Dynamic Task Mapping is a feature in Apache Airflow 2.0 and later that allows you to dynamically generate tasks at runtime. This can be used to create a separate quality check task for each table in your database by iterating over a list of tables and generating a task for each one, ensuring all necessary quality checks are automated and scalable.

While option A might be necessary for specific metrics, option B and D highlight Airflow's built-in capabilities:Airflow Metrics: Allow you to track DAG run times, task execution times, and success/failure rates, providing insights into performance. Alerts and Notifications (Optional): Can be configured to trigger upon events like DAG failures or performance thresholds being breached, enabling proactive monitoring and troubleshooting.

A potential drawback of improperly configured bucketing in Hive is skewed data distribution, which can lead to an uneven load across nodes. If the bucketing column or the number of buckets is not chosen carefully, it may result in certain buckets being significantly larger than others. This imbalance can negate the performance benefits of bucketing by causing some nodes to do much more work than others, leading to bottlenecks and inefficient resource utilization during data processing tasks.

Spark SQL's windowing functions offer a powerful way to perform aggregations over specific time windows. Option A demonstrates this approach:from pyspark.sql.functions import month, avg
# Assuming "purchase_date" is a timestamp column
df = df.withColumn("month", month(df["purchase_date"]))
# Filter for the past year (assuming current date is stored in a variable "current_date") df_filtered = df.filter(df["purchase_date"].between(current_date - F.interval(l, "year"), current_datE. )
# Calculate average purchase amount per month
avg_purchase_df = df_filtered.groupBy("month").agg(avg("amount").alias("avg_purchase_amount")) avg_purchase_df.show()

When deploying a Spark application on CDE service, which is managed on Kubernetes, you would typically use the CDE CLI to submit and configure your Spark jobs. The correct command would involve the cde spark submit command along with -conf flags to specify configuration options such as executor memory and cores. This approach is tailored to the CDE environment, leveraging its CLI for configuration rather than directly interfacing with Kubernetes (kubectl) or using the generic spark-submit command.