Apache Spark for Java Developers

Get processing Big Data using RDDs, DataFrames, SparkSQL and Machine Learning - and real time streaming with Kafka!

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What you'll learn

1. Use functional style Java to define complex data processing jobs
2. Learn the differences between the RDD and DataFrame APIs
3. Use an SQL style syntax to produce reports against Big Data sets
4. Use Machine Learning Algorithms with Big Data and SparkML
5. Connect Spark to Apache Kafka to process Streams of Big Data
6. See how Structured Streaming can be used to build pipelines with Kafka

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₹2,240

79% off

Discount 79% off

This course includes

- 21.5 hours on-demand video

- Full lifetime access

- Access on mobile and TV

- Certificate of Completion

Requirements

1. Java 8 is required for the course. Spark does not currently support Java9+, and you need Java 8 for the functional Lambda syntax
2. Previous knowledge of Java is assumed, but anything above the basics is explained
3. Some previous SQL will be useful for part of the course, but if you’ve never used it before this will be a good first experience

Description

Get started with the amazing Apache Spark parallel computing framework – this course is designed especially for Java Developers.
If you’re new to Data Science and want to find out about how massive datasets are processed in parallel, then the Java API for spark is a great way to get started, fast.
All of the fundamentals you need to understand the main operations you can perform in Spark Core, SparkSQL and DataFrames are covered in detail, with easy to follow examples. You’ll be able to follow along with all of the examples, and run them on your own local development computer.
Included with the course is a module covering SparkML, an exciting addition to Spark that allows you to apply Machine Learning models to your Big Data! No mathematical experience is necessary!
And finally, there’s a full 3 hour module covering Spark Streaming, where you will get hands-on experience of integrating Spark with Apache Kafka to handle real-time big data streams. We use both the DStream and the Structured Streaming APIs.

Optionally, if you have an AWS account, you’ll see how to deploy your work to a live EMR (Elastic Map Reduce) hardware cluster. If you’re not familiar with AWS you can skip this video, but it’s still worthwhile to watch rather than following along with the coding.
You’ll be going deep into the internals of Spark and you’ll find out how it optimizes your execution plans. We’ll be comparing the performance of RDDs vs SparkSQL, and you’ll learn about the major performance pitfalls which could save a lot of money for live projects.
Throughout the course, you’ll be getting some great practice with Java 8 Lambdas – a great way to learn functional-style Java if you’re new to it.
NOTE: Java 8 is required for the course. Spark does not currently support Java9+ (we will update when this changes) and Java 8 is required for the lambda syntax.