Data Engineering using Spark SQL - Getting Started - Getting Started - Overview

Let us get started to get into Spark SQL. In this module we will see how to launch and use Spark SQL.

Key Concepts Explanation

Overview of Spark Documentation

Spark documentation provides detailed information on how to utilize Spark SQL efficiently. It includes various guides, APIs, and examples to help users navigate through Spark SQL functionalities.

Launching and Using Spark SQL

To launch and use Spark SQL, first, ensure that Spark is properly installed. Then, initialize a SparkSession in your code to start working with Spark SQL commands.

import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
    .appName("Spark SQL Example")

Overview of Spark SQL Properties

Spark SQL properties can be configured to tune and optimize Spark SQL performance. These properties can be set using configuration files like spark-defaults.conf or programmatically in the SparkSession.

spark.conf.set("spark.sql.shuffle.partitions", "5")

Running OS Commands using Spark SQL

Spark SQL allows users to execute OS commands directly from the SparkSession. This feature enables seamless interaction between Spark and the underlying operating system.

val commandResult = spark.sql("! ls -l")

Understanding Warehouse Directory

Warehouse directory in Spark SQL is the default location to store managed tables and dataframes. It is specified during the SparkSession initialization and can be customized as per user requirements.

val spark = SparkSession.builder()
    .appName("Spark SQL Example")
    .config("spark.sql.warehouse.dir", "file:///path/to/warehouse/directory")

Hands-On Tasks

  1. Launch Spark SQL in your local environment and create a SparkSession.
  2. Set a custom warehouse directory for Spark SQL and create a managed table.


In conclusion, Spark SQL provides a powerful tool for executing SQL queries and interacting with data stored in Spark. By following the steps outlined in this module and practicing the hands-on tasks, you can enhance your skills in Spark SQL. Remember to engage with the community for further learning and support.

Watch the video tutorial here