Streaming Big Data with Spark Streaming and Scala - Hands On
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
Course content
- 00:51Tip: Apply for a Twitter Developer Account now!
- 00:17Warning about Java 16
- Preview17:27
- Preview14:11
- Preview02:10
Requirements
- 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.
Description
New! Updated for Spark 3.0.0!
"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
Use structured streaming to stream into dataframes in real-time
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!
Who this course is for:
- 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.
Instructors
![Sundog Education by Frank Kane](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.5 Instructor Rating
- 104,775 Reviews
- 492,843 Students
- 22 Courses
Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.
Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.
- 4.5 Instructor Rating
- 100,751 Reviews
- 447,921 Students
- 14 Courses
Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.