Azure Databricks
Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure. It provides a collaborative workspace for data engineers, data scientists, and analysts to work together on big data and machine learning workloads.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Azure Databricks the way a machine reads it — 231 machine-readable artifacts across 39 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 — Azure Databricks scores 52.6/100 (developing), with a separate agent-readiness read of 37/100 (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 2026-08-17 · rubric v0.11.0
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How we profile Azure Databricks
Each block below is one kind of artifact we hold for Azure Databricks. 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 39
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.
Azure Databricks REST API
Core REST API for managing Azure Databricks workspaces, clusters, jobs, notebooks, and other resources programmatically.
Clusters API
Manage Databricks clusters for running Spark jobs including creating, starting, editing, listing, terminating, and deleting clusters.
Jobs API
Create, manage, and run jobs on Databricks clusters including scheduling, listing runs, and managing job permissions.
Workspace API
Manage notebooks, folders, and other workspace objects including listing, importing, exporting, and deleting workspace items.
DBFS API
Access Databricks File System (DBFS) for file operations including uploading, downloading, listing, and deleting files and directories.
Libraries API
Manage libraries and dependencies on clusters including installing, uninstalling, and listing library statuses.
Secrets API
Manage secrets and secret scopes for secure credential storage including creating scopes, putting secrets, and managing ACLs.
Token Management API
Create and manage personal access tokens for API authentication including creating, listing, and revoking tokens.
SQL Analytics API
Manage SQL warehouses, queries, and dashboards for Databricks SQL analytics workloads.
MLflow API
Track experiments, log metrics, and manage ML models using the MLflow tracking and registry APIs.
Instance Pools API
Create and manage instance pools to reduce cluster start and autoscaling times by maintaining a set of idle ready-to-use cloud instances.
Cluster Policies API
Create, list, and edit cluster policies to control cluster configurations and limit the ability to configure clusters based on a set of rules.
Repos API
Manage Git repositories within Databricks workspaces for version control of notebooks and files.
Git Credentials API
Manage Git credentials for authenticating with Git providers when using Databricks Repos.
Pipelines API
Create, edit, delete, start, and view details about Delta Live Tables pipelines for building reliable data pipelines.
Permissions API
Manage permissions on workspace objects including clusters, jobs, notebooks, and other resources using access control lists.
Unity Catalog - Catalogs API
Manage Unity Catalog catalogs for organizing and governing data assets across workspaces.
Unity Catalog - Schemas API
Manage schemas within Unity Catalog catalogs for organizing tables, views, and functions.
Unity Catalog - Tables API
Manage tables within Unity Catalog schemas including listing, getting, and deleting tables.
Unity Catalog - Volumes API
Manage Unity Catalog volumes for governing non-tabular data such as files and directories.
Unity Catalog - Grants API
Manage permissions and grants on Unity Catalog objects including catalogs, schemas, tables, and other securable objects.
Unity Catalog - External Locations API
Manage external locations in Unity Catalog for connecting to cloud storage paths.
Unity Catalog - Storage Credentials API
Manage storage credentials in Unity Catalog for authenticating access to cloud storage.
Unity Catalog - Metastores API
Manage Unity Catalog metastores which serve as the top-level container for data governance.
Model Serving Endpoints API
Create and manage model serving endpoints for deploying machine learning models as REST API endpoints.
Model Registry API
Manage registered models and model versions in the Databricks Model Registry for model lifecycle management.
Registered Models API
Manage registered models in Unity Catalog for centralized model governance and sharing.
Global Init Scripts API
Manage global cluster initialization scripts that run on every cluster in the workspace.
IP Access Lists API
Manage IP access lists to control network access to Azure Databricks workspaces.
Statement Execution API
Execute SQL statements on SQL warehouses and retrieve results for programmatic SQL access.
Command Execution API
Execute commands on running clusters and retrieve results programmatically.
Files API
Manage files in Unity Catalog volumes and workspace filesystem with operations for uploading, downloading, and deleting files.
Apps API
Deploy and manage Databricks Apps including creating, starting, stopping, and listing custom applications.
Lakeview API
Manage Lakeview dashboards programmatically including creating, updating, and publishing dashboards.
