Building High-Throughput Data Microservices

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Gregory Green

1:58:10

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01 - Introduction

01 - Improve your microservices.mp4

00:50

02 - What you should know.mp4

00:40

02 - 1. Introduction to High Throughput Data Microservices

01 - Microservices overview.mp4

04:19

02 - What are cloud-native data services.mp4

04:09

03 - Overview of throughput.mp4

05:09

04 - Why is high throughput with microservices important.mp4

02:10

05 - Throughput architectural data patterns.mp4

03:43

06 - High throughput scalability.mp4

03:41

03 - 2. High Throughput Architecture

01 - High throughput data architectures using Spring.mp4

04:04

02 - Database throughput with Postgres or MySQL.mp4

07:10

03 - Batch architecture throughput.mp4

03:12

04 - Event streaming throughput.mp4

02:21

05 - RabbitMQ streams for high throughput.mp4

03:10

06 - Apache Kafka for high throughput.mp4

02:28

04 - 3. Spring Throughput Overview

01 - Microservices with Spring Boot.mp4

04:24

02 - Batching throughput with Spring Batch.mp4

04:29

03 - Event streaming throughput with Spring Cloud Stream.mp4

03:41

04 - API throughput with Spring Web.mp4

05:15

05 - 4. Batching Throughput Implementation

01 - Spring Batch with Postgres throughput tuning.mp4

06:25

02 - Spring Batch with MySQL throughput tuning.mp4

05:44

03 - Spring Batch with Apache Kafka throughput tuning.mp4

04:38

04 - Spring Batch with RabbitMQ streams throughput tuning.mp4

04:32

06 - 5. Event Streaming and API Throughput

01 - Event streaming and API throughput architecture with Spring Boot.mp4

07:19

02 - Spring Cloud Stream Consumer with RabbitMQ streams and Postgres.mp4

05:24

03 - API with Spring Boot RabbitMQ streams publisher.mp4

05:34

04 - Spring Web Event streaming performance and observability.mp4

12:51

07 - Conclusion

01 - Continue your journey.mp4

00:48

Description

Microservices come with all kinds of great benefits. But they can be extremely difficult to manage well at scale. As organizations face the challenge of processing increasingly large volumes of data, they need to be able to build and maintain high-throughput data microservices. In this course, instructor Gregory Green covers the fundamental skills you need to know to meet today’s growing data demands with high-performance, scalable, high-throughput architectures.

Discover best practices for data patterns for throughput with flexible data services and multisite cloud-based use cases. Explore some of the most critical factors that affect high-throughput requirements, before diving deeper into antipatterns and the pros and cons of data technologies. Along the way, Gregory provides examples with RabbitMQ, Postgres, MySQL, Spring, and Apache Kafka, offering tips and pointers with hands-on demonstrations of how to design and implement successful, high-throughput data microservices.

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Python

Spring Boot

Data Science

MySQL

Microservices

Apache Kafka

Gregory Green

Instructor's Courses

Senior software architect, developer, and consultant with over 25 years of diverse experience in various industries such as financial, pharmaceutical, telecommunication, and others. Specializing in Java and .NET C# based data services solutions. Extensive expertise in data management frameworks, application performance tuning, enterprise integration, application architecture, and development design patterns. EXPERTISE • Apache Geode/GemFire • RabbitMQ • Cloud-Native Data • Cloud-Native Applications • Agile Software Development • System Analysis/Architecture • Aspect-Oriented Programming (AOP) • Requirements Analysis • Web Development • Enterprise Integration • Software Design Patterns • Database Management/Migration

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.