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Pipelinecentric

Pipelinecentric refers to an architectural and design approach that places data processing pipelines at the center of a system. In a pipelinecentric design, data flows through a sequence of processing stages, with each stage performing a specific transformation or enrichment before handing data to the next stage. Pipelines may be batch or streaming, and stages are typically decoupled with explicit interfaces and contracts. Data provenance, state management, and handling backpressure are common concerns, especially in streaming contexts.

A pipelinecentric system is often described as a dataflow graph, where nodes represent processing stages and

Applications include data engineering pipelines for ETL and analytics, media processing pipelines for transcoding or enhancement,

Benefits of this approach include clear separation of concerns, easier testing of individual stages, and the

See also: dataflow programming, ETL, streaming data pipelines, data contracts.

edges
represent
data
movement.
This
model
supports
modularity,
reuse
of
processing
components,
and
independent
scaling
of
stages.
It
contrasts
with
objectcentric
or
servicecentric
architectures
that
organize
work
around
entities
or
services
rather
than
the
flow
of
data.
and
real-time
event
processing
in
streaming
platforms.
Frameworks
and
tools
that
support
pipelinecentric
design
include
Apache
Beam
and
other
dataflow
systems,
Kafka
Streams,
Apache
NiFi,
and
orchestration
tools
like
Apache
Airflow.
ability
to
scale
bottleneck
stages.
Potential
drawbacks
include
added
end-to-end
latency,
complexity
of
monitoring
and
debugging
across
a
graph
of
stages,
challenges
with
schema
evolution
and
data
contracts,
and
the
need
for
robust
backpressure
and
fault-tolerance
mechanisms.