Online Tables API
Manage online tables for low-latency serving of feature data in Unity Catalog.
Vector Search Indexes API
Manage vector search indexes for similarity search and retrieval-augmented generation workloads.
Vector Search Endpoints API
Manage vector search endpoints for hosting vector search indexes.
Query History API
Retrieve query history for SQL warehouses including query text, status, and performance metrics.
Account SCIM API
Manage users, groups, and service principals across the Databricks account using SCIM 2.0 protocol.
Scroll within the panel for all 39 ·
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.
Azure Databricks REST API
POSTMANOpen Collections 5
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).
API Collection
OPEN COLLECTIONAzure Databricks REST API
OPEN COLLECTIONAzure Databricks REST Clusters API
OPEN COLLECTIONAzure Databricks REST Clusters Jobs API
OPEN COLLECTIONAzure Databricks REST Clusters Workspace API
OPEN COLLECTIONArazzo Workflows 20
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.
Azure Databricks Back Up a Notebook by Export and Re-import
Confirm a notebook, export its content, and re-import it to a backup path.
ARAZZOAzure Databricks Cancel an Active Job Run
Cancel a run and poll until its life cycle state is TERMINATED.
ARAZZOAzure Databricks Clean Up the Latest Completed Job Run
Find a job's latest completed run, confirm it, and delete it.
ARAZZOAzure Databricks Cluster Health Diagnostics
Read a cluster's state then pull its recent events for diagnosis.
ARAZZOAzure Databricks Create a Directory and Import a Notebook
Make a workspace directory, import a notebook into it, then verify it.
ARAZZOAzure Databricks Create a Job and Run It to Completion
Create a notebook job, trigger a run, and poll until TERMINATED.
ARAZZOAzure Databricks Safely Delete a Workspace Directory
List a directory, confirm it is a directory, then recursively delete it.
ARAZZOAzure Databricks Import a Notebook and Run It
Import a notebook, confirm it landed, then submit a run of it.
ARAZZOAzure Databricks Pin the First Listed Cluster
List clusters, pick the first, and pin it so it is always retained.
ARAZZOAzure Databricks Preflight and Create a Cluster
Resolve a valid Spark version and node type, then create a cluster.
ARAZZOAzure Databricks Provision a Cluster and Run a Job on It
Create a cluster, wait until RUNNING, create a job on it, then run it.
ARAZZOAzure Databricks Provision and Wait for Cluster
Create a cluster and poll its state until it reaches RUNNING.
ARAZZOAzure Databricks Overwrite Job Settings and Verify
Reset all of a job's settings, then read the job back to confirm.
ARAZZOAzure Databricks Resize a Running Cluster and Wait
Edit a running cluster's worker count and poll until it is RUNNING.
ARAZZOAzure Databricks Restart a Running Cluster and Wait
Restart a running cluster and poll until it returns to RUNNING.
ARAZZOAzure Databricks Run an Existing Job and Wait
Trigger an existing job with parameters and poll the run to completion.
ARAZZOAzure Databricks Start a Terminated Cluster and Wait
Start a terminated cluster and poll its state until RUNNING.
ARAZZOAzure Databricks Submit a One-time Run and Wait
Submit a one-time notebook run without a job and poll to completion.
ARAZZOAzure Databricks Terminate and Permanently Delete a Cluster
Terminate a cluster, wait until TERMINATED, then permanently delete it.
ARAZZOAzure Databricks Update a Job and Re-run It
Partially update a job's settings, then trigger and poll a fresh run.
ARAZZOScroll within the panel for all 20 ·
Pricing Plans 2
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 2
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.
Azure Databricks Rate Limits
RATE LIMITSMicrosoft Azure Databricks Rate Limits
RATE LIMITSFinOps 2
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 12
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.
Scroll within the panel for all 12 ·
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.
Azure Databricks Context
JSON-LDSpectral Rules 3
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.
Azure Databricks API Rules
SPECTRALAzure Databricks API Rules
SPECTRALAzure Databricks API Rules
SPECTRALJSON Schema 45
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.
