Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general execution graphs. Spark offers a comprehensive suite of APIs for batch processing, SQL queries, streaming analytics, machine learning, and graph computation, governed by the Apache Software Foundation.

Apache Spark publishes 2 APIs on the APIs.io network: Applications API and Version API. Tagged areas include Analytics, Big Data, Distributed Computing, Machine-Learning, and Open-Source.

Apache Spark’s developer surface includes developer portal, documentation, getting-started guide, engineering blog, support, Stack Overflow tag, and 14 more developer resources.

🌐 Visit website 📡 Source on GitHub

38.9/100 thin ▬ flat Agent 20/100 agent aware open core · Apache-2.0 Full breakdown ↓
scored 2026-09-08 · rubric v0.20.0

AccessFreemium

6 APIs 7 Features 5 Use Cases

Analytics Big Data Distributed Computing Machine-Learning Open-Source Streaming

Kin Score

Kin Score How this is scored →
scored 2026-09-08 · rubric v0.20.0

Composite quality — 38.9/100 · thin

Contract Quality 7.8 / 23

Developer Ergonomics 9.9 / 18

Access Clarity 5.1 / 18

Operational Transparency 2.8 / 12

Contract Governance 0.0 / 11

Discoverability 5.8 / 9

Open Source Surface 7.5 / 10

Agent readiness — 20/100 · agent aware

Machine-Readable Contract 18 / 18

Agentic Access Contract 10 / 10

Documented Reversibility 6 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 9 / 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

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 4 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against it.

Create-or-Update Ergonomics does not apply to this provider. The published contracts declare no write operations, and a read-only API cannot create-or-update. The facet is excluded from this provider's denominator entirely — not scored zero. A reference or data API is not deficient for being unable to upsert.

The six quality facets above are damped to 90 points between them, because the conditional facet above carries the other 10. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 90% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/apache-spark: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 6

Individual APIs this provider publishes, each with its own machine-readable definition.

Apache Spark SQL API

Spark module for structured data processing with DataFrame and Dataset APIs. Provides a SQL interface and supports various data sources including Parquet, ORC, JSON, CSV, JDBC, ...

Apache Spark Streaming API

Scalable, high-throughput, fault-tolerant stream processing of live data streams. Supports Structured Streaming (the newer DStream-based API) with exactly-once semantics, contin...

Apache Spark MLlib API

Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dime...

Apache Spark GraphX API

Spark API for graphs and graph-parallel computation with a collection of graph algorithms and builders, including PageRank, Connected Components, Triangle Counting, and shortest...

Apache Spark Applications API

The Applications API from Apache Spark — 25 operation(s) for applications.

Apache Spark Version API

The Version API from Apache Spark — 1 operation(s) for version.

Open Collections 4

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

API Collection

OPEN COLLECTION

Apache Spark Monitoring REST Applications API

OPEN COLLECTION

Apache Spark Monitoring REST Applications Version API

OPEN COLLECTION

Apache Spark Monitoring REST API

OPEN COLLECTION

Pricing Plans 1

Published pricing tiers and plan structures.

Apache Spark Plans Pricing

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Apache Spark Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Apache Spark Finops

FINOPS

Features 7

Notable capabilities this provider offers.

Unified Analytics Engine

Single engine for batch, streaming, SQL, ML, and graph processing workloads.

Lazy Evaluation and DAG Execution

Optimized execution plans with Catalyst optimizer and DAG scheduling.

In-Memory Processing

Up to 100x faster than Hadoop MapReduce for iterative algorithms via in-memory caching.

Structured Streaming

Unified streaming and batch processing with exactly-once semantics and Kafka integration.

Multi-Language Support

High-level APIs in Scala, Java, Python (PySpark), and R (SparkR).

Delta Lake Integration

ACID transactions, schema evolution, and time travel for data lakes.

Kubernetes Native

Native Kubernetes scheduling for cloud-native deployment of Spark workloads.

Scroll for all 7

Security Posture 2

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Apache Spark Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Apache Spark Vulnerability Disclosure

security.txt · contact published

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Apache Spark Agentic Access

26 operations

26 operations · 0 acting

AGENTIC

Use Cases 5

What developers build with this provider.

