Spark Programming in Python for Beginners with Apache Spark 3 - Introduction to Data Transformation

The video tutorial covers the concept of transformations in Apache Spark, focusing on reading data, applying transformations, and writing it back. It explains the difference between data frames and database tables, and compares programmatic and SQL approaches to transformations. The tutorial lists various transformation techniques such as JOIN, union, aggregation, and custom expressions, and concludes with a learning path for mastering these techniques.

What is the primary goal of data engineering in Apache Spark?

• To create machine learning models

• To store data in cloud

• To read, transform, and write data

• To visualize data

Which two interfaces are used in Spark for handling data?

• SQL queries and XML files

• Data frames and database tables

• Database tables and CSV files

• Data frames and JSON files

What is the advantage of learning the programmatic approach for transformations?

• It provides more flexibility

• It is easier than SQL

• It is faster than SQL

• It requires less coding

Which operation is NOT mentioned as a transformation in Spark?

• Windowing

• Clustering

• Union

• JOIN

What can be achieved by using custom expressions in Spark transformations?

• Creating new data types

• Enhancing data security

• Covering most transformation requirements

• Improving data storage