Spark Structured Streaming 3.0 : All You Need to Know
What you'll learn
- In Depth exploration of Spark Structured Streaming 3.0 using Python API. We'll also introduce you to Apache Kafka on a high level in the process.
- Understanding of Spark SQL and Python (or pyspark) will be beneficial
Getting faster action from the data is the need of many industries and Stream Processing helps doing just that. But it comes with its own set of theories, challenges and best practices.
Apache Spark has seen tremendous development being in stream processing. The rich features of Spark Structured Streaming introduces a learning curve and this course is aimed at bringing all those concepts in a friendly and easy to reflect manner.
You will learn the differences between batch & stream processing and the challenges specific to stream processing. Quickly we'll move to understand the concepts of stream processing with wide varieties of examples & hands-on, dealing with inner working and taking a use case towards the end. All of this activity will be on cloud using Spark 3.0.
Who this course is for:
- Data Engineers looking to expand their skill set, Data Scientists who wish want hands on working with stream processing and Technical Architects who want to evaluate the Spark Structured Streaming for their use cases
With over 8 years of industry experience in distributed computation, Amit is involved in a variety of big data engineering projects across different domains. He is a certified Hadoop Developer with skills in designing, developing, programming, administering and teaching.
Currently working as a Member of Technical Staff, he deals with data, its processing challenges and optimizations on a daily basis. He has been a mentor to hundreds of professionals and his videos have helped hundreds of thousands of learners.