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dataarchitecturen

Data architectures, or dataarchitecturen, refer to the structural design of data assets and the flow of data within an organization. They specify how data is collected, stored, integrated, secured, governed, and made available for analysis and operations.

Key components include data modeling (conceptual, logical, physical models), data storage and platforms (databases, data warehouses,

Architectural styles and patterns: centralized architectures with a single data warehouse provide a unified source of

Governance and lifecycle: data architectures align with enterprise strategy and governance frameworks (DAMA-DMBOK, TOGAF). They define

Relationship to analytics and operations: data architecture supports business intelligence, analytics, AI/ML, and operational data needs.

Challenges and trends: scale, cost, data quality, interoperability, and security remain priorities. Trends include data mesh,

data
lakes,
data
lakehouses),
data
integration
and
processing
(ETL/ELT,
streaming),
metadata
management,
data
quality,
and
master
data
management.
Security,
privacy,
access
control,
data
lineage
and
auditing
are
integral.
truth;
decentralized
approaches
use
data
marts
tied
to
specific
domains;
data
lakes
and
lakehouses
store
raw
or
semi-structured
data
with
processing
layers;
data
mesh
distributes
ownership
to
domain
teams;
data
fabric
offers
virtualization
and
cross-system
access.
standards,
data
stewardship,
and
policies
for
retention,
privacy,
and
compliance
(GDPR,
HIPAA).
The
architecture
evolves
through
design,
implementation,
and
modernization,
driven
by
business
needs
and
technology
changes.
Roles
often
include
data
architects,
data
engineers,
data
stewards,
and
platform
engineers.
lakehouse
architectures,
and
a
stronger
emphasis
on
metadata
and
automation.