Achieving Low-Latency Data with Edge Computing

1h 13mIntermediate2022-09-23

Authors

Gregory Green

Advisory Solution Engineer at VMware

Course details

Edge computing processes data at or near its actual physical source, which allows you to make data available faster than ever—without poor user experiences, bottlenecks, or system degradation. In this course, instructor Gregory Green walks you through how to achieve low latency by building a scaling architecture for edge computing data integration and management.

Discover best practices for reliable messaging and patterns for throughput as well as low latency with flexible data pipelines and multisite cloud-based use cases. Explore some of the most important factors that affect edge computing latency, diving deeper into antipatterns and the pros and cons of database management technologies, with examples drawn from RabbitMQ, Apache Geode, MQTT, and Spring. Along the way, Gregory gives you tips and pointers with hands-on demonstrations of how to successfully design and implement edge computing architecture for low-latency data access in real time.

Skills covered

RabbitMQSpringApacheCloud AdministrationCloud ComputingOpen SourceDeep Dive (X:Y)

Concepts

0. Introduction

01 - Introduction
02 - Prerequisites
03 - Exercise files

1. Edge Computing Latency Overview

04 - What is edge computing
05 - Overview of data latency
06 - Why is low-latency data with edge computing important

2. Edge Computing Low-Latency Architecture

07 - Reference architecture
08 - Messaging systems
09 - Push vs. pull
10 - In-memory access
11 - Scalability

3. Implementation

12 - Implementation architecture
13 - MQTT
14 - RabbitMQ
15 - Apache Geode
16 - Spring

4. Demonstrations

17 - Demo example implementation overview
18 - Producing at the edge using RabbitMQ
19 - Consuming edge data using RabbitMQ
20 - Low-latency access using Apache Geode

Conclusion

21 - Wrap up