Large-Scale ETL

Extract, transform, and load petabytes of data across distributed clusters.

Real-Time Analytics

Streaming analytics on live event data with sub-second latency.

Machine Learning Pipelines

Distributed ML training and feature engineering at scale with MLlib.

Data Lake Processing

Query and transform data stored in cloud object stores and HDFS.

Interactive SQL Analytics

Interactive SQL queries on structured and semi-structured data at scale.

Integrations 7

Pre-built integrations with other platforms and tools.

Apache Hadoop

HDFS storage, YARN cluster manager, and Hadoop ecosystem integration.

Apache Kafka

Structured Streaming source and sink for real-time event processing.

Delta Lake

Open-source storage layer with ACID transactions for data lakes.

Apache Iceberg

Open table format for huge analytic datasets on cloud storage.

Apache Hive

Hive metastore integration for table catalog and metadata management.

Kubernetes

Native Kubernetes scheduling for cloud-native Spark deployments.

Apache Airflow

Workflow orchestration for scheduling and managing Spark jobs.

Scroll for all 7

Resources

Get Started 2

Portal, sign-up, and the first successful call

Portal

Portal

GettingStarted

GettingStarted

Documentation 2

Reference material describing how the API behaves

ContributionGuide

ContributionGuide

Documentation

Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

AgenticAccess

AgenticAccess

Build 4

SDKs, sample code, and the tooling you integrate with

CodeOfConduct

CodeOfConduct

GitHubRepository

GitHubRepository

PySpark (Python)

SDKs

Maven (Scala/Java)

SDKs

Access & Security 3

Authentication, authorization, and security posture

SecurityPolicy

SecurityPolicy

VulnerabilityDisclosure

VulnerabilityDisclosure

DomainSecurity

DomainSecurity

Operate 3

Status, limits, changes, and where to get help

IssueTracker

IssueTracker

Support

Support

StackOverflow

StackOverflow

Commercial 2

Pricing, plans, and the legal terms of use

License

License

TermsOfService

TermsOfService

Company 3

The organization behind the API

Website

Website

LinkedIn

Blog

Blog

Source (apis.yml)

