Loading content
Data orchestration coordinates data flows, processing steps, and dependencies across heterogeneous systems to deliver reliable end-to-end pipelines. It defines control logic, scheduling, error handling, and operational practices for both batch and streaming workloads. Implementations integrate monitoring, pipeline versioning, and data-quality policies to ens…
Use this profile to understand the building block briefly, place it in the model, and switch to the 360° assessment when needed.
Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
Data orchestration coordinates when data jobs run, which dependencies they have, and how execution, retries, and monitoring work together so end-to-end data flows stay reliable.
The term belongs to the practice of distributed data pipelines, where many steps span different systems, schedules, and operating modes. As batch and streaming tasks multiplied, teams needed a control layer that centralizes ordering, approvals, failure handling, and reruns. Tools such as Apache Airflow make this orchestration visible as a concrete operating pattern.
Think of a control room above a network of tasks. Each step is a node in the run plan, and arrows show which upstream work must finish before the next execution can begin. Orchestration sets timing, checks status, triggers retries, and records delays or failures. The actual data processing happens inside the tasks; orchestration keeps the overall flow dependable.
Rules determine start time, order, approvals, and run states for tasks.
A run plan shows which steps depend on others and how they relate.
Recurring, event-driven, or ad hoc runs are placed in time and initiated.
Retries, alerts, pauses, and reruns define how disruptions are handled.
Logs, metrics, status, and lineage make a pipeline's condition traceable.
Checks before handoff prevent defective data from propagating unchecked.
Data orchestration matters when multiple sources, teams, or runtimes must converge into a dependable data flow, for example in nightly batch jobs, regular synchronizations, or hybrid batch/streaming platforms. It is especially useful when approvals, reruns, and ownership need to be explicit. The trade-off is additional operational complexity; retries or backfills can also repeat side effects if tasks are not idempotent.
Where this building block is located in the topic model.
No structure path available.
Explore how this building block connects to concepts, methods, technologies, and tools.
These sources establish the term and its professional meaning.
All direct connections of the current building block in a compact text view.
This classification shows where the building block typically matters, how demanding it is, and what kind of impact it has in the model.
The level within the organization (enterprise, domain, team) at which the AssetBlock is applied.