10-16-2024, 09:08 AM
Data Denormalization In Modern System Design
![[Image: a80ff139de7b1b90a07f898b2c677ba2.jpeg]](https://i124.fastpic.org/big/2024/1015/a2/a80ff139de7b1b90a07f898b2c677ba2.jpeg)
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 626.80 MB | Duration: 0h 37m
The myth of normalization and how denormalized views powered by event driven or batch processing architecture can help
What you'll learn
What is a normalized dataset
Where normalization falls short and why is normalization a myth
The concept of denormalized views
How to implement denormalized views with event driven (streaming) workflows
The pitfalls of event driven (streaming) replication to your denormalized view
How to implement denormalized views with batch processing workflows
The pitfalls of batch processing replication to your denormalized view
Tradeoffs of denormalized views
When should you really consider denormalized views
Gain a new perspective in your existing microservice architecture
Requirements
High level understanding of backend system design
High level understanding of databases
High level understanding of microservice architecture and distributed systems
Description
We will dive deep into the cutting-edge world of modern database and software engineering, where traditional data normalization is increasingly giving way to more powerful techniques: denormalized views.Whether you're struggling with slow queries, high latency, or scaling data for millions of users, this series will teach you how to harness the true potential of denormalized views.We'll break down complex concepts in simple terms, showing you how modern views replicate and transform data for specific use cases, reduce the need for real-time processing, and improve overall performance. We'll cover how to implement such denormalization process, from using event-driven architecture to using batch processing instead and materialized views. We'll talk about denormalization techniques, and how to map and handle all of the many edge cases they will bring about.In this course, I want to challenge the way we think about databases and system design. In the past, normalization -structuring data into its smallest parts, and forbidding data duplication- was the golden rule for optimizing databases. But nowadays things have changed. When we're dealing with massive amounts of data, oftentimes normalization can actually slow us down.What if there was another approach? I'll show how effective denormalization of your data to what I call "denormalized views" can help provide an alternative solution to complete normalization. We'll demonstrate how replicating your data in a new denormalized format, designed for fast read access for a specific use case, can help you solve a new class of problem you weren't able to serve before because of performance reasons. Perhaps this is about a specific process in your organisation that is taking increasingly longer and more resources to be scaled and that needs to be rethought as soon as possible. Perhaps a new feature request that your team wasn't able to prioritize because there was no known solution to the problem at hand. Either way, this approach to system design is potentially one solution to your problems.Whether you're a backend engineer, system architect, or just someone curious about database performance, this series will arm you with the knowledge to design faster, more scalable, and more reliable systems. Learn how today's largest platforms - from Netflix to Facebook - use these principles to handle massive data volumes and power seamless user experiences.
Overview
Section 1: Normalization and its tradeoffs
Lecture 1 The Myth of Normalization
Lecture 2 What is Normalization? What are its tradeoffs?
Section 2: What are Denormalized Views?
Lecture 3 The Denormalized View Concept
Lecture 4 Caching Denormalized views
Section 3: Implementing Denormalized Views
Lecture 5 Implementing Denormalized Views Replication Workflows
Lecture 6 The complexities of Batch Processing
Lecture 7 The complexities of Event-Driven Workflows
Section 4: Tradeoffs and conclusion
Lecture 8 Trade-offs and Conclusion
Section 5: Examination
Junior to Senior Software engineers with some professional experience in backend software engineering,Senior+ software engineers looking to challenge their views on distributed systems
Screenshots
![[Image: 042a5c2242efa51cfd46f1cc01dbd3eb.jpeg]](https://i124.fastpic.org/big/2024/1015/eb/042a5c2242efa51cfd46f1cc01dbd3eb.jpeg)