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

Let us go through the details related to Spark Documentation. It is very important for you to get comfortable with Spark Documentation if you are aspiring for open book certification exams like CCA 175.

  • Click here to go to latest Spark SQL and Data Frames documentation.

  • We typically get documentation for the latest version.

  • We can replace latest in the URL with the version of Spark to get the specific version’s official documentation.

  • Also, we have resources provided by databricks.

Key Concepts Explanation

Spark Documentation contains important information that can help you understand and work effectively with Spark. Here are some key concepts explained:

Interactive Queries

Interactive queries allow users to run queries interactively against data. They are beneficial for ad-hoc analysis. Here is an example of an interactive query:

SELECT * FROM table_name WHERE column_name = 'value';


DataFrames are the preferred method of working with structured data in Spark. They provide a more user-friendly API than RDDs. Here is how you can create a DataFrame in PySpark:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("example").getOrCreate()
df ="file.csv", header=True, inferSchema=True)

Hands-On Tasks

To practice working with Spark Documentation, you can perform the following hands-on tasks:

  1. Navigate to the official Spark Documentation linked above and explore the different sections.
  2. Try to find information on how to optimize Spark jobs for performance.


In conclusion, understanding Spark Documentation is essential for mastering Spark and preparing for certification exams. Make sure to familiarize yourself with the documentation and use it as a valuable resource in your Spark journey. Happy learning!

Watch the video tutorial here