2 . DDL Spark SQL

The video titled "2. DDL Spark SQL" provides an in-depth explanation of Data Definition Language (DDL) in Spark SQL. DDL commands are used to define, modify, and manage the structure of tables and other database objects in Spark. Here’s an outline of what you can expect:
Introduction to DDL in Spark SQL
Overview of DDL and its role in Spark SQL.
How it helps in defining schemas and managing tables in a distributed environment.
Creating Tables and Databases
Syntax for creating databases and tables using Spark SQL.
Examples of defining tables with specific schemas (e.g., column names, data types, etc.).
Alteration of Tables
Commands to modify existing table structures, such as adding or dropping columns.
Explanation of use cases where table alterations are necessary.
Dropping Tables and Databases
How to safely remove tables and databases.
The implications of dropping objects in a distributed data processing system.
Partitioning and External Tables
How DDL handles table partitioning for optimized data processing.
Explanation of external tables and their linkage with external storage systems.
Practical Demonstrations
Step-by-step demonstrations of DDL commands in action using Spark SQL.
Real-world examples that illustrate managing big data structures effectively.