aid: amazon-glue-databrew
name: Amazon Glue DataBrew
description: AWS Glue DataBrew is a visual data preparation tool that makes it easy for data analysts and data scientists
  to clean and normalize data to prepare it for analytics and machine learning. It provides over 250 pre-built transformations
  to automate data preparation tasks.
type: Index
image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg
tags:
- AWS
- Data Analytics
- Data Preparation
- ETL
- Machine Learning
url: https://raw.githubusercontent.com/api-evangelist/amazon-glue-databrew/refs/heads/main/apis.yml
created: '2026-03-16'
modified: '2026-05-19'
specificationVersion: '0.19'
apis:
- aid: amazon-glue-databrew:aws-glue-databrew-api
  name: AWS Glue DataBrew API
  description: The AWS Glue DataBrew API provides programmatic access to create and manage datasets, recipes, projects, jobs,
    and rulesets for visual data preparation and transformation workflows.
  humanURL: https://aws.amazon.com/glue/features/databrew/
  baseURL: https://databrew.amazonaws.com
  tags:
  - Data Analytics
  - Data Preparation
  - ETL
  properties:
  - type: Documentation
    url: https://docs.aws.amazon.com/databrew/latest/dg/API_Reference.html
  - type: OpenAPI
    url: openapi/amazon-glue-databrew-openapi.yaml
  - type: GettingStarted
    url: https://aws.amazon.com/glue/features/databrew/
  - type: Pricing
    url: https://aws.amazon.com/glue/pricing/
  - type: FAQ
    url: https://aws.amazon.com/glue/faqs/
  - type: APIReference
    url: https://docs.aws.amazon.com/databrew/latest/dg/API_Reference.html
  - type: Authentication
    url: https://docs.aws.amazon.com/general/latest/gr/signature-version-4.html
  - type: JSONSchema
    url: json-schema/glue-databrew-dataset-schema.json
  - type: JSONLD
    url: json-ld/amazon-glue-databrew-context.jsonld
  - type: NaftikoCapability
    url: capabilities/amazon-glue-databrew.yaml
common:
- type: Portal
  url: https://aws.amazon.com/glue/features/databrew/
- type: Documentation
  url: https://docs.aws.amazon.com/databrew/
- type: TermsOfService
  url: https://aws.amazon.com/service-terms/
- type: PrivacyPolicy
  url: https://aws.amazon.com/privacy/
- type: Support
  url: https://aws.amazon.com/premiumsupport/
- type: Blog
  url: https://aws.amazon.com/blogs/big-data/tag/aws-glue-databrew/
- type: GitHubOrganization
  url: https://github.com/aws
- type: Console
  url: https://console.aws.amazon.com/databrew/
- type: SignUp
  url: https://portal.aws.amazon.com/billing/signup
- type: StatusPage
  url: https://health.aws.amazon.com/health/status
- type: Contact
  url: https://aws.amazon.com/contact-us/
- type: SpectralRules
  url: rules/amazon-glue-databrew-spectral-rules.yml
- type: Vocabulary
  url: vocabulary/amazon-glue-databrew-vocabulary.yaml
- type: Features
  data:
  - name: 250+ Pre-Built Transformations
    description: Apply over 250 ready-to-use transformations without writing code, including filtering, normalizing, aggregating,
      and reformatting data.
  - name: Visual Data Preparation Interface
    description: Interactive visual interface to explore and transform data without writing code.
  - name: Recipe-Based Transformations
    description: Save transformation steps as reusable recipes that can be versioned and shared across teams.
  - name: Data Profiling
    description: Automatically profile datasets to understand data quality, distribution, and statistics.
  - name: Data Quality Rules
    description: Define and enforce data quality rules with rulesets to validate data before processing.
  - name: Collaborative Projects
    description: Create shared projects for team-based data preparation with centralized management.
- type: UseCases
  data:
  - name: Analytics Data Preparation
    description: Clean, normalize, and transform raw data for business analytics dashboards and reports.
  - name: Machine Learning Feature Engineering
    description: Prepare and transform features from raw data for training machine learning models.
  - name: Data Quality Validation
    description: Profile datasets and apply quality rules to ensure data meets standards before processing.
  - name: ETL Pipeline Automation
    description: Automate recurring data transformation jobs as part of data pipeline workflows.
- type: Integrations
  data:
  - name: Amazon S3
    description: Read input datasets from and write transformed output to S3 buckets.
  - name: AWS Glue Data Catalog
    description: Connect to Glue Data Catalog tables as data sources.
  - name: Amazon Redshift
    description: Connect to Redshift databases as data sources for preparation.
  - name: Amazon RDS
    description: Use RDS databases as input sources for DataBrew transformation.
  - name: AWS Lake Formation
    description: Integrate with Lake Formation for secure data lake access.
- type: Integrations
  url: https://aws.amazon.com/marketplace
integrations:
- name: Sign in
- name: Agent Mode
- name: Why AWS Marketplace?
- name: Get started in AWS Marketplace
- name: Industry
- name: Resources
- name: Become a Channel Partner
- name: Sell in AWS Marketplace
- name: Manage Your Account
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com