce/Tez Backend
Compiles Pig Latin to MapReduce or Apache Tez execution plans
UDF Support
User-defined functions in Java, Python, JavaScript, and Ruby
Streaming
Process data through external programs using STREAM operator
Schema Evolution
Flexible schema handling for semi-structured data
Optimization
Automatic logical and physical plan optimization
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 Pig 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 Pig API Rules
SPECTRALApache Pig 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.
JobList
JSON SCHEMAJobLogs
JSON SCHEMAJobRequest
JSON SCHEMAJob
JSON SCHEMAScriptRequest
JSON SCHEMAValidationError
JSON SCHEMAValidationResult
JSON SCHEMAScroll within the panel for all 7 ·
JSON Structure 7
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 Pig Job List Structure
JSON STRUCTUREApache Pig Job Logs Structure
JSON STRUCTUREApache Pig Job Request Structure
JSON STRUCTUREApache Pig Job Structure
JSON STRUCTUREApache Pig Script Request Structure
JSON STRUCTUREApache Pig Validation Error Structure
JSON STRUCTUREApache Pig Validation Result Structure
JSON STRUCTUREScroll within the panel for all 7 ·
Examples 7
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.
Scroll within the panel for all 7 ·
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.
ETL Pipelines
Build data transformation pipelines from raw logs to structured data
Ad-hoc Data Analysis
Analyze large datasets with ad-hoc Pig Latin queries
Data Preparation
Clean and prepare data for machine learning workflows
Log Processing
Process and aggregate web server and application logs
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 MapReduce execution on YARN/HDFS
Apache Tez
High-performance Tez execution engine support
Apache HBase
HBase storage handler for reading/writing HBase tables
Apache Hive
HCatalog integration for Hive metastore access
Amazon S3
S3 input/output for cloud-based data processing
Resources
Every other property we hold for Apache Pig — 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
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This is an independent, third-party profile of Apache Pig, 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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