PySpark and AWS: Master Big Data with PySpark and AWS - Spark Streaming Cluster Restart

The video tutorial discusses the differences between regular Spark and Spark Streaming, highlighting the limitations of Spark Streaming in terms of data cleanup after job completion. It provides solutions for handling unexpected errors in Databricks, such as transferring code to a local setup. The tutorial explains the concept of DAGs and context in Spark, and how they can lead to issues when manipulating streaming data. Finally, it suggests restarting the cluster as a solution to resolve these issues.

What is a limitation of Spark Streaming mentioned in the text?

What should you do if you face unexpected errors while working with Databricks?

Explain the significance of the DAG in Spark Streaming as described in the text.

What steps can be taken to resolve issues with the streaming context?

How does restarting the cluster help in resolving issues in Spark Streaming?