Amazon Data Pipeline
AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. With AWS Data Pipeline, you can regularly access your data where it is stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. It supports data-driven workflows with retry, failure handling, and scheduling capabilities.
Reference-quality API operations across every facet — a rich contract, published governance, transparent operations, and machine-readable commercial terms.
API Evangelist profiles Amazon Data Pipeline the way a machine reads it — 86 machine-readable artifacts across 4 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 — Amazon Data Pipeline scores 71.3/100 (exemplar), with a separate agent-readiness read of 48/100 (agent ready). 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 Amazon Data Pipeline
Each block below is one kind of artifact we hold for Amazon Data Pipeline. 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 4
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.
Amazon Data Pipeline Pipeline Objects API
Operations for managing pipeline object definitions
Amazon Data Pipeline Pipeline Runs API
Operations for managing pipeline execution and task runs
Amazon Data Pipeline Pipelines API
Operations for managing data pipelines
Amazon Data Pipeline Tags API
Operations for managing pipeline tags
Postman Collections 1
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
AWS Data Pipeline API
POSTMANOpen 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).
AWS Data Pipeline API
OPEN COLLECTIONArazzo Workflows 9
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Amazon Data Pipeline Clone Pipeline
Copy an existing pipeline's definition into a brand-new pipeline and activate it.
ARAZZOAmazon Data Pipeline Deactivate and Delete
Stop a running pipeline and then permanently remove it and its run history.
ARAZZOAmazon Data Pipeline Export Definition
Confirm a pipeline exists and then export its active definition objects.
ARAZZOAmazon Data Pipeline Inspect Running Tasks
Find running task instances in a pipeline and pull their full object definitions.
ARAZZOAmazon Data Pipeline List and Describe
List all accessible pipelines and pull full metadata for the first page of them.
ARAZZOAmazon Data Pipeline Provision and Activate
Create an empty pipeline, populate its definition, activate it, and confirm its state.
ARAZZOAmazon Data Pipeline Redeploy Definition
Deactivate a pipeline, write a new definition, then reactivate it with the new objects.
ARAZZOAmazon Data Pipeline Tag and Confirm
Add governance tags to a pipeline and confirm they are attached.
ARAZZOAmazon Data Pipeline Validate Then Put Definition
Validate a candidate pipeline definition and only commit it when it is error free.
ARAZZOScroll within the panel for all 9 ·
Pricing 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.
Amazon Data Pipeline 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.
Features 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.
Data-Driven Workflows
Define complex data processing workflows with activities, data nodes, schedules, and preconditions using a declarative pipeline definition.
Multi-Service Integration
Move and transform data between Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift, and Amazon EMR in a single pipeline.
Flexible Scheduling
Schedule pipeline runs at fixed intervals (hourly, daily, weekly) or trigger them based on data availability preconditions.
Automated Retry and Failure Handling
Configure automatic retries for failed activities with configurable retry intervals, timeout settings, and failure notifications.
On-Premises Data Support
Process data from on-premises databases and file systems using the Data Pipeline Task Runner agent installed locally.
EMR Integration
Launch and manage Amazon EMR clusters as pipeline resources to run Hive, Pig, and MapReduce jobs as part of data workflows.
Pipeline Versioning
Manage active and latest pipeline definition versions, enabling updates to running pipelines without disrupting current execution.
Scroll within the panel for all 7 ·
Semantic 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.
Amazon Data Pipeline Context
JSON-LDSpectral Rules 2
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.
Amazon Data Pipeline API Rules
SPECTRALAmazon Data Pipeline API Rules
SPECTRALJSON Schema 16
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.
Activate Pipeline Request
JSON SCHEMACreate Pipeline Output
JSON SCHEMACreate Pipeline Request
JSON SCHEMADescribe Pipelines Output
JSON SCHEMADescribe Pipelines Request
JSON SCHEMAError
JSON SCHEMAField
JSON SCHEMAGet Pipeline Definition Output
JSON SCHEMAList Pipelines Output
JSON SCHEMAPipeline Description
JSON SCHEMAPipeline ID Name
JSON SCHEMAPipeline Object
JSON SCHEMAPut Pipeline Definition Output
JSON SCHEMAQuery Objects Output
JSON SCHEMATag
JSON SCHEMAValidation Error
JSON SCHEMAScroll within the panel for all 16 ·
JSON Structure 16
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.
Activate Pipeline Request Structure
JSON STRUCTURECreate Pipeline Output Structure
JSON STRUCTURECreate Pipeline Request Structure
JSON STRUCTUREDescribe Pipelines Output Structure
JSON STRUCTUREDescribe Pipelines Request Structure
JSON STRUCTUREError Structure
JSON STRUCTUREField Structure
JSON STRUCTUREGet Pipeline Definition Output Structure
JSON STRUCTUREList Pipelines Output Structure
JSON STRUCTUREPipeline Description Structure
JSON STRUCTUREPipeline Id Name Structure
JSON STRUCTUREPipeline Object Structure
JSON STRUCTUREPut Pipeline Definition Output Structure
JSON STRUCTUREQuery Objects Output Structure
JSON STRUCTURETag Structure
JSON STRUCTUREValidation Error Structure
JSON STRUCTUREScroll within the panel for all 16 ·
Examples 16
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.
Error Example
EXAMPLEField Example
EXAMPLEPipeline Description Example
EXAMPLEPipeline Id Name Example
EXAMPLEPipeline Object Example
EXAMPLEQuery Objects Output Example
EXAMPLETag Example
EXAMPLEValidation Error Example
EXAMPLEScroll within the panel for all 16 ·
Security Posture 4
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.
Daily ETL Workflows
Schedule daily extraction, transformation, and loading of data from relational databases into S3 or Redshift for analytics processing.
Log Processing Pipelines
Process application and server log files from S3 using EMR activities to generate aggregated reports and analytics datasets.
Database Migration
Migrate data between on-premises databases and AWS managed database services using scheduled pipeline activities.
Data Lake Ingestion
Automate the ingestion and transformation of raw data into structured formats in S3 data lakes for downstream analytics.
Cross-Region Data Replication
Replicate DynamoDB tables or S3 data across AWS regions using scheduled pipeline copy activities for disaster recovery.
Resources
Every other property we hold for Amazon Data Pipeline — 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 5
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 11
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 11 ·
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 4
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
← All providers · Data indexed from github.com/api-evangelist/amazon-data-pipeline · machine-readable index on apis.io