The framework for processing data is called Apache Spark. Whether used alone or in conjunction with distributed computing technologies, it can quickly handle large data volumes and distribute data processing tasks over numerous machines.
In the fields of “big data” and “machine learning,” which require a lot of computational power to sift through enormous volumes of data, these two elements are crucial. Additionally, Spark makes it simpler for developers to complete these tasks by providing them with a user-friendly API that hides a lot of the tedious labour involved in distributed computing and big data processing.
Let’s take a look at a couple training programmes that can get you started with this technology.
Udemy’s Spark Starter Kit
This course aims to fill in the knowledge gaps between what developers can learn from other courses and the Apache Spark documentation.
It attempts to address a number of the most frequently asked concerns about Apache Spark on StackOverflow and other forums, including why you need Apache Spark if you already have Hadoop and how Apache Spark differs from Hadoop. How, for instance, does Apache Spark speed up computation? RDD abstraction, etc., what is it?
Beginner’s Guide to Apache Spark – Simplilearn
It takes seven hours to complete this self-paced course. The fundamentals of big data, as well as what Apache Spark is and how it functions, will be explained to the students. They will also discover how to set up Apache Spark on Windows and Ubuntu. The components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL, will also be covered with the students. People who aspire to work as data scientists, software engineers, business intelligence (BI) professionals, IT specialists, project managers, etc. should take this course.
Coursera’s Hadoop Platform and Application Framework
Python developers who also want to grasp Apache Spark for Big Data should take this course. Through practical application, important Hadoop components like Spark, Map Reduce, Hive, Pig, HBase, HDFS, YARN, Squoop, and Flume are thoroughly introduced.
In this free Spark course for Python developers, you will learn Apache Spark and Python through observing 12+ realistic, real-world examples of analysing Big Data using PySpark and the Spark library. Additionally, with nearly 22K students already registered and more than 2000 4.9 rating, it is one of the most popular Apache Spark courses on Coursera. Additionally, before comprehending RDDs, or resilient distributed datasets, which are enormous collections of read-only data, you will first study Apache Spark’s architecture.
Sparklyr in R: An Introduction – DataCamp
Apache Spark is designed to swiftly examine a lot of data. The sparklyr package enables you to write dplyr R code that executes on a Spark cluster, giving you the best of both worlds. This course shows you how to use both Spark’s native interface and the dplyr interface to deal with Spark DataFrames. Additionally, it enables you to test out machine learning methods. Throughout the course, you will learn about the Million Song Dataset.
Fundamentals of Apache Spark – Pluralsight
If you want to learn how to use Apache Spark from scratch, I highly recommend this Pluralsight course. It explains why Apache Spark’s processing speed is advantageous and why Hadoop cannot be used to analyse vast amounts of data in the modern era. You will learn Spark in this course from scratch, beginning with its history before creating an application to analyse Wikipedia in order to better comprehend the Apache Spark Core API. Once you have a good understanding of the Apache Spark Core library, you will learn about Spark libraries including Streaming and SQL APIs.
Finally, you’ll learn about several practises you should avoid when using Apache Spark. Overall, a great introduction to Apache Spark.
