You're building an Airflow DAG to automate data quality checks on the output of your ETL pipeline. The checks involve performing various data validation tasks like checking for missing values, ensuring data type consistency, and verifying data integrity based on specific business rules. How can you implement these checks within Airflow?
For automating the deployment of Spark applications within a Cloudera Data Engineering (CDE. environment using the CDE CLI, what is the primary consideration to ensure seamless integration with existing CI/CD pipelines?
A data engineer is deploying a Spark application on a Kubernetes cluster. To minimize downtime during updates, which Kubernetes deployment strategy should be considered?
You're working with a large Spark DataFrame and need to perform an aggregation operation (e.g., can you improve the performance of the aggregation? SUM, COUNT). How
Which Airflow feature allows you to template your tasks, enabling dynamic generation of task parameters such as table names for data quality checks?
