Apache Spark 3 for Data Engineering and Analytics with Python - Working with Missing or Bad Data

The video tutorial covers handling missing data in data engineering using Spark DataFrame API. It begins with creating a DataFrame with null values, then demonstrates how to drop rows with null values using the NA function. The tutorial also shows how to filter DataFrame records based on specific columns and use the describe function to obtain statistical summaries of columns. The video emphasizes practical coding steps and provides examples for each operation.

Why is it important for data engineers to handle missing data?

• To reduce data processing time

• To enhance data visualization

• To increase data storage

• To ensure data consistency and cleanliness

What is the first step in creating a DataFrame with missing values?

• Assigning a schema

• Using the describe function

• Copying code from a lesson

• Creating a heading

Which Spark function is used to drop rows with null values?

• filter

• describe

• drop

• select

How can you drop rows where all values are null?

• Use the parameter 'some'

• Use the parameter 'none'

• Use the parameter 'all'

• Use the parameter 'any'

What is a potential benefit of filtering a DataFrame on a specific column?

• It increases the number of null values

• It automatically fills missing values

• It allows focusing on relevant data

• It changes the data type of the column

What is the purpose of the describe function in Spark?

• To create a DataFrame

• To provide statistical summaries

• To filter data

• To drop null values

Which statistical information can be obtained from a string column using the describe function?

• Mean and standard deviation

• Count, Min, and Max

• Variance and median

• Sum and average