aid: apache-spark
name: Apache Spark
description: Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java,
  Scala, Python, and R, and an optimized engine that supports general execution graphs. Spark offers a comprehensive suite
  of APIs for batch processing, SQL queries, streaming analytics, machine learning, and graph computation, governed by the
  Apache Software Foundation.
type: Index
deliveryModel:
  model: open-core
  license: Apache-2.0
  open_source: true
  commercial: true
  callable_host: false
  label: Open core · an OSS project plus a commercial hosted product
  confidence: high
  source:
  - license
  - openapi
  - pricing
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: medium
  source:
  - plans
  generated: '2026-07-22'
  method: derived
position: Consuming
access: 3rd-Party
image: https://spark.apache.org/images/spark-logo-trademark.png
tags:
- Analytics
- Big Data
- Distributed Computing
- Machine-Learning
- Open-Source
- Streaming
tags_raw:
- Analytics
- Big Data
- Distributed Computing
- Machine Learning
- Open Source
- Streaming
created: '2024-01-01'
modified: '2026-05-19'
url: https://raw.githubusercontent.com/api-evangelist/apache-spark/refs/heads/main/apis.yml
specificationVersion: '0.23'
apis:
- aid: apache-spark:apache-spark-sql-api
  name: Apache Spark SQL API
  description: Spark module for structured data processing with DataFrame and Dataset APIs. Provides a SQL interface and supports
    various data sources including Parquet, ORC, JSON, CSV, JDBC, Hive, and Delta Lake. The Spark SQL API supports Scala,
    Python, Java, and R bindings.
  humanURL: https://spark.apache.org/docs/latest/sql-programming-guide.html
  tags:
  - DataFrames
  - SQL
  - Structured Data
  properties:
  - type: Documentation
    url: https://spark.apache.org/docs/latest/sql-programming-guide.html
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/sql/index.html
    title: Scala API Reference
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/index.html
    title: Python API Reference
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/java/index.html?org/apache/spark/sql/package-summary.html
    title: Java API Reference
- aid: apache-spark:apache-spark-streaming-api
  name: Apache Spark Streaming API
  description: Scalable, high-throughput, fault-tolerant stream processing of live data streams. Supports Structured Streaming
    (the newer DStream-based API) with exactly-once semantics, continuous processing mode, and integration with Kafka, Kinesis,
    HDFS, and other sources.
  humanURL: https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html
  tags:
  - Data Processing
  - Real-Time
  - Streaming
  properties:
  - type: Documentation
    url: https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/streaming/index.html
    title: Scala Streaming API
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/python/reference/pyspark.streaming/index.html
    title: Python Streaming API
- aid: apache-spark:apache-spark-mllib-api
  name: Apache Spark MLlib API
  description: Spark's scalable machine learning library consisting of common learning algorithms and utilities, including
    classification, regression, clustering, collaborative filtering, dimensionality reduction, and feature engineering. Supports
    pipeline-based ML workflows through the spark.ml package.
  humanURL: https://spark.apache.org/docs/latest/ml-guide.html
  tags:
  - Algorithms
  - Data Science
  - Machine-Learning
  - ML
  tags_raw:
  - Algorithms
  - Data Science
  - Machine Learning
  - ML
  properties:
  - type: Documentation
    url: https://spark.apache.org/docs/latest/ml-guide.html
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/ml/index.html
    title: Scala MLlib API
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/python/reference/pyspark.ml.html
    title: Python MLlib API
- aid: apache-spark:apache-spark-graphx-api
  name: Apache Spark GraphX API
  description: Spark API for graphs and graph-parallel computation with a collection of graph algorithms and builders, including
    PageRank, Connected Components, Triangle Counting, and shortest paths.
  humanURL: https://spark.apache.org/docs/latest/graphx-programming-guide.html
  tags:
  - Analytics
  - Graph Processing
  - Graphs
  properties:
  - type: Documentation
    url: https://spark.apache.org/docs/latest/graphx-programming-guide.html
  - type: SDKs
    url: https://spark.apache.org/docs/latest/api/scala/org/apache/spark/graphx/index.html
    title: Scala GraphX API
- aid: apache-spark:apache-spark-applications-api
  name: Apache Spark Applications API
  description: The Applications API from Apache Spark — 25 operation(s) for applications.
  humanURL: https://spark.apache.org/docs/latest/monitoring.html#rest-api
  tags:
  - Application
  tags_raw:
  - Applications
  properties:
  - type: OpenAPI
    url: openapi/apache-spark-applications-api-openapi.yml
  - type: Documentation
    url: https://spark.apache.org/docs/latest/monitoring.html#rest-api
- aid: apache-spark:apache-spark-version-api
  name: Apache Spark Version API
  description: The Version API from Apache Spark — 1 operation(s) for version.
  humanURL: https://spark.apache.org/docs/latest/monitoring.html#rest-api
  tags:
  - Version
  properties:
  - type: OpenAPI
    url: openapi/apache-spark-version-api-openapi.yml
  - type: Documentation
    url: https://spark.apache.org/docs/latest/monitoring.html#rest-api
common:
- type: Website
  url: https://www.apache.org/
- type: IssueTracker
  url: https://github.com/apache/spark/issues
- type: SecurityPolicy
  url: https://github.com/apache/spark/blob/master/SECURITY.md
- type: CodeOfConduct
  url: https://github.com/apache/.github/blob/main/.github/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/apache/spark/blob/master/CONTRIBUTING.md
- type: License
  name: Apache-2.0
  url: https://github.com/apache/spark/blob/master/LICENSE
- type: AgenticAccess
  url: agentic-access/apache-spark-agentic-access.yml
- type: VulnerabilityDisclosure
  url: security/apache-spark-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/apache-spark-domain-security.yml
- type: LinkedIn
  url: https://www.linkedin.com/company/apachespark
- type: GitHubRepository
  url: https://github.com/apache/spark
- type: Portal
  url: https://spark.apache.org/
- type: Documentation
  url: https://spark.apache.org/docs/latest/
- type: GettingStarted
  url: https://spark.apache.org/docs/latest/quick-start.html
- type: Blog
  url: https://spark.apache.org/news/
- type: Support
  url: https://spark.apache.org/community.html
- type: TermsOfService
  url: https://www.apache.org/licenses/LICENSE-2.0
- type: StackOverflow
  url: https://stackoverflow.com/questions/tagged/apache-spark
- type: SDKs
  url: https://pypi.org/project/pyspark/
  title: PySpark (Python)
- type: SDKs
  url: https://search.maven.org/search?q=g:org.apache.spark
  title: Maven (Scala/Java)
- type: Features
  data:
  - name: Unified Analytics Engine
    description: Single engine for batch, streaming, SQL, ML, and graph processing workloads.
  - name: Lazy Evaluation and DAG Execution
    description: Optimized execution plans with Catalyst optimizer and DAG scheduling.
  - name: In-Memory Processing
    description: Up to 100x faster than Hadoop MapReduce for iterative algorithms via in-memory caching.
  - name: Structured Streaming
    description: Unified streaming and batch processing with exactly-once semantics and Kafka integration.
  - name: Multi-Language Support
    description: High-level APIs in Scala, Java, Python (PySpark), and R (SparkR).
  - name: Delta Lake Integration
    description: ACID transactions, schema evolution, and time travel for data lakes.
  - name: Kubernetes Native
    description: Native Kubernetes scheduling for cloud-native deployment of Spark workloads.
- type: UseCases
  data:
  - name: Large-Scale ETL
    description: Extract, transform, and load petabytes of data across distributed clusters.
  - name: Real-Time Analytics
    description: Streaming analytics on live event data with sub-second latency.
  - name: Machine Learning Pipelines
    description: Distributed ML training and feature engineering at scale with MLlib.
  - name: Data Lake Processing
    description: Query and transform data stored in cloud object stores and HDFS.
  - name: Interactive SQL Analytics
    description: Interactive SQL queries on structured and semi-structured data at scale.
- type: Integrations
  data:
  - name: Apache Hadoop
    description: HDFS storage, YARN cluster manager, and Hadoop ecosystem integration.
  - name: Apache Kafka
    description: Structured Streaming source and sink for real-time event processing.
  - name: Delta Lake
    description: Open-source storage layer with ACID transactions for data lakes.
  - name: Apache Iceberg
    description: Open table format for huge analytic datasets on cloud storage.
  - name: Apache Hive
    description: Hive metastore integration for table catalog and metadata management.
  - name: Kubernetes
    description: Native Kubernetes scheduling for cloud-native Spark deployments.
  - name: Apache Airflow
    description: Workflow orchestration for scheduling and managing Spark jobs.
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

