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Apache Pig website screenshot

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.

agent aware

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.

Kin Score

API Evangelist profiles Apache Pig the way a machine reads it — 47 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 48.3/100 (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.

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 48.3/100 · developing
Contract Quality 15.5 / 25
Developer Ergonomics 1.7 / 20
Commercial Clarity 7.9 / 20
Operational Transparency 4.8 / 13
Governance 10.4 / 12
Discoverability 8.0 / 10
Agent readiness — 39/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 15 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

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.

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 Pig Rate Limits

5 limits

RATE LIMITS

FinOps 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.

Features 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

7 classes · 19 properties

JSON-LD

Spectral 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

5 rules · 3 warnings

SPECTRAL

Apache Pig API Rules

12 rules · 5 errors · 5 warnings

SPECTRAL

JSON 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

2 properties

JSON SCHEMA

JobLogs

2 properties

JSON SCHEMA

JobRequest

4 properties

JSON SCHEMA

Job

8 properties

JSON SCHEMA

ScriptRequest

1 properties

JSON SCHEMA

ValidationError

4 properties

JSON SCHEMA

ValidationResult

2 properties

JSON SCHEMA

Scroll 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

2 properties

JSON STRUCTURE

Apache Pig Job Logs Structure

2 properties

JSON STRUCTURE

Apache Pig Job Request Structure

4 properties

JSON STRUCTURE

Apache Pig Job Structure

8 properties

JSON STRUCTURE

Apache Pig Script Request Structure

1 properties

JSON STRUCTURE

Apache Pig Validation Error Structure

4 properties

JSON STRUCTURE

Apache Pig Validation Result Structure

2 properties

JSON STRUCTURE

Scroll 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.

Apache Pig Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Apache Pig Vulnerability Disclosure

security.txt · contact published

SECURITY

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.

Apache Pig Agentic Access

6 operations · 3 acting · 1 human-in-the-loop

6 operations · 3 acting

AGENTIC

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

← All providers · Data indexed from github.com/api-evangelist/apache-pig · machine-readable index on apis.io