Scenario: Your team is designing a complex data pipeline that involves multiple tasks with dependencies. Which workflow orchestration tool would you recommend, and why?
- AWS Glue - for its serverless ETL capabilities
- Apache Airflow - for its DAG (Directed Acyclic Graph) based architecture allowing complex task dependencies and scheduling
- Apache Spark - for its powerful in-memory processing capabilities
- Microsoft Azure Data Factory - for its integration with other Azure services
Apache Airflow would be recommended due to its DAG-based architecture, which enables the definition of complex workflows with dependencies between tasks. It provides a flexible and scalable solution for orchestrating data pipelines, allowing for easy scheduling, monitoring, and management of workflows. Additionally, Airflow offers a rich set of features such as task retries, logging, and extensibility through custom operators and hooks.
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