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 key difference between regular Spark and Spark Streaming?

• Regular Spark does not support cluster operations.

• Regular Spark requires manual data cleanup after job completion.

• Spark Streaming automatically cleans up all data after job completion.

• Spark Streaming maintains some data in the cluster after job completion.

What is a recommended solution if you face unexpected errors in Databricks?

• Use a different programming language.

• Ignore the errors and continue working.

• Run the code locally and replicate the setup.

• Switch to a different cloud service.

Why might Spark Streaming raise an exception when manipulating a DAG?

• The DAG is already in use and cannot be modified.

• The DAG is too large to handle.

• The DAG is not properly initialized.

• The DAG is not compatible with Spark Streaming.

What is a DAG in the context of Spark Streaming?

• A graphical user interface for Spark.

• A debugging tool for Spark.

• A directed acyclic graph used for processing.

• A type of data storage.

What should you do if Spark Streaming context is still open and causing issues?

• Close the context manually.

• Switch to a different context.

• Restart the cluster to resolve the issues.

• Ignore the context and continue.