Spark employs a combination of data lineage tracking, checkpointing, and task re-execution to ensure job completion even in the event of node failures.

The ExternalTaskSensor is designed to wait for a task in another DAG to complete before proceeding. By using multiple instances of this sensor, each configured to wait for data loading to complete in a different target system, you can ensure that data quality checks are only initiated once all target systems have successfully completed their loading processes. This approach allows for precise control and synchronization across disparate systems and processes.

While filtering out outliers might seem like a way to address data skew, it's not a recommended strategy for managing skewed data during join operations because it can lead to loss of potentially valuable data. The goal in handling skew is to ensure even data distribution without discarding data. Salting keys, using broadcast joins for small datasets, and adjusting the number of partitions are more effective techniques for mitigating skew without sacrificing data integrity.

Network Policies in Kubernetes are used to control the flow of traffic to and from pods. They can be configured to allow traffic from specific namespaces, thereby restricting access to the Spark application pods.

Task re-execution is the mechanism in Spark that ensures tasks are retried on other nodes in the event of a node failure. This is a key feature of Spark's fault tolerance capability, allowing it to handle worker node failures without data loss.