Apache Spark 3 Essentials


We are living in an era of ‘big data’. Spark is a popular platform for analyzing big data. This course introduces Apache Spark to students. This class is taught with Python language and using Jupyter environment. This course covers the latest features in Spark version 3.

What You Will Learn

  • Spark ecosystem
  • New features in Spark3
  • Spark Shell
  • Spark Data structures (RDD / Dataframe / Dataset)
  • Spark SQL
  • Modern data formats and Spark
  • Spark API


Developers, Data analysts, and business analysts


2 days

Skill level

Introductory to Intermediate


Basic knowledge of Python language and Jupyter notebooks is preferred but not mandatory Even if you haven’t done any Python programming, Python is such an easy language to learn quickly. We will provide Python resources.

Lab Environment

A cloud-based lab environment will be provided to students, no need to install anything on the laptop

Students will need the following

  • A reasonably modern laptop with unrestricted connection to the Internet. Laptops with overly restrictive VPNs or firewalls may not work properly
  • Chrome browser

Detailed Outline

Spark Introduction

  • Big Data stacks, Hadoop, Spark
  • Spark-3 new features
  • Spark concepts and architecture
  • Spark components overview
  • Labs : Installing and running Spark

First Look at Spark

  • Spark shell
  • Spark web UIs
  • Analyzing dataset – part 1
  • Labs: Spark shell exploration

Spark Data structures

  • Partitions
  • Distributed execution
  • Operations : transformations and actions
  • Labs : Unstructured data analytics


  • Caching overview
  • Various caching mechanisms available in Spark
  • In memory file systems
  • Caching use cases and best practices
  • Labs: Benchmark of caching performance

Dataframes / Datasets

  • Dataframes Intro
  • Loading structured data (json, CSV) using Dataframes
  • Specifying schema for Dataframes
  • Labs : Dataframes, Datasets, Schema

Spark SQL

  • Spark SQL concepts and overview
  • Defining tables and importing datasets
  • Querying data using SQL
  • Handling various storage formats : JSON / Parquet / ORC
  • Adaptive Query Engine (AQE) (Spark 3 feature)
  • Labs : querying structured data using SQL; evaluating data formats

Spark and Hadoop

  • Hadoop Primer
  • Hadoop + Spark architecture
  • Running Spark on Hadoop
  • Processing HDFS files using Spark
  • Spark & Hive
  • Labs: Spark and Hive

Spark API

  • Overview of Spark APIs in Scala / Python
  • Life cycle of an Spark application
  • Spark APIs
  • Deploying Spark applications on YARN
  • Labs : Developing and deploying an Spark application