Data Stream Development via Spark, Kafka and Spring Boot

Data Stream Development via Spark, Kafka and Spring Boot, available at $44.99, has an average rating of 3.85, with 27 lectures, based on 134 reviews, and has 1064 subscribers.

You will learn about Attain a solid foundation in the most powerful and versatile technologies involved in data streaming: Apache Spark and Apache Kafka Form a robust and clean architecture for a data streaming pipeline Implement the correct tools to bring your data streaming architecture to life Isolate the most problematic tradeoff for each tier involved in a data streaming pipeline Query, analyze, and apply machine learning algorithms to collected data Display analyzed pipeline data via Google Maps on your web browser Discover and resolve difficulties in scaling and securing data streaming applications This course is ideal for individuals who are This course is perfect for Java developers and architects who want to design and write data streaming pipelines. It is particularly useful for This course is perfect for Java developers and architects who want to design and write data streaming pipelines.

Enroll now: Data Stream Development via Spark, Kafka and Spring Boot

Summary

Title: Data Stream Development via Spark, Kafka and Spring Boot

Price: $44.99

Average Rating: 3.85

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Attain a solid foundation in the most powerful and versatile technologies involved in data streaming: Apache Spark and Apache Kafka
Form a robust and clean architecture for a data streaming pipeline
Implement the correct tools to bring your data streaming architecture to life
Isolate the most problematic tradeoff for each tier involved in a data streaming pipeline
Query, analyze, and apply machine learning algorithms to collected data
Display analyzed pipeline data via Google Maps on your web browser
Discover and resolve difficulties in scaling and securing data streaming applications

Who Should Attend

This course is perfect for Java developers and architects who want to design and write data streaming pipelines.

Target Audiences

This course is perfect for Java developers and architects who want to design and write data streaming pipelines.

Today, organizations have a difficult time working with huge numbers of datasets. In addition, data processing and analyzing need to be done in real time to gain insights. This is where data streaming comes in. As big data is no longer a niche topic, having the skillset to architect and develop robust data streaming pipelines is a must for all developers. In addition, they also need to think of the entire pipeline, including the trade-offs for every tier.

This course starts by explaining the blueprint architecture for developing a completely functional data streaming pipeline and installing the technologies used. With the help of live coding sessions, you will get hands-on with architecting every tier of the pipeline. You will also handle specific issues encountered working with streaming data. You will input a live data stream of Meetup RSVPs that will be analyzed and displayed via Google Maps.

By the end of the course, you will have built an efficient data streaming pipeline and will be able to analyze its various tiers, ensuring a continuous flow of data.

About the Author

Anghel Leonard is currently a Java chief architect. He is a member of the Java EE Guardians with 20+ years’ experience. He has spent most of his career architecting distributed systems. He is also the author of several books, a speaker, and a big fan of working with data.

Course Curriculum

Chapter 1: Introducing Data Streaming Architecture

Lecture 1: The Course Overview

Lecture 2: Discovering the Data Streaming Pipeline Blueprint Architecture

Lecture 3: Analyzing Meetup RSVPs in Real-Time

Chapter 2: Deployment of Collection and Message Queuing Tiers

Lecture 1: Running the Collection Tier (Part I – Collecting Data)

Lecture 2: Collecting Data Via the Stream Pattern and Spring WebSocketClient API

Lecture 3: Explaining the Message Queuing Tier Role

Lecture 4: Introducing Our Message Queuing Tier –Apache Kafka

Lecture 5: Running The Collection Tier (Part II – Sending Data)

Chapter 3: Proceeding to the Data Access Tier

Lecture 1: Dissecting the Data Access Tier

Lecture 2: Introducing Our Data Access Tier – MongoDB

Lecture 3: Exploring Spring Reactive

Lecture 4: Exposing the Data Access Tier in Browser

Chapter 4: Implementing the Analysis Tier

Lecture 1: Diving into the Analysis Tier

Lecture 2: Streaming Algorithms For Data Analysis

Lecture 3: Introducing Our Analysis Tier – Apache Spark

Lecture 4: Plug-in Spark Analysis Tier to Our Pipeline

Lecture 5: Brief Overview of Spark RDDs

Lecture 6: Spark Streaming

Lecture 7: DataFrames, Datasets and Spark SQL

Lecture 8: Spark Structured Streaming

Lecture 9: Machine Learning in 7 Steps

Lecture 10: MLlib (Spark ML)

Lecture 11: Spark ML and Structured Streaming

Lecture 12: Spark GraphX

Chapter 5: Mitigate Data Loss between Collection, Analysis and Message Queuing Tiers

Lecture 1: Fault Tolerance (HML)

Lecture 2: Kafka Connect

Lecture 3: Securing Communication between Tiers

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 6 votes
2 stars: 5 votes
3 stars: 18 votes
4 stars: 49 votes
5 stars: 56 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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