Five Best Online Courses for Apache Spark

Apache Spark is a data processing framework. It can quickly handle large data sets and distribute data processing tasks across multiple computers, either alone or alongside distributed computing technologies.

Both of these things are essential in the worlds of “big data” and “machine learning”, which need a lot of computing power to sift through large amounts of data. Spark also makes it easier for developers to perform these tasks by giving them an easy-to-use API that hides much of the tedious work of distributed computing and big data processing.

Let’s look at some courses to help you get started with this technology.

Spark Starter Kit – Udemy

This course attempts to fill the gap between what developers can find in the Apache Spark documentation and other courses and what they want to know.

It tries to answer most of the most common Apache Spark questions asked on StackOverflow and other forums, such as why you need Apache Spark if you already have Hadoop and what makes Apache Spark different from Hadoop. For example, how does Apache Spark speed up computation? What is RDD abstraction, etc. ?

Apache Spark Beginners Course – Simplilearn

This course is self-paced and lasts seven hours. It will help students learn the basics of Big Data, what Apache Spark is and how it works. Additionally, they will learn how to install Apache Spark on Windows and Ubuntu. Students will also learn about Spark components, such as Spark Streaming, Spark MLlib, and Spark SQL. The course is suitable for people who want to become data scientists, software developers, business intelligence (BI) experts, IT experts, project managers, etc.

Hadoop Platform and Application Framework – Coursera

This course is ideal for Python developers who also want to understand Apache Spark for Big Data. Key components of Hadoop such as Spark, Map Reduce, Hive, Pig, HBase, HDFS, YARN, Squoop, and Flume are fully introduced through hands-on.

You’ll learn Apache Spark and Python by following over 12 hands-on, real-world examples of big data analysis using PySpark and the Spark library in this free Spark course for Python developers. Additionally, it is one of the most popular Apache Spark courses on Coursera, with nearly 22,000 students already enrolled with over 2,000 4.9 grades. Additionally, you will start by getting familiar with the architecture of Apache Spark before understanding RDDs, or Resilient Distributed Datasets, which are huge collections of read-only data.

Introduction to Spark with sparklyr in R – DataCamp

Apache Spark is designed to examine lots of data quickly. The sparklyr package gives you the best of both worlds by allowing you to write dplyr R code that runs on a Spark cluster. This course teaches you how to work with Spark DataFrames using the dplyr interface and Spark’s native interface. It also lets you try out machine learning techniques. You will learn about the Million Song data set throughout the course.

Apache Spark Fundamentals – Pluralsight

This Pluralsight course on Apache Spark is great if you want to start using it from scratch. This shows why we can’t use Hadoop to examine big data in today’s era and how Apache Spark’s processing speed is beneficial. You will learn Spark from scratch in this course, starting with its history before building an application to analyze Wikipedia to better understand the Apache Spark Core API. You will learn about Spark libraries such as Streaming and SQL APIs once you have mastered the Apache Spark Core library.

Finally, you’ll learn some things you should avoid when working with Apache Spark. A great introduction to Apache Spark as a whole.