Apache Spark is a popular framework choice for big data analysis. Apache Spark is a multi-language engine for executing data engineering and machine learning on single-node machines or clusters. It has APIs for Python, Scala, Java, and R. Let us look at a few courses (paid and free) that can get you started in this technology.
Free courses
Apache Spark Tutorial – Spark Starter Kit [Udemy]
As per the course website, most courses for Spark lack in helping students understand the foundational concepts. The course will first answer questions like the need for Spark when Hadoop is already there, why we need RDD (before jumping into what is RDD), how Spark achieves speed and efficiency, how fault tolerance works in Spark, etc.
Then, after clearing such fundamental questions, students will learn about the similarities and differences between Spark and Hadoop and look at the challenges Spark solves. Students will also be provided with the foundational knowledge of understanding Resilient Distributed Dataset (RDDs) and exposed to some common misconceptions about RDD among new Spark learners.
Students will be given detailed guidance on key concepts behind Spark’s execution engine. Enthusiasts in distributed systems, computing, and big data tech can opt for this course.
For more information, click here.
Apache Spark Beginners Course [Simplilearn]
This course has a duration of seven hours and is self-paced. It will help the students understand the basics of big data, what Apache Spark is, and the architecture of Apache Spark. They will be taught how to install Apache Spark on Windows and Ubuntu. It will also inform students about the components of Spark, like Spark Streaming, Spark MLlib, and Spark SQL. The course is suitable for aspiring data scientists, software developers, BI professionals, IT professionals, project managers, etc.
For more information, click here.
Paid courses
Apache Spark for Java Developers [Udemy]
This course will teach participants to use functional style Java to define complex data processing jobs and learn the differences between the RDD and DataFrame APIs. It will also introduce how to use an SQL style syntax to produce reports against big data sets and use machine learning algorithms with big data and SparkML. It will teach the students how to connect Spark to Apache Kafka to process streams of big data and how structured streaming can be used to build pipelines with Kafka.
Java 8 is required for the course. Spark does not currently support Java 9+. Previous SQL will be useful for this course, but it will not be a constraint to go for the course.
For more information, click here.
Introduction to Apache Spark [Coursera]
This course is for people who are interested in understanding the core tools used to wrangle and analyse big data. They will get hands-on examples with Hadoop and Spark frameworks. In the assignments, the instructors will guide students to show data scientists apply the techniques such as Map-Reduce that are used to solve problems in big data.
For more information, click here.
Big Data, Hadoop and Spark basics [EdX] (free to audit)
It will introduce students to the features, benefits, and limitations of big data and explore some of the big data processing tools. They will get to understand how Hadoop, Hive, and Spark can help organisations to overcome big data challenges. The course will give an overview to the students of the different components that constitute Apache Spark. Students will also learn how RDDs enable parallel processing across the nodes of a Spark cluster. They will get hands-on knowledge to analyse data in Spark using PySpark and Spark SQL.
For more information, click here.
