Apache Spark 3 for Data Engineering and Analytics with Python - Creating a Database and Table

The video tutorial covers creating a database and table using Spark SQL. It explains the use of Data Definition Language (DDL) for defining database schemas and discusses advanced table options like file formats and partitioning. The tutorial emphasizes the importance of using the correct database to avoid creating tables in the default database. It also highlights the differences between managed and unmanaged tables, and the advantages of using the Delta file format for data integrity.

What is the purpose of using the 'USE' command in Spark SQL?

• To create a new table

• To update existing records

• To select a database for subsequent operations

• To delete a database

Why are triple quotes used in SQL statements?

• To allow multi-line statements

• To highlight the code

• To execute the code

• To comment out the code

What does DDL stand for in SQL?

• Data Deployment Language

• Data Definition Language

• Data Description Language

• Data Derivation Language

What happens if you create a table without the 'IF NOT EXISTS' clause and the table already exists?

• The statement fails

• The table is overwritten

• A new table is created with a different name

• The existing table is updated

Which file format is used by default if no format is specified during table creation in Databricks?

• CSV

• Parquet

• Delta

• JSON

What is the main advantage of using the Delta file format?

• It is faster to read

• It supports ACID transactions

• It is more compact

• It is easier to write

What is the difference between managed and unmanaged tables?

• Unmanaged tables are more secure

• Unmanaged tables are deleted with metadata

• Managed tables delete data with metadata

• Managed tables are faster