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 is listed with 6 APIs on the APIs.io network, including Applications API, Version API, and 4 more. Tagged areas include Analytics, Big Data, Distributed Computing, Machine Learning, and Open Source.
The Apache Spark catalog on APIs.io includes 1 Spectral governance ruleset.
Apache Spark’s developer surface includes developer portal, documentation, getting-started guide, engineering blog, support, Stack Overflow tag, and 17 more developer resources.
Regulatory Posture applies to this provider. Its tags matched the
Horizontal (data, software, accessibility, platform) regime, so
Regulatory Posture carries 15 points of the composite.
If this regime is wrong for your business, say so on your
provider repo — the
applicability map is public and we will correct it.
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 75 points between them,
because both conditional facets apply and carry
25 points together. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 75% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
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Agent readiness — checks not met, largest first (exact, from this profile):
MCP Server+12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Machine-Readable Auth+10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Stable Error Semantics+8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes
A2A Agent Card+8 pts — a2a/-a2a.yml manifest, graded on conformance.grade against A2A 1.0.0
Request/Response Examples+7 pts — share of operations carrying request/response examples; else an Examples / Code Samples link
Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.
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, ...
Scalable, high-throughput, fault-tolerant stream processing of live data streams. Supports Structured Streaming (the newer DStream-based API) with exactly-once semantics, contin...
Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dime...
Spark API for graphs and graph-parallel computation with a collection of graph algorithms and builders, including PageRank, Connected Components, Triangle Counting, and shortest...
Not yet checked against Apache Spark's own pricing page. Compiled by API Evangelist on 2026-05-04; confirm before you rely on a figure. A free plan or tier is listed.
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