Nothing static, all in motion.

You probably know by now: Spark is the most popular computing engine for big data, the most maintained, and with a proven track record of performance. It’s 100 times faster than the old MapReduce paradigm, and can easily be extended with machine learning and streaming capabilities, and much more.

In this Spark Streaming course, we’ll take the natural step forward: process big data as it arrives.

What’s in for you:

You’ll learn how Spark Structured Streaming and “normal” Spark batch operations are similar and different
You’ll work with new streaming abstractions (DStreams) for low-level, high-control processing
You’ll integrate Kafka, JDBC, Cassandra and Akka Streams (!) so that you can later integrate anything you like
You’ll work with powerful stateful APIs that only a few know how to properly use

And some extra perks: - You’ll have access to the entire code I write on camera (2200+ LOC) - You’ll be invited to our private Slack room where I’ll share latest updates, discounts, talks, conferences, and recruitment opportunities - (soon) You’ll have access to the takeaway slides - (soon) You’ll be able to download the videos for your offline view

Skills you’ll get:

Same comfort with Spark Structured Streaming APIs as with “normal” Spark batch:
- projections
- joins
- aggregations
- sums
- groups
High control over how data is processed with DStreams:
- map, flatMap, filter
- transform
- by-key operations
- process each RDD individually
Ability to work with time columns and window functions, both on structured and low-level streams
- sliding windows
- tumbling windows
- reduce by window
- reduce by window and key
Integration between Spark and other data sources, including
- Kafka (structured and low-level)
- JDBC
- NoSQL
- and something that’s not “natural” to Spark, like Akka
Ability to manually manage stateful data processing in ways SQL is incapable of
- mapGroupsWithState
- flatMapGroupsWithState

This course is for Scala and Spark programmers who need to process streaming data rather than one-time or batch. If you’ve never done Scala or Spark, this course is not for you.

Project 1: Twitter

In this project we will integrate live data from Twitter. We will create a custom data source that we use with Spark, and we will do various analyses: tweet lengths, most used hashtags in real time. You will be able to use this project as a blueprint for any data source that you might want to integrate. At the very end, we will use an NLP library from Stanford to do sentiment analysis on tweets and find the general state of social media.

You will learn:

How to set up your own data receiver, that you can manage yourself and “pull” new data
How to create a DStream from your custom code
How to pull data from Twitter
How to aggregate tweets
How to use Stanford’s coreNLP library for sentiment analysis
How to apply sentiment analysis on tweets in real time

Project 2: A Science Project

In this project we will write a full-stack web application which will support multiple users that are test subjects of a scientific test. We will investigate the effects of alcohol/substances/insert_your_addictive_drug_like_Scala on reflexes and response times. We will send the data through a web UI connected to a REST endpoint, then the data will flow through a Kafka broker and finally to a Spark Streaming backend which will do the data crunching. You can use this application as a blueprint for any full-stack application that aggregates and processes data with Spark Streaming in real time, from any number of concurrent users.

You will learn:

How to set up an HTTP server in minutes with Akka HTTP
How to manually send data through Kafka
How to aggregate data in a way that’s almost impossible in SQL
How to write a full-stack application with a web UI, Akka HTTP, Kafka and Spark Streaming

Daniel Ciocîrlan

I'm a software engineer and the founder of Rock the JVM.

I'm a software engineer and the founder of Rock the JVM. I started the Rock the JVM project out of love for Scala and the technologies it powers - they are all amazing tools and I want to share as much of my experience with them as I can.

As of February 2024, I've taught Java, Scala, Kotlin and related tech (e.g. Cats, ZIO, Spark) to 100000+ students at various levels and I've held live training sessions for some of the best companies in the industry, including Adobe and Apple. I've also taught university students who now work at Google and Facebook (among others), I've held Hour of Code for 7-year-olds and I've taught more than 35000 kids to code.

I have a Master's Degree in Computer Science and I wrote my Bachelor and Master theses on Quantum Computation. Before starting to learn programming, I won medals at international Physics competitions.