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Early movement with low total volume — a signal worth watching before it broadens.
Why it is trending now. The surge follows enterprise teams scrambling to implement reliable data pipeline orchestration as AI workloads demand more sophisticated workflow management. Organizations are rapidly replacing brittle cron jobs and custom scripts with proper orchestration platforms to handle complex machine learning pipelines and real-time data processing requirements.
What it is. Apache Airflow orchestrates complex data workflows through Python-based directed acyclic graphs (DAGs). Data engineers and ML teams use it to schedule, monitor, and manage multi-step data pipelines across distributed systems.
What makes it different. Airflow treats workflows as code, allowing version control and programmatic pipeline creation rather than drag-and-drop interfaces used by traditional ETL tools.
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