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metadataregistry

A metadata registry is a centralized repository that stores metadata definitions and their associations for data assets within an organization. It serves as a canonical source of information about data assets such as databases, data lakes, tables, columns, reports, and data products, along with their business meaning, owners, and technical characteristics.

Metadata registries capture descriptive, technical, and operational metadata, including names, data types, formats, constraints, lineage, usage

Key capabilities typically include modeling and versioning of metadata schemas, support for data lineage and impact

Architectures can be centralized, federated, or embedded within data catalogs. In practice, registries frequently serve as

Applications and standards emphasize data discovery, governance, and regulatory compliance, including lineage tracing for audits and

Challenges include keeping metadata up to date, scaling, ensuring privacy and security, avoiding duplication, and aligning

statistics,
access
controls,
data
steward
assignments,
and
mappings
between
assets.
They
may
also
include
business
glossaries,
tag
schemas,
and
mappings
to
external
standards
to
support
interoperability.
analysis,
search
and
discovery,
APIs
for
programmatic
access,
and
integration
with
data
pipelines
and
governance
platforms.
They
often
provide
governance
workflows,
access
control,
auditing,
and
change
management
to
support
compliance
and
stewardship.
backends
for
data
catalogs
or
governance
platforms.
Ingestion
mechanisms
are
push-based
from
data
sources
or
pull-based
via
connectors,
with
metadata
quality
checks
and
enrichment
as
common
features.
privacy
assessments.
Standards
such
as
ISO/IEC
11179,
Dublin
Core,
and
schema.org
mappings
facilitate
interoperability.
Common
platforms
described
as
metadata
registries
or
catalogs
include
Apache
Atlas,
AWS
Glue
Data
Catalog,
DataHub,
Amundsen,
and
Collibra.
with
evolving
business
definitions.
Effective
use
requires
clear
ownership,
governance
policies,
and
integration
with
data
pipelines.