Databand website screenshot

Databand

DBND (Databand) is an open-source Python framework — a library, a set of APIs, and a CLI — for building, tracking, and observing data pipelines across data ingestion, preparation, ETL/ELT, and machine-learning workflows. It collects pipeline metadata (runs, metrics, dataframes, schemas) to give data-engineering teams a system of record for runs and to detect pipeline failures and data quality issues before they hit downstream consumers. The DBND SDK integrates with Apache Airflow, Apache Spark, Databricks, dbt, and IBM DataStage. Databand was acquired by IBM in 2022 and is now IBM Databand, part of IBM's data observability offering; the open-source DBND SDK remains published on PyPI and GitHub under the databand-ai organization.

Databand is profiled on the APIs.io network. Tagged areas include Company, Enterprise, Data Observability, Data Engineering, and Data Pipelines.

Databand’s developer surface includes documentation, getting-started guide, support, CLI, changelog, and 9 more developer resources.

🌐 Visit website 📡 Source on GitHub
18.6/100 emerging ▬ flat Agent 0/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5
0 APIs
CompanyEnterpriseData ObservabilityData EngineeringData PipelinesData QualityDataOpsMachine LearningOpen SourceSDKCLIIBM

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 18.6/100 · emerging
Contract Quality 0.0 / 25
Developer Ergonomics 9.1 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 2.7 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 0/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/databand: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Databand Domain Security

DMARC

SECURITY

Resources

Get Started 2

Portal, sign-up, and the first successful call

GettingStarted
GettingStarted
 DeveloperPortal
DeveloperPortal

Documentation 1

Reference material describing how the API behaves

Documentation
Documentation

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

LLMsTxt
LLMsTxt

Build 5

SDKs, sample code, and the tooling you integrate with

GitHubOrganization
GitHubOrganization
 SourceCode
SourceCode
 Packages
Packages
 SDKs
SDKs
 CLI
CLI

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Operate 2

Status, limits, changes, and where to get help

Support
Support
 ChangeLog
ChangeLog

Company 2

The organization behind the API

Website
Website
 Homepage
Homepage

Source (apis.yml)

apis.yml Raw ↑ 
aid: databand
name: Databand
description: DBND (Databand) is an open-source Python framework — a library, a set of APIs, and a CLI — for building, tracking,
  and observing data pipelines across data ingestion, preparation, ETL/ELT, and machine-learning workflows. It collects pipeline
  metadata (runs, metrics, dataframes, schemas) to give data-engineering teams a system of record for runs and to detect pipeline
  failures and data quality issues before they hit downstream consumers. The DBND SDK integrates with Apache Airflow, Apache
  Spark, Databricks, dbt, and IBM DataStage. Databand was acquired by IBM in 2022 and is now IBM Databand, part of IBM's data
  observability offering; the open-source DBND SDK remains published on PyPI and GitHub under the databand-ai organization.
url: https://raw.githubusercontent.com/api-evangelist/databand/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- accel
- bessemer-venture-partners
x-tier: stub
x-tier-reason: portfolio-lead
x-acquired-by: IBM (2022)
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Unknown
  confidence: low
  source: []
  generated: '2026-07-22'
  method: derived
image: https://avatars.githubusercontent.com/u/48434944
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-18'
tags:
- Company
- Enterprise
- Data Observability
- Data Engineering
- Data Pipelines
- Data Quality
- DataOps
- Machine Learning
- Open Source
- SDK
- CLI
- IBM
apis: []
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
common:
- type: Website
  url: https://databand.ai/
- type: Homepage
  url: https://www.ibm.com/products/watsonx-data-integration/data-observability
- type: Documentation
  url: https://www.ibm.com/docs/en/dobd
- type: GettingStarted
  url: https://www.ibm.com/docs/en/dobd
- type: DeveloperPortal
  url: https://dbnd.readme.io/
- type: GitHubOrganization
  url: https://github.com/databand-ai
- type: SourceCode
  url: https://github.com/databand-ai/dbnd
- type: Support
  url: mailto:contact@databand.ai
- type: Packages
  url: packages/databand-packages.yml
- type: SDKs
  url: packages/databand-packages.yml
- type: CLI
  url: cli/databand-cli.yml
- type: ChangeLog
  url: changelog/databand-changelog.yml
- type: DomainSecurity
  url: security/databand-domain-security.yml
- type: LLMsTxt
  url: llms/databand-llms.txt
x-enrichment:
  date: '2026-07-19'
  status: backfilled
  pass: local-v1
  note: backfilled from .gitignore signal + verified work evidence