data-engineering

Data pipeline orchestration across Airflow, Dagster, and Prefect is managed through unified workflow modeling. It coordinates cross-framework协同 tasks to enable reliable ingestion, scheduling, and dependency tracking.

Yes, data pipelines support both stream processing and batch workflows. The orchestration covers end-to-end scenarios from real-time streaming to scheduled batch processing within a single configured environment.

Data quality validation is integrated directly into ETL pipelines through workflow orchestration. It applies quality checks and guardrails during task execution to ensure reliable data ingestion and processing.

Modeling data assets and tracking dependencies relies on defining execution graphs within the orchestration framework. This approach maps relationships to ensure reliable workflow scheduling and governance.

Yes, data pipeline orchestration supports scalable configuration for large workflows. It applies guardrails and dependency tracking to maintain reliability across complex batch and streaming scenarios.