Scenario: You need to schedule and monitor daily ETL jobs for your organization's data warehouse. Which features of Apache Airflow would be particularly useful in this scenario?
- Automated data quality checks, Schema evolution management, Data lineage tracking, Integrated data catalog
- Built-in data transformation functions, Real-time data processing, Machine learning integration, No-code ETL development
- DAG scheduling, Task dependencies, Monitoring dashboard, Retry mechanism
- Multi-cloud deployment, Serverless architecture, Managed Spark clusters, Cost optimization
Features such as DAG scheduling, task dependencies, monitoring dashboard, and retry mechanism in Apache Airflow would be particularly useful in scheduling and monitoring daily ETL jobs. DAG scheduling allows defining workflows with dependencies, task dependencies ensure tasks execute in the desired order, the monitoring dashboard provides visibility into job status, and the retry mechanism helps handle failures automatically, ensuring data pipelines complete successfully.
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