Schema validation in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is schema validation in Apache Spark?

Schema validation is the process of checking if the data matches the expected structure and data types defined in a schema before processing it.

beginner

Why is schema validation important in data processing?

It ensures data quality by catching errors early, prevents crashes during processing, and helps maintain consistent data formats.

intermediate

How do you define a schema in Apache Spark?

You define a schema using StructType and StructField objects that specify column names, data types, and nullability.

intermediate

What happens if data does not match the schema during validation?

Spark throws an error or skips invalid rows depending on the mode, preventing incorrect data from entering the system.

intermediate

Name two ways to enforce schema validation when reading data in Spark.

1. Provide an explicit schema when reading data. 2. Use DataFrame API options like 'mode' to control error handling.

What Spark class is used to define a schema?

A StructType

B DataFrame

C RDD

D SparkSession

StructType is used to define the schema structure in Spark.

What does schema validation help prevent?

A More storage space

B Data format errors

C Faster computation

D Automatic data cleaning

Schema validation helps catch data format errors before processing.

Which option controls how Spark handles corrupt records during schema validation?

A mode

B format

C inferSchema

D header

The 'mode' option controls error handling like dropping or failing on corrupt records.

If you do not provide a schema, Spark will:

A Use default schema with all strings

B Throw an error immediately

C Skip all data

D Infer schema automatically

Spark infers the schema automatically when no schema is provided.

Which data type is NOT part of Spark's schema types?

A FloatType

B IntegerType

C ArrayListType

D StructType

ArrayListType is not a Spark data type; Spark uses ArrayType instead.

Explain how schema validation works in Apache Spark and why it is useful.

Think about how Spark checks data before processing.

Describe how to define and apply a schema when reading a CSV file in Spark.

Focus on the steps to set schema explicitly.