Spark Streaming Example with PySpark ❌ BEST Apache SPARK Structured STREAMING TUTORIAL with PySpark

In this video we'll understand Spark Streaming with PySpark through an applied example of how we might use Structured Streaming in a real world scenario.

Stream processing is the act of continuously incorporating new data to compute a result. In stream processing, the input data has no predetermined beginning or end as it simply forms a series of events that arrive as a stream (ex. credit card transactions).

Here we're focusing on Structured Streaming in Spark using Python, more specifically PySpark, and in the simplest terms, Structured Streaming is a dataFrame, but streaming.

The main idea behind Spark Structured Streaming is to treat a stream of data as a table, a dataset to which data is continuously appended. The job then periodically checks for new input data, processes it and updates the result.

You can access the Jupyter notebook here (login required):
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Видео Spark Streaming Example with PySpark ❌ BEST Apache SPARK Structured STREAMING TUTORIAL with PySpark канала DecisionForest