This plan includes
- Limited free courses access
- Play & Pause Course Videos
- Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Email & Chat Support
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.
Course Overview
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.
Pre-requisites
- Understanding of Spark SQL and Python (or pyspark) will be beneficial
Target Audience
- 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
Curriculum 14 Lectures 02:43:11
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Section 1 : First Steps with Spark Structured Streaming
- Lecture 2 :
- Concepts of Spark Structured Streaming
- Lecture 3 :
- Structure of Spark Structured Streaming Application
- Lecture 4 :
- Writing the first Structured Streaming Application
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Section 2 : Resources
- Lecture 1 :
- Links to resources
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Section 3 : Deep dive into the structured streaming
- Lecture 1 :
- Understanding output modes
- Lecture 2 :
- Windows in stream processing
- Lecture 3 :
- Watermarking in stream processing
- Lecture 4 :
- Quick Introduction to Kafka
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Section 4 : Integrating Spark Structured Streaming with Kafka
- Lecture 1 :
- Watermarking with Kafka Sourc
- Lecture 2 :
- Use Case: Get materialized advertisements by joining Kafka topics
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Section 5 : Applying Structured Streaming in Production and Road to Expertise
- Lecture 1 :
- Structured Streaming in Production
- Lecture 2 :
- System Designs involving Spark Structured Streaming
- Lecture 3 :
- Road to Expertise
Our learners work at
Frequently Asked Questions
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