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Spark Structured Streaming 3.0 : All You Need to Know
Rating: 4.4 out of 5(27 ratings)
1,165 students

Spark Structured Streaming 3.0 : All You Need to Know

Get to hands on from the first hour and travel through the concepts and details to emerge out master at the end
Created byAmit Ranjan
Last updated 8/2020
English
English [Auto],

What you'll learn

  • In Depth exploration of Spark Structured Streaming 3.0 using Python API.
  • Get introduced to Apache Kafka on a high level in the process.
  • Understand the nuances of Stream Processing in Apache Spark
  • Discover various features Spark provides out of the box for Stream Processing

Course content

5 sections • 14 lectures • 2h 43m total length

  • Need and Challenges of Stream Processing18:03

    Explore the need for streaming over batch, real-time insights, and the challenges like low latency, fault tolerance, event time handling, windowing, and recovery in Spark structured streaming 3.0.

  • Concepts of Spark Structured Streaming5:21
  • Structure of Spark Structured Streaming Application11:12

    Learn to build a Spark structured streaming application with input from sources like Kafka or socket, transform with dataframe operations, and output to console or sinks using micro-batch triggers.

  • Writing the first Structured Streaming Application12:39

    Write your first structured streaming application with Spark, reading from a net server, performing a word-count on streaming data, and monitoring via Spark UI and streaming metrics.

  • Basics of Spark Structured Streaming

Requirements

  • Understanding of Spark SQL and Python (or pyspark) will be helpful

Description

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. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. It allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis.

This illustrative course will build your foundational knowledge. You will learn the differences between batch & stream processing, programming model, the APIs 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 want hands on working with stream processing and Technical Architects who want to evaluate the Spark Structured Streaming for their use cases

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