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By Dr Nivash Jeevanandam
Apache Spark is an open-source analytics engine that can process large amounts of data. Spark enables you to programme clusters with implicit data parallelism and fault tolerance through a programming interface.
Apache Spark is a framework for processing data. It can quickly handle big data sets and distribute data processing jobs across several computers, alone or alongside distributed computing technologies.
These two things are essential in the worlds of “big data” and “machine learning,” which need a lot of computing power to go through vast amounts of data. Spark also makes it easier for developers to do these tasks by giving them an easy-to-use API that hides a lot of the grunt work of distributed computing and processing of big data.
Let’s look at a few courses to help you get started with this technology.
Spark Starter Kit – Udemy
This course tries to fill in the gaps between what developers can find in the Apache Spark documentation and in other courses and what they want to know.
It tries to answer many 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 make computation faster? What is RDD abstraction, etcetera?
Apache Spark Beginners Course – Simplilearn
This course is self-paced and lasts for seven hours. It will help the students learn about the basics of big data, what Apache Spark is, and how it works. In addition, they will learn how to install Apache Spark on Windows and Ubuntu. Students will also learn about Spark’s parts, 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 wish to understand Apache Spark for Big Data. Key Hadoop components like Spark, Map Reduce, Hive, Pig, HBase, HDFS, YARN, Squoop, and Flume are fully introduced through hands-on practice.
You will learn Apache Spark and Python by following 12+ practical, real-world examples of analysing Big Data using PySpark and the Spark library in this free Spark course for Python developers. Additionally, it is one of the most well-liked Apache Spark courses on Coursera, with nearly 22K students already registered with more than 2000 4.9 ratings. Furthermore, you will start by learning about the architecture of Apache Spark before understanding the RDDs, or resilient distributed datasets, which are enormous collections of read-only data.
Introduction to Spark with sparklyr in R – DataCamp
Apache Spark is made to look at a lot of data quickly. The sparklyr package gives you the best of both worlds by letting you 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’ll learn about the Million Song Dataset throughout the course.
Apache Spark Fundamentals – Pluralsight
This Pluralsight course on Apache Spark is excellent if you want to start using it from scratch. It demonstrates why we cannot use Hadoop to examine massive data in the present era and how Apache Spark’s processing speed is beneficial. From the ground up, you will learn Spark in this course, starting with its history before building an application to analyse Wikipedia to understand the Apache Spark Core API better. You will learn about Spark libraries like Streaming and SQL APIs once you have a firm grasp of the Apache Spark Core library.
Finally, you’ll discover certain things you should steer clear of when working with Apache Spark. An excellent introduction to Apache Spark overall.
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Senior Research Writer at INDIAai
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