Take your first steps towards discovering, learning, and using Apache Spark 3.0. We will be taking a live coding approach in this carefully structured course and explaining all the core concepts needed along the way.
In this course, we will understand the real-time stream processing concepts, Spark structured streaming APIs, and architecture.
We will work with file streams, Kafka source, and integrating Spark with Kafka. Next, we will learn about state-less and state-full streaming transformations. Then cover windowing aggregates using Spark stream. Next, we will cover watermarking and state cleanup. After that, we will cover streaming joins and aggregation, handling memory problems with streaming joins. Finally, learn to create arbitrary streaming sinks.
By the end of this course, you will be able to create real-time stream processing applications using Apache Spark.
All the resources for the course are available at https://github.com/PacktPublishing/Real-time-stream-processing-using-Ap…
Explore state-less and state-full streaming transformations
Windowing aggregates using Spark stream
Learn Watermarking and state cleanup
Implement streaming joins and aggregations
Handling memory problems with streaming joins
Learn to create arbitrary streaming sinks
For this course, you need to know Spark fundamentals and should be exposed to Spark Dataframe APIs. Also, you should know Kafka fundamentals and have a working knowledge of Apache Kafka. One should also have programming knowledge of Python programming.