Spark Programming in Python for Beginners with Apache Spark 3 - Spark Databases and Tables

This video tutorial covers the basics of Apache Spark as a database, focusing on the creation and management of databases and tables. It explains the concept of metadata and the role of the metastore, particularly the use of the Apache Hive metastore for persistence. The tutorial distinguishes between managed and unmanaged tables, highlighting their differences in data storage and management. It also discusses the implications of dropping tables and the future enhancements in Spark SQL, emphasizing the advantages of managed tables.

What are the two main components of a table in Spark?

• Table metadata and table views

• Table views and table schema

• Table schema and table data

• Table data and table metadata

What is the purpose of the metastore catalog in Spark?

• To store data files

• To hold metadata information

• To manage Spark sessions

• To execute SQL queries

Why did Spark decide to reuse the Apache Hive metastore?

• To provide a persistent and durable metastore

• To improve data storage efficiency

• To enhance SQL query performance

• For better data processing

What is the Spark SQL Warehouse directory used for?

• Storing unmanaged tables

• Managing Spark sessions

• Storing managed tables

• Executing SQL queries

What must you specify when creating an unmanaged table in Spark?

• Table views

• Table schema

• SQL query

• Data directory location

What happens when you drop a managed table in Spark?

• Data files are untouched

• Only data is deleted

• Both metadata and data are deleted

• Only metadata is deleted

Why are unmanaged tables considered external in Spark?

• They offer bucketing and sorting features

• They are stored in the warehouse directory

• Spark has limited control over them

• They are used for permanent data storage