Digital Lexicon Hub

The coordinated execution and control of multiple tasks, services or workflows to achieve an end-to-end outcome.

Orchestration makes multi-step processes such as data pipelines, order fulfillment or AI agent workflows reliable and observable, so failures are retried or flagged instead of silently breaking downstream work.

A data team defines a nightly Airflow DAG that extracts CRM data, loads it to the warehouse, runs dbt models and refreshes dashboards. Each task retries twice on failure, and a Slack alert fires if the run exceeds 2 hours.

An orchestrator coordinates tasks but does not make them correct or idempotent. Central workflows can become bottlenecks or single points of failure, and complex dependency graphs are hard to test and change.

An orchestrator holds a workflow definition, often a directed acyclic graph or state machine, that sets task order, dependencies, branching and parallelism. A scheduler or trigger starts runs, executors dispatch tasks to workers or services, and a state store records progress so failed steps can be retried or resumed. Examples include Apache Airflow for data pipelines, AWS Step Functions for service workflows and Kubernetes for container placement.