The world’s most reliable data catalog
Eliminate your manual data governance bottlenecks!
Woven bridges the gap between application code and your data stack to solve your most critical data challenges
Govern Data Changes
Automate DataOps
The world’s most reliable data catalog
Woven bridges the gap between application code and your data stack to solve your most critical data challenges
schema: https://opendapi.org/spec/0-0-1/dapi.json
title: User
description: This data model represents a customer entity.
It includes essential information about the user, such as
their identification details, contact information, and
preferences.
urn: company.users.user_entity
type: entity
owner_team_urn: company.engineering.user_management
datastores:
sources:
- urn: company.postgres.user_db
business_purposes:
- user_management
- authentication
retention_days: 3650
sinks:
- urn: company.postgres.user_db
business_purposes:
- user_management
retention_days: 365
fields:
- name: user_id
data_type: integer
is_nullable: false
description: Unique numerical identifier for the user.
This is the primary key for the user record.
data_subjects_and_categories:
- subject_urn: user
category_urn: identifier.user_id
sensitivity_level: internal
is_personal_data: true
is_direct_identifier: false
- name: full_name
data_type: string
is_nullable: false
description: The user's full name, including first and last
names.
data_subjects_and_categories:
- subject_urn: user
category_urn: name
sensitivity_level: confidential
is_personal_data: true
is_direct_identifier: true
- name: email
data_type: string
is_nullable: true
description: The user's email address.
data_subjects_and_categories:
- subject_urn: user
category_urn: contact.email
sensitivity_level: confidential
is_personal_data: true
is_direct_identifier: true
- name: created_at
data_type: timestamp
is_nullable: false
description: The timestamp indicating when the user account
was created.
data_subjects_and_categories:
- subject_urn: user
category_urn: metadata
sensitivity_level: internal
is_personal_data: false
is_direct_identifier: false
- name: updated_at
data_type: timestamp
is_nullable: false
description: The timestamp indicating when the user account
was last updated.
data_subjects_and_categories:
- subject_urn: user
category_urn: metadata
sensitivity_level: internal
is_personal_data: false
is_direct_identifier: false
primary_key:
- user_id
privacy_requirements:
dsar_access_endpoint: /dsr/access/users/{id}
dsar_deletion_endpoint: /dsr/deletion/users/{id}
context:
service: user_service
integration: sqlalchemy
rel_model_path: models/user.py
rel_doc_path: docs/user_api.md
By integrating in your CI pipelines to keep metadata in-sync with your source code, Woven can enforce governance controls, automate your DataOps workflows, and streamline how engineering and data teams collaborate
Woven AI enriches updated schemas based on your metadata graph and data policies. By integrating into your existing CI pipeline, it's simple and familiar to review and commit the metadata suggestions.
See the product
Define your data policies in code or using Woven's governance portal. Each policy will appear in the developer experience as a task that must be completed before code is deployed. Find, fix, and prevent data issues before they impact production!
What workflows need to happen whenever a data model changes in code? That's what Woven automates for you! Native connectors and workflow configurations give you the power of declarative metadata. Think: Terraform for your data stack.
Change is the only constant. Woven is built to make sure your data stakeholdes and consumers know about upcoming and recent data model changes so that they can adapt. Or, use Woven to bring data consumers into the PR to ensure strong collaboration from the start.
Streamline sensitivity classification, PII masking, access controls, and more with Woven’s security features that keep data safe. Woven enforces your data privacy and security policies in your existing CI pipelines, then automates your SecOps workflows on schema change.
Accelerate dbt model development with Woven’s embedded metadata. Whenever an application schema is updated, Woven auto-updates your dbt source layer with schema, semantics, owner, quality tests, and more. Woven empowers Analytics Engineers to build robust, reliable data models faster, improving analytical insights and efficiency.
Simplify privacy compliance by enforcing data policies -- like personal data classification, data retention, and DSR integration -- prior to code being deployed. Woven’s "shift-left" governance brings privacy controls into the software development lifecycle, minimizing risk and ensuring regulatory adherence.
Develop GenAI applications faster with agentic access to Woven's metadata platform and our deep contextual understanding of data. Woven enhances accuracy, reliability, and trust in your GenAI applications.
Notifies data consumers and stakeholders of any changes to data models.
Automatically updates your catalog with metadata, including schema, semantics, classifications, ownership, and entity relationships.
Maintain your source and staging layers with model and field changes along with relevant data quality tests.
Ensures data accuracy by automatically updating data replication configurations. Selectively replicate certain columns.
Proactively monitors data quality by automatically updating data quality rules and freshness guarantees in your data observability platform.
Maintains data security by automatically updating access controls based on approved processing purposes.
Ensures compliance by automatically updating data retention policies.
Simplifies privacy compliance by automatically updating your privacy orchestrator with the necessary endpoints for handling access and deletion requests.
Enforces data model change management policies for your most critical data products to ensure tight collaboration between teams.
Leverages security classifications to apply your preferred PII masking strategy to your data.
Analyzes metadata to determine if a schema change impacts the RoPA, then updates the RoPA automatically.
Ensures real-time access to rich metadata for your GenAI applications to better understand your data ecosystem.
