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.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Apache Spark the way a machine reads it — 32 machine-readable artifacts across 6 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 Spark scores 40.0/100 (thin), 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
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Apache Spark
Each block below is one kind of artifact we hold for Apache Spark. 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 6
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 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 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 Spark Monitoring REST 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 Spark 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 Spark Finops
FINOPSFeatures 7
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.
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 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 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.
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 tell you where this provider already fits in a stack.
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 within the panel for all 7 ·
Resources
Every other property we hold for Apache Spark — 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 2
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
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Operate 2
Status, limits, changes, and where to get help
Commercial 1
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
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