AutoScale
JSON SCHEMAAzureAttributes
JSON SCHEMAClusterEvent
JSON SCHEMAClusterInfo
JSON SCHEMAClusterLogConf
JSON SCHEMAAzure Databricks Cluster
JSON SCHEMACreateClusterRequest
JSON SCHEMACronSchedule
JSON SCHEMAEmailNotifications
JSON SCHEMAError
JSON SCHEMAGitSource
JSON SCHEMAInitScriptInfo
JSON SCHEMAJobCluster
JSON SCHEMAJob
JSON SCHEMAJobSettings
JSON SCHEMALibrary
JSON SCHEMANodeType
JSON SCHEMARun
JSON SCHEMARunState
JSON SCHEMASparkNode
JSON SCHEMATaskSettings
JSON SCHEMAWebhookNotifications
JSON SCHEMAWorkspaceObject
JSON SCHEMAAutoScale
JSON SCHEMAAzureAttributes
JSON SCHEMAClusterEvent
JSON SCHEMAClusterInfo
JSON SCHEMAClusterLogConf
JSON SCHEMACreateClusterRequest
JSON SCHEMACronSchedule
JSON SCHEMAEmailNotifications
JSON SCHEMAError
JSON SCHEMAGitSource
JSON SCHEMAInitScriptInfo
JSON SCHEMAJob
JSON SCHEMAJobCluster
JSON SCHEMAJobSettings
JSON SCHEMALibrary
JSON SCHEMANodeType
JSON SCHEMARun
JSON SCHEMARunState
JSON SCHEMASparkNode
JSON SCHEMATaskSettings
JSON SCHEMAWebhookNotifications
JSON SCHEMAWorkspaceObject
JSON SCHEMAScroll within the panel for all 45 ·
JSON Structure 23
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.
Azure Databricks Auto Scale Structure
JSON STRUCTUREAzure Databricks Azure Attributes Structure
JSON STRUCTUREAzure Databricks Cluster Event Structure
JSON STRUCTUREAzure Databricks Cluster Info Structure
JSON STRUCTUREAzure Databricks Cluster Log Conf Structure
JSON STRUCTUREAzure Databricks Create Cluster Request Structure
JSON STRUCTUREAzure Databricks Cron Schedule Structure
JSON STRUCTUREAzure Databricks Email Notifications Structure
JSON STRUCTUREAzure Databricks Error Structure
JSON STRUCTUREAzure Databricks Git Source Structure
JSON STRUCTUREAzure Databricks Init Script Info Structure
JSON STRUCTUREAzure Databricks Job Cluster Structure
JSON STRUCTUREAzure Databricks Job Settings Structure
JSON STRUCTUREAzure Databricks Job Structure
JSON STRUCTUREAzure Databricks Library Structure
JSON STRUCTUREAzure Databricks Node Type Structure
JSON STRUCTUREAzure Databricks Run State Structure
JSON STRUCTUREAzure Databricks Run Structure
JSON STRUCTUREAzure Databricks Spark Node Structure
JSON STRUCTUREAzure Databricks Task Settings Structure
JSON STRUCTUREAzure Databricks Webhook Notifications Structure
JSON STRUCTUREAzure Databricks Workspace Object Structure
JSON STRUCTUREMicrosoft Azure Databricks Structure
JSON STRUCTUREScroll within the panel for all 23 ·
Examples 54
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.
Azure Databricks Job Example
EXAMPLEAzure Databricks Run Example
EXAMPLEScroll within the panel for all 54 ·
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.
Scopes 2
OAuth scopes are the vocabulary of least-privilege access. Profiling them shows exactly what an integration — or an agent acting on a user's behalf — is allowed to do.
OAuth scopes governing access to this provider's APIs.
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 8
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.
Scroll within the panel for all 8 ·
Integrations 8
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Scroll within the panel for all 8 ·
Resources
Every other property we hold for Azure Databricks — 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 1
Portal, sign-up, and the first successful call
Documentation 4
Reference material describing how the API behaves
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 23
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 23 ·
Build 9
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 9 ·
Access & Security 7
Authentication, authorization, and security posture
Scroll within the panel for all 7 ·
Operate 6
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/microsoft-azure-databricks · machine-readable index on apis.io
This is an independent, third-party profile of Azure Databricks, 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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