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.
Apache Hudi publishes 2 APIs on the APIs.io network: Tables API and Timeline API. Tagged areas include ACID, Apache, Big Data, Data Lake, and Incremental Processing.
The Apache Hudi catalog on APIs.io includes 1 JSON-LD context and 2 Spectral governance rulesets.
Apache Hudi’s developer surface includes documentation, getting-started guide, engineering blog, and 8 more developer resources.
Kin Score
APIs 3
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 API collections (OpenAPI-derived and Bruno).
Apache Hudi Timeline Server API
OPEN COLLECTIONPricing Plans 1
Published pricing tiers and plan structures.
Rate Limits 1
Documented rate limits and quota policies.
Apache Hudi Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals for API financial operations.
Apache Hudi Finops
FINOPSFeatures 8
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.
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Semantic Vocabularies 1
JSON-LD contexts and semantic vocabularies used across these APIs.
Apache Hudi Timeline Context
JSON-LDSpectral Rules 2
Spectral governance rulesets for linting and validating these APIs.
Apache Hudi API Rules
SPECTRALApache Hudi API Rules
SPECTRALJSON Schema 6
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 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
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.
Agentic Access 1
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 5
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
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