Apache Hudi
Apache Hudi is a data lake platform that provides incremental data processing primitives including upserts and incremental queries. It manages storage of large analytical datasets on distributed file systems with ACID transactions, timeline-based versioning, and integrations for Spark, Flink, and Hive.
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 Hudi the way a machine reads it — 44 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 Hudi scores 52.6/100 (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 Hudi
Each block below is one kind of artifact we hold for Apache Hudi. 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 Hudi Java API
Java API for writing Hudi tables with upserts, inserts, and deletes, plus timeline management, compaction, and Spark/Flink DataSource integration APIs.
Apache Hudi Tables API
Hudi table management operations
Apache Hudi Timeline API
Hudi timeline and commit operations
Open Collections 1
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).
Apache Hudi Timeline Server 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 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 Hudi 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 Hudi Finops
FINOPSFeatures 8
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.
ACID Upserts
Atomically insert or update records in data lake tables with ACID guarantees using record keys.
Hudi Timeline
Immutable commit timeline tracking all mutations for time travel, rollback, and incremental queries.
Incremental Queries
Query only the data changed since a given commit timestamp for efficient streaming ingestion.
Copy-On-Write Tables
COW table type rewrites entire Parquet files on upsert for read-optimized query performance.
Merge-On-Read Tables
MOR table type appends delta logs for fast writes with compaction-based read optimization.
Table Services
Built-in cleaning, compaction, clustering, and indexing services for table maintenance.
Multi-Engine Support
Read and write Hudi tables from Apache Spark, Flink, Hive, Presto, Trino, and Athena.
Schema Evolution
Support for adding, renaming, and dropping columns with backward-compatible schema evolution.
Scroll within the panel for all 8 ·
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 Hudi Timeline 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 Hudi API Rules
SPECTRALApache Hudi API Rules
SPECTRALJSON Schema 6
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.
CleanConfig
JSON SCHEMACommitMetadata
JSON SCHEMAHudiTable
JSON SCHEMAQueryConfig
JSON SCHEMATimelineInstant
JSON SCHEMAWriteConfig
JSON SCHEMAJSON Structure 6
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.
Hudi Cleanconfig Structure
JSON STRUCTUREHudi Commitmetadata Structure
JSON STRUCTUREHudi Huditable Structure
JSON STRUCTUREHudi Queryconfig Structure
JSON STRUCTUREHudi Timelineinstant Structure
JSON STRUCTUREHudi Writeconfig Structure
JSON STRUCTUREExamples 6
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.
Hudi Cleanconfig Example
EXAMPLEHudi Commitmetadata Example
EXAMPLEHudi Huditable Example
EXAMPLEHudi Queryconfig Example
EXAMPLEHudi Timelineinstant Example
EXAMPLEHudi Writeconfig Example
EXAMPLESecurity 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 5
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.
CDC Pipeline Ingestion
Ingest change data capture (CDC) events from databases into data lake tables with upsert support.
Streaming Data Lake
Build near-real-time data lake pipelines with Spark Structured Streaming or Flink.
Data Lake Maintenance
Manage storage costs with automated cleaning, compaction, and clustering of Hudi tables.
Incremental ETL
Build incremental ETL pipelines that process only changed data since the last run.
Regulatory Data Retention
Implement GDPR right-to-erasure by deleting records from Hudi tables with delete operations.
Resources
Every other property we hold for Apache Hudi — 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.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Company 2
The organization behind the API
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