Configuring Spark Structured Streaming for real-time data processing

In this recipe, you will learn how to configure Apache Spark Structured Streaming using Python for real-time data processing. Spark Structured Streaming is used in a variety of scenarios in which you need to ingest and analyze data as they arrive in real time from sources such as IoT devices, social media streams, sensors, or financial transactions. Structured Streaming provides the means to handle these continuous data streams. This configuration is particularly relevant when low-latency processing is crucial for making timely decisions or taking immediate actions based on incoming data. Structured Streaming also becomes essential when dealing with event time-based processing, enabling you to perform time-based aggregations and windowing operations on data with timestamps.

Getting ready

To run this recipe, we first need to set up incoming streaming data. We will feed data by opening a terminal window in the...