Stream processing is another big and popular topic for Apache Spark. It involves the processing of data in Spark as streams and covers topics such as input and output operations, transformations, persistence, and checkpointing, among others.
Apache Spark Streaming will cover the area of processing, and we will also see practical examples of different types of stream processing. This discusses batch and window stream configuration and provides a practical example of checkpointing. It also covers different examples of stream processing, including Kafka and Flume.
There are many ways in which stream data can be used. Other Spark module functionality (for example, SQL, MLlib, and GraphX) can be used to process the stream. You can use Spark Streaming with systems such as MQTT or ZeroMQ. You can even create custom receivers for your own user-defined data sources.