find_providersBrowse and filter every provider in the catalog.
get_provider_artifactsEvery artifact this provider publishes, grouped by type.
get_provider_operationsEvery operation across all of their OpenAPIs — one call instead of parsing every spec.
get_provider_toolsEvery MCP tool they ship, with the operation each wraps.
get_provider_evidenceHow each part of their score was established. Free — the basis for a claim should not sit behind it.
get_provider_ratingPRO — composite, band, trend and facet scores.
apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
resolveTurn a domain, URL or GitHub org into the provider it belongs to.
find_cohortsEvery scored population of providers in the catalog.

All 92 tools →

Call it yourself

curl for this page

This provider

curl "https://apis.io/api/v1/providers/apache-spark"

All providers

curl "https://apis.io/api/v1/providers?limit=25"

Every operation they expose

curl "https://apis.io/api/v1/providers/apache-spark/operations?limit=25"

How their score was established

curl "https://apis.io/api/v1/providers/apache-spark/evidence"

Discovery needs no key. Ratings and market analysis are Pro.

Get an API key

Free tier, no form to fill in. Signing in shares your email address with us — we store it to create your key and to recognise you if you sign in with another provider. See our Privacy Policy and Terms.

GitHub Google LinkedIn

A second provider on the same verified email joins the account you already have.