Apache Spark 3 - Real-time Stream Processing using Python

Self-Paced Learning

AWS Cloud Practitioner

What you'll learn

Dive into the dynamic realm of real-time stream processing. This course offers a comprehensive overview of Apache Spark 3, emphasizing its application in real-time data streaming. Explore key concepts, hands-on Python programming, and gain the skills to harness the power of Apache Spark for efficient real-time data analysis and processing.

Real-time Stream Processing Concepts - Acquire a solid understanding of real-time stream processing concepts, laying the groundwork for efficient data analysis.

Spark Structured Streaming APIs and Architecture - Explore the Spark Structured Streaming APIs and delve into the architecture, learning how to structure and manage streaming data efficiently.

Working with File Streams, Kafka Integration, and Stateful Transformations - Gain practical skills in handling file streams, integrating Spark with Kafka sources, and mastering both stateless and stateful streaming transformations.

Advanced Stream Processing Techniques - Explore advanced techniques such as windowing aggregates, watermarking, state cleanup, streaming joins, aggregation, and mitigating memory issues with streaming joins.

Course Prerequisite

What do you need to know before you start this course

Spark Fundamentals and exposure to Spark Dataframe APIs

Kafka Fundamentals and working knowledge of Apache Kafka.

Programming Knowledge Using Python Programming Language

A Recent 64-bit Windows/Mac/Linux Machine with 8 GB RAM

Course curriculum

Apache Spark 3 - Real-time Stream Processing

About this Course


32 lessons

0 hours of video content

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