Apache Spark is the most widely used analytics engine for large-scale data processing. This course will teach you how to process real-time data streams and productionize real-time data applications.
Handling real-time data streams is crucial for modern applications, but many find it challenging to process and analyze data efficiently as it arrives. In this course, Real-time Stream Processing with PySpark, you'll gain the ability to build and deploy scalable, real-time data applications using Apache Spark and Python. First, you'll explore the fundamentals of the modern Spark Streaming and structured streaming concepts. Next, you'll discover advanced streaming techniques, such as window operations, stateful transformations, and fault tolerance, to enhance the reliability and performance of your applications. Finally, you'll learn how to integrate PySpark with various data sources and sinks, enabling seamless data ingestion and output to and from your streaming applications. When you're finished with this course, you'll have the skills and knowledge of stream processing with PySpark needed to develop robust, real-time data processing systems that can handle large-scale data streams efficiently.
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https://app.pluralsight.com/library/courses/pyspark-real-time-stream-processing/table-of-contents