Data Engineering Spark SQL - Managing Tables - DDL & DML - Overview of Data Types

This article provides a step-by-step guide on creating tables and understanding data types in Spark SQL. Watch the video below to get a visual understanding of the concepts discussed in this article.

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Key Concepts Explanation

Overview of Data Types

Spark SQL supports a wide range of data types including Numeric (INT, BIGINT, FLOAT), Alpha Numeric or String (CHAR, VARCHAR, STRING), Date and Timestamp (DATE, TIMESTAMP), Special Data Types (ARRAY, STRUCT), and Boolean (BOOLEAN).

To create a table with specific data types, we need to consider the file format, delimiter options, and other clauses under DELIMITED ROW FORMAT.

Creating Tables

We will walk you through the process of creating a table ‘students’ with columns for student details like name, phone numbers, and address. The table will be stored as a TEXTFILE with specific delimiter settings.

Hands-On Tasks

  1. Execute code snippets to create a database ‘itversity_sms’ and switch to it.
  2. Create a table ‘students’ with defined columns and data types.
  3. Insert sample data into the ‘students’ table.
  4. Query the ‘students’ table to view the inserted data.


In this article, we covered the basics of creating tables and understanding data types in Spark SQL. Practice these concepts by performing hands-on tasks and feel free to engage with the community for further learning.

Start your Spark context and dive into the world of managing tables and data types in Spark SQL!

Watch the video tutorial here