Stream processing frameworks for big data- the internals

Stream processing frameworks for big data: the internals, available at $54.99, has an average rating of 4.07, with 42 lectures, 7 quizzes, based on 7 reviews, and has 62 subscribers.

You will learn about The features and internals of Flink, Spark Streaming, Structured Streaming and Kafka Streams. How to select the right stream processing framework for a use case. The current state-of-the-art of distributed stream processing. References to equivalent implementations in all frameworks. This is not a programming course! This is a course on understanding how these systems work. This course is ideal for individuals who are Anybody who needs to get a feeling on how to select the right framework for a use case. or Anybody who wants to build up firm, in-depth knowledge on the differences and characteristics of these frameworks. or Anybody who wants to build up a deep understanding of stream processing in general. It is particularly useful for Anybody who needs to get a feeling on how to select the right framework for a use case. or Anybody who wants to build up firm, in-depth knowledge on the differences and characteristics of these frameworks. or Anybody who wants to build up a deep understanding of stream processing in general.

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Summary

Title: Stream processing frameworks for big data: the internals

Price: $54.99

Average Rating: 4.07

Number of Lectures: 42

Number of Quizzes: 7

Number of Published Lectures: 42

Number of Published Quizzes: 7

Number of Curriculum Items: 49

Number of Published Curriculum Objects: 49

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

The features and internals of Flink, Spark Streaming, Structured Streaming and Kafka Streams.

How to select the right stream processing framework for a use case.

The current state-of-the-art of distributed stream processing.

References to equivalent implementations in all frameworks.

This is not a programming course! This is a course on understanding how these systems work.

Who Should Attend

Anybody who needs to get a feeling on how to select the right framework for a use case.

Anybody who wants to build up firm, in-depth knowledge on the differences and characteristics of these frameworks.

Anybody who wants to build up a deep understanding of stream processing in general.

Target Audiences

Anybody who needs to get a feeling on how to select the right framework for a use case.

Anybody who wants to build up firm, in-depth knowledge on the differences and characteristics of these frameworks.

Anybody who wants to build up a deep understanding of stream processing in general.

Do you need to use stream processing for your next project but have no idea where to begin? Or do you want to grow into a data engineering role and want to start building up knowledge on stream processing?

In this course, we give a detailed explanation and comparison of several popular stream processing frameworks. At the finish line, you will be able to make a well-grounded selection of the right framework for  your use case or to start your learning process. We will cover Flink, Kafka Streams, Spark Streaming and Structured Streaming. These are the four frameworks that are currently the state-of-the-art in the industry.

You will understand their features, characteristics and differences. This course gives you the perfect primer to start learning and better understand the APIs and programming languages behind these frameworks.

This course covers all relevant aspects:

– their general characteristics

– APIs

– latency and throughput performance

– scalability

– elasticity

– fault tolerance

– state management

– deployment

–

We will dive deeply into the workings and the advantages and disadvantages of the different mechanisms and approaches.

!!!This course is not a programming course but focuses on more theoretical aspects.

At the end, you will be provided with a concise overview on what was covered.

The content of this course is based on the results of Giselle’s PhD work in which she benchmarked and analyzed these frameworks on all these characteristics. 

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course overview

Chapter 2: General characteristics

Lecture 1: Overview

Lecture 2: Stream processing and distributed processing

Lecture 3: Frameworks: Flink

Lecture 4: Frameworks: Kafka Streams

Lecture 5: Frameworks: Spark Streaming and Structured Streaming

Lecture 6: Ecosystem: Connectors

Lecture 7: Ecosystem: Batch Processing

Lecture 8: Ecosystem: ML Libraries and Other Libraries

Lecture 9: Maturity

Lecture 10: Streaming models

Chapter 3: APIs

Lecture 1: Programming languages

Lecture 2: API levels

Lecture 3: Operators

Lecture 4: Operators: Sliding and Tumbling Windows

Lecture 5: Operators: Session and Count Windows

Lecture 6: Operators: Joining

Lecture 7: Operators: Low-level Operators

Lecture 8: Configuration

Chapter 4: Time

Lecture 1: Time characteristics l

Lecture 2: Time characteristics II

Lecture 3: Out-of-order processing

Lecture 4: Triggers

Chapter 5: Performance: Latency and throughput

Lecture 1: Latency: Definition and influence of streaming model

Lecture 2: Latency: influence of operation

Lecture 3: Latency: predictability

Lecture 4: Throughput

Lecture 5: General advice

Chapter 6: Scalability, elasticity and parallelization

Lecture 1: Scalability

Lecture 2: Elasticity

Lecture 3: Parallelization

Chapter 7: State management

Lecture 1: State

Lecture 2: State backends

Lecture 3: State features

Chapter 8: Fault tolerance

Lecture 1: Message delivery guarantees

Lecture 2: Checkpointing

Lecture 3: Checkpointing: savepoints

Lecture 4: Write-ahead-logs

Lecture 5: Fault tolerance in Kafka Streams

Lecture 6: Master and worker failures

Chapter 9: Summary

Lecture 1: Summary

Instructors

Giselle van Dongen
Instructor in distributed stream processing

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2 stars: 1 votes

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4 stars: 4 votes

5 stars: 2 votes

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