Apache Spark 3 for Data Engineering and Analytics with Python - Reading CSV Files into DataFrame

The video tutorial guides users through loading sales CSV files into Databricks, creating a schema, and understanding the Databricks File System (DPFS) compared to Hadoop's HDFS. It covers file uploading, indexing, cluster management, creating headings, using keyboard shortcuts, and loading data into a DataFrame. The tutorial concludes with displaying data records and printing the schema.

What does DPFS stand for in the context of Databricks?

• Databricks Parallel File System

• Databricks Partitioned File System

• Databricks Data File System

• Databricks Distributed File System

Which system is DPFS similar to?

• NTFS

• FAT32

• EXT4

• HDFS

What is the purpose of the dropdown list under the sales data heading?

• To set a data path

• To create a new schema

• To select a file format

• To choose a cluster

How do you create an H2 heading in a Databricks notebook?

• Use a single hash

• Use a double hash

• Use a percentage sign

• Use a triple hash

What type is used to define a schema in Spark?

• StructType

• ArrayType

• ListType

• MapType

Which option is used to specify that CSV files contain headers?

• header: false

• schema: true

• header: true

• schema: false

What command is used to display the first ten records of a DataFrame?

• print(10)

• list(10)

• show(10)

• display(10)