Orchestration
Learn how data orchestration automates and coordinates workflows. Discover how tools like Airflow and Dagster schedule jobs and manage dependencies in data pipelines.
Orchestration: Automating and Coordinating Data Workflows
Overview
Orchestration in data engineering is the practice of automating and coordinating a sequence of tasks or jobs across your data stack. An orchestrator acts as a “conductor” for data workflows. It ensures that each step (extracting data, transforming it, loading it, etc.) happens at the right time, in the right order, and with proper handling of failures. In effect, orchestration schedules and manages dependencies so that data pipelines run reliably end to end. A data orchestration system lets engineers define workflows as code (often using a Directed Acyclic Graph or DAG). Each task in the DAG is a discrete unit and edges define dependencies.
Orchestrator Functions
* Schedule Jobs: Triggers pipeline runs on a calendar schedule or in response to events, replacing manual execution.
* Manage Dependencies: Ensures that tasks run in the correct, predefined order (e.g., transformation does not begin until extraction is complete).
* Handle Failures Gracefully: Can retry failed tasks according to policies or alert engineers, preventing a single failure from breaking downstream processes.
* Provide Monitoring: Includes a user interface to show the status of job runs and task histories, allowing teams to quickly see running, succeeded, or failed jobs.
Orchestration tools automate and coordinate complex pipelines, ensuring all pieces of a data pipeline work together smoothly with minimal manual intervention, which is critical for reliable BI operations.