Learning Structured Streaming in Azure Databricks

Spark Structured Streaming provides a scalable and fault-tolerant approach to processing data in real-time. In Structured Streaming, Spark processes data in micro-batches to achieve low latencies. Syntactically, it looks very similar to batch processing. The same Spark DataFrame Transformations are used for streaming aggregations, joining static and streaming data, and more. Structured Streaming also guarantees exactly-once stream processing, which ensures that there is no duplication of data. Let's look at a quick example:

1. Create a new Databricks notebook, start your Spark cluster, and run the following command:

   %fs ls dbfs:/databricks-datasets/structured-streaming/events/

   This displays a list of 50 JSON files that we will be reading using Structured Streaming.

   Figure 2.24 – Data to be read using Structured Streaming

2. Run the following code block. It imports the necessary functions, defines the schema for the DataFrame...