Data Stream Development with Apache Spark, Kafka, and Spring Boot

$125 | Duration: 7h 51m | Video: h264, 1920×1080 | Audio: AAC, 48kHz, 2 Ch | 1.89 GB
Genre: eLearning | Language: English | November 30, 2018

Handle high volumes of data at high speed. Architect and implement an end-to-end data streaming pipeline

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.

All the code and supporting files for this course are available at https://github.com/PacktPublishing/-Data-Stream-Development-with-Apache-Spark-Kafka-and-Spring-Boot

Style and Approach
This course is a combination of text, a lot of images (diagrams), and meaningful live coding sessions. Each topic covered follows a three-step structure: first, we have some headlines (facts); second, we continue with images (diagrams) meant to provide more details; and finally we convert the text and images into code written in the proper technology.

Table of Contents
INTRODUCING DATA STREAMING ARCHITECTURE
DEPLOYMENT OF COLLECTION AND MESSAGE QUEUING TIERS
PROCEEDING TO THE DATA ACCESS TIER
IMPLEMENTING THE ANALYSIS TIER
MITIGATE DATA LOSS BETWEEN COLLECTION, ANALYSIS AND MESSAGE QUEUING TIERS

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

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