Streaming
Streaming is a topic catalog of the protocols, platforms, and processing engines used to move and transform real-time, high-volume, often bidirectional data over the network. It indexes the canonical log-structured and broker systems (Apache Kafka, Apache Pulsar, Redpanda, NATS JetStream, AWS Kinesis, GCP Pub/Sub + Dataflow, Azure Event Hubs, Confluent Cloud, StreamNative), the over-the-wire streaming protocols exposed to API consumers (Server-Sent Events, WebSocket, gRPC streaming, GraphQL subscriptions), the change-data capture and connector frameworks that feed them (Kafka Connect, Debezium), and the stream-processing engines that consume them (Apache Flink, Spark Structured Streaming, Materialize, Tinybird, Bytewax, Apache Beam). This topic is distinguished from `events` and `async-apis`: streaming emphasizes real-time, high-throughput, partitioned, and often bidirectional pipes, rather than discrete event envelopes or static contract documents.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles Streaming the way a machine reads it — 33 machine-readable artifacts across 21 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 — Streaming scores 19.2/100 (emerging), with a separate agent-readiness read of 2/100 (human only). 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 2026-08-17 · rubric v0.11.0
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</a>More shapes, themes and sizes → · Score as JSON · How badges work
How we profile Streaming
Each block below is one kind of artifact we hold for Streaming. 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 21
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 Kafka
Distributed, partitioned, replicated log. The reference open-source streaming platform; durable, ordered topics with consumer groups, exactly -once semantics, and the de facto w...
Apache Pulsar
Cloud-native, multi-tenant pub/sub and streaming platform with a tiered storage architecture (BookKeeper) that separates compute from storage, native geo-replication, and built-...
Redpanda
Kafka-API-compatible streaming platform implemented in C++ with no ZooKeeper/JVM dependency. Single binary, thread-per-core architecture, Raft consensus; positioned as a drop-in...
NATS JetStream
Persistence layer for the NATS messaging system providing at-least-once and exactly-once streaming, key/value and object stores, and durable consumers — designed for edge, IoT, ...
Amazon Kinesis
AWS managed family for real-time streaming: Kinesis Data Streams (shards, partition keys, 24h–365d retention), Kinesis Data Firehose (delivery to S3/Redshift/OpenSearch), and Ki...
Google Cloud Pub/Sub and Dataflow
GCP's managed messaging (Pub/Sub) and stream-processing (Dataflow, built on Apache Beam) stack. Pub/Sub provides at-least-once and exactly-once delivery with push/pull subscribe...
Azure Event Hubs
Microsoft's managed big-data streaming platform; Kafka-protocol compatible, partitioned, with Capture (delivery to ADLS/Blob) and tight integration with Azure Stream Analytics a...
Confluent Cloud
Managed Kafka by the original Kafka authors. Cluster, topic, connector, KSQL, Schema Registry, Stream Governance, and Flink offerings exposed via a Confluent Cloud REST API and ...
StreamNative
Managed Apache Pulsar as a service from Pulsar's original contributors, with multi-cloud clusters, Functions, sources/sinks, and a control-plane REST API.
Server-Sent Events (SSE)
One-directional HTTP-based streaming from server to client using the `text/event-stream` media type. Defined by the HTML Living Standard EventSource API; widely used for LLM tok...
WebSocket
Full-duplex, bidirectional streaming protocol over a single TCP connection, upgraded from HTTP. RFC 6455. Foundation for chat, collaborative apps, market data, and real-time con...
gRPC Streaming
gRPC defines four RPC styles, three of which are streaming: server streaming, client streaming, and bidirectional streaming, all multiplexed over HTTP/2. The default streaming s...
GraphQL Subscriptions
The GraphQL operation type for receiving a stream of updates over a long-lived transport (typically WebSocket via the graphql-ws or graphql-transport-ws sub-protocols, or SSE). ...
Kafka Connect
Framework and runtime for source/sink connectors that move data into and out of Kafka. Distributed mode runs a REST-controlled cluster of workers managing connector and task lif...
Debezium
Change-data-capture (CDC) platform that streams row-level database changes (Postgres, MySQL, MongoDB, SQL Server, Oracle, Cassandra) as Kafka records using each database's nativ...
Apache Flink
Distributed, stateful stream-processing engine with event-time semantics, windowing, watermarks, and exactly-once state. SQL, DataStream, and Table APIs; reference engine for su...
Spark Structured Streaming
Stream-processing API built on Spark SQL using a micro-batch (and experimental continuous) execution model. Treats a stream as an unbounded table.
Materialize
Operational data warehouse and streaming SQL database built on Differential Dataflow. Maintains incrementally updated materialized views over streaming sources with millisecond ...
Tinybird
Real-time analytics platform built on ClickHouse; ingests streams via HTTP, Kafka, or CDC, exposes SQL pipes as parameterized HTTP API endpoints with auth tokens.
Bytewax
Open-source Python-native stream-processing framework built on Timely Dataflow; targets data scientists and Python teams building real-time ML and data pipelines.
Apache Beam
Unified batch and streaming programming model. Beam pipelines run on multiple runners (Dataflow, Flink, Spark, Samza), defining the canonical event-time / watermark / window / t...
Scroll within the panel for all 21 ·
GraphQL 1
Where a provider ships GraphQL, the schema is the contract. We profile it alongside the REST surface so the whole interface is legible in one place.
GraphQL schemas published by this provider.
Streaming GraphQL API
The GraphQL operation type for receiving a stream of updates over a long-lived transport (typically WebSocket via the graphql-ws or graphql-transport-ws sub-protocols, or SSE). ...
GRAPHQLSemantic 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.
Streaming Context
JSON-LDSpectral Rules 1
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.
Streaming API Rules
SPECTRALJSON Schema 3
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.
JSON Structure 1
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.
Streaming Stream Structure
JSON STRUCTUREExamples 3
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.
Streaming Stream 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.
Resources
Every other property we hold for Streaming — 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.
Documentation 3
Reference material describing how the API behaves
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Build 3
SDKs, sample code, and the tooling you integrate with
Access & Security 2
Authentication, authorization, and security posture
Commercial 1
Pricing, plans, and the legal terms of use
← All providers · Data indexed from github.com/api-evangelist/streaming · machine-readable index on apis.io
This is an independent, third-party profile of Streaming, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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