Apache Spark 3 for Data Engineering and Analytics with Python - Spark SQL Introduction

The video introduces Spark SQL, explaining that SQL stands for Structured Query Language, which allows interaction with data structures in relational databases. It covers SQL's capabilities, such as creating, retrieving, updating, and deleting records. The video highlights Spark SQL's compliance with SQL standards and its integration with the DataFrame API. It discusses the advantages of using Spark SQL, including its ability to execute Java bytecode over a cluster. The video concludes by encouraging practice and introducing Databricks as a platform for learning.

What does SQL stand for?

• Structured Query Language

• Standard Query Language

• Simple Query Language

• Systematic Query Language

Which of the following is NOT a basic SQL operation?

• Compile code

• Create databases

• Insert records

• Execute queries

What must technologies claiming to support SQL ensure?

• They use Java bytecode

• They are ENC or ISO compliant

• They are compatible with Databricks

• They support only select and update commands

How does Spark SQL execute SQL commands?

• By converting them into Java bytecode

• By directly executing them on a database

• By converting them into Python scripts

• By using a standalone SQL interpreter

What platform is introduced for practicing Spark SQL?

• Azure

• AWS

• Databricks

• Google Cloud