Taming Big Data with Spark Streaming and Scala – Hands On!
Learn to process massive streams of data in real time on a cluster with Apache Spark Streaming.
What you’ll learn
- Process massive streams of real-time data using Spark Streaming
Integrate Spark Streaming with data sources, including Kafka, Flume, and Kinesis
Use Spark 2’s Structured Streaming API
- Create Spark applications using the Scala programming language
- Output transformed real-time data to Cassandra or file systems
- Integrate Spark Streaming with Spark SQL to query streaming data in real time
- Train machine learning models with streaming data, and use those models for real-time predictions
- Ingest Apache access log data and transform streams of it
- Receive real-time streams of Twitter feeds
- Maintain stateful data across a continuous stream of input data
- Query streaming data across sliding windows of time
- To follow along with the examples, you’ll need a personal computer. The course is filmed using Windows 10, but the tools we install are available for Linux and MacOS as well.
- We’ll walk through installing the required software in the first lecture: The Scala IDE, Spark, and a JDK.
- My “Taming Big Data with Apache Spark – Hands On!” would be a helpful introduction to Spark in general, but it is not required for this course. A quick introduction to Spark is included.
- The course includes a crash course in the Scala programming language if you’re new to it; if you already know Scala, then great.
New! Updated for Spark 2.3.
“Big Data” analysis is a hot and highly valuable skill. Thing is, “big data” never stops flowing! Spark Streaming is a new and quickly developing technology for processing massive data sets as they are created – why wait for some nightly analysis to run when you can constantly update your analysis in real time, all the time? Whether it’s clickstream data from a big website, sensor data from a massive “Internet of Things” deployment, financial data, or something else – Spark Streaming is a powerful technology for transforming and analyzing that data right when it is created, all the time.
You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb.
This course gets your hands on to some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models! You’ll write and run real Spark Streaming jobs right at home on your own PC, and toward the end of the course, we’ll show you how to take those jobs to a real Hadoop cluster and run them in a production environment too.
Across over 30 lectures and almost 6 hours of video content, you’ll:
- Get a crash course in the Scala programming language
- Learn how Apache Spark operates on a cluster
- Set up discretized streams with Spark Streaming and transform them as data is received
- Analyze streaming data over sliding windows of time
- Maintain stateful information across streams of data
- Connect Spark Streaming with highly scalable sources of data, including Kafka, Flume, and Kinesis
- Dump streams of data in real-time to NoSQL databases such as Cassandra
- Run SQL queries on streamed data in real time
- Train machine learning models in real time with streaming data, and use them to make predictions that keep getting better over time
- Package, deploy, and run self-contained Spark Streaming code to a real Hadoop cluser using Amazon Elastic MapReduce.
This course is very hands-on, filled with achievable activities and exercises to reinforce your learning. By the end of this course, you’ll be confidently creating Spark Streaming scripts in Scala, and be prepared to tackle massive streams of data in a whole new way. You’ll be surprised at how easy Spark Streaming makes it!
- Students with some prior programming or scripting ability SHOULD take this course.
- If you’re working for a company with “big data” that is being generated continuously, or hope to work for one, this course is for you.
- Students with no prior software engineering or programming experience should seek an introductory programming course first.
Created by Sundog Education by Frank Kane, Frank Kane
Last updated 11/2018
Size: 2.56 GB