Acceldata
Acceldata is an agentic data management platform that helps enterprises monitor, govern, and optimize data across cloud, lakehouse, and hybrid environments. The platform combines AI-powered agents with data observability to proactively detect issues, trace root causes, and automate remediation workflows. Key products include ADM (Agentic Data Management), ADOC (Acceldata Data Observability Cloud), Pulse for Hadoop environments, and Agent Studio for building custom AI agents. It supports integrations with Snowflake, Databricks, AWS, GCP, Azure, and Hadoop.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Acceldata the way a machine reads it — 91 machine-readable artifacts across 7 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 — Acceldata scores 69.6/100 (strong), 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 Acceldata
Each block below is one kind of artifact we hold for Acceldata. 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 7
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.
Acceldata Alerts API
Monitor and manage data quality and pipeline alerts
Acceldata Data Quality Rules API
Manage data quality rules and monitoring policies
Acceldata Datasets API
Manage and query dataset metadata and quality metrics
Acceldata Lineage API
Query data lineage and impact analysis
Acceldata Pipeline Jobs API
Monitor data pipeline job execution and health
Acceldata Roles API
Manage roles and permissions
Acceldata Users API
Manage users and user invitations
Scroll within the panel for all 7 ·
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.
Arazzo Workflows 7
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.
Acceldata Access Review
List organization users and the platform roles so access can be reviewed against defined permissions.
ARAZZOAcceldata Create and Verify Data Quality Rule
Resolve a dataset, create a data quality rule on it, and confirm the rule is registered.
ARAZZOAcceldata Critical Alert Sweep
List open critical alerts and acknowledge the first one when any are present.
ARAZZOAcceldata Dataset Quality Audit
Resolve a dataset, list its data quality rules, and map its lineage for impact analysis.
ARAZZOAcceldata Onboard Rule With Impact
Resolve a dataset, review its existing rules, create a new rule, and map downstream impact.
ARAZZOAcceldata Pipeline Failure Investigation
Find failed pipeline jobs, pull related critical alerts, and acknowledge the first one.
ARAZZOAcceldata Triage Dataset Alerts
Resolve a dataset, pull its open alerts, and acknowledge the most severe one.
ARAZZOScroll within the panel for all 7 ·
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.
Acceldata 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.
Acceldata Finops
FINOPSFeatures 9
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.
Agentic Data Management
AI-powered agents that proactively detect issues, trace root causes, and automate data quality remediation workflows
Data Quality Monitoring
Multi-variate anomaly detection, column-level profiling, and proactive monitoring across all data platforms
Data Lineage
End-to-end data lineage visualization with schema change management and column-level impact analysis
Pipeline Health Monitoring
Real-time SLA monitoring, bottleneck identification, and root cause analysis for data pipelines
Data Cost Management
Visibility into data spending, budget optimization, chargebacks, and cost forecasting across cloud environments
Business Notebook
Natural language interface with contextual memory for querying data quality and observability insights
Agent Studio
Low-code environment for building and deploying custom AI agents for data management workflows
BYOLLM Support
Bring Your Own Large Language Model for enterprise-controlled AI inference within data operations
xLake Reasoning Engine
Exabyte-scale, AI-aware processing engine supporting cloud hyperscalers and on-premises deployments
Scroll within the panel for all 9 ·
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.
Acceldata Adoc Api 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.
Acceldata API Rules
SPECTRALAcceldata API Rules
SPECTRALJSON Schema 17
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.
AcknowledgeAlertRequest
JSON SCHEMAAlertList
JSON SCHEMAAlert
JSON SCHEMACreateDataQualityRuleRequest
JSON SCHEMADataQualityRuleList
JSON SCHEMADataQualityRule
JSON SCHEMADatasetList
JSON SCHEMADataset
JSON SCHEMAErrorResponse
JSON SCHEMALineageGraph
JSON SCHEMALineageNode
JSON SCHEMAPipelineJobList
JSON SCHEMAPipelineJob
JSON SCHEMARoleList
JSON SCHEMARole
JSON SCHEMAUserList
JSON SCHEMAUser
JSON SCHEMAScroll within the panel for all 17 ·
JSON Structure 17
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.
Adoc Api Acknowledge Alert Request Structure
JSON STRUCTUREAdoc Api Alert List Structure
JSON STRUCTUREAdoc Api Alert Structure
JSON STRUCTUREAdoc Api Create Data Quality Rule Request Structure
JSON STRUCTUREAdoc Api Data Quality Rule List Structure
JSON STRUCTUREAdoc Api Data Quality Rule Structure
JSON STRUCTUREAdoc Api Dataset List Structure
JSON STRUCTUREAdoc Api Dataset Structure
JSON STRUCTUREAdoc Api Error Response Structure
JSON STRUCTUREAdoc Api Lineage Graph Structure
JSON STRUCTUREAdoc Api Lineage Node Structure
JSON STRUCTUREAdoc Api Pipeline Job List Structure
JSON STRUCTUREAdoc Api Pipeline Job Structure
JSON STRUCTUREAdoc Api Role List Structure
JSON STRUCTUREAdoc Api Role Structure
JSON STRUCTUREAdoc Api User List Structure
JSON STRUCTUREAdoc Api User Structure
JSON STRUCTUREScroll within the panel for all 17 ·
Examples 17
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.
Adoc Api Alert Example
EXAMPLEAdoc Api Alert List Example
EXAMPLEAdoc Api Dataset Example
EXAMPLEAdoc Api Role Example
EXAMPLEAdoc Api Role List Example
EXAMPLEAdoc Api User Example
EXAMPLEAdoc Api User List Example
EXAMPLEScroll within the panel for all 17 ·
Security Posture 3
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 6
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.
Data Quality Assurance
Continuously monitor and automatically remediate data quality issues across cloud and hybrid environments
Cloud Migration Validation
Validate data completeness, consistency, and accuracy during cloud migration projects
AI and LLM Data Readiness
Ensure data pipelines produce clean, reliable, and AI-ready datasets for training and inference
Cost Optimization and FinOps
Identify and reduce wasteful data pipeline and infrastructure costs with granular usage analytics
Data Reconciliation
Automatically detect and resolve discrepancies between source and target systems across platforms
Compliance and Data Governance
Track data lineage and access patterns to support regulatory compliance and data governance programs
Resources
Every other property we hold for Acceldata — 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 2
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 10
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 10 ·
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Commercial 3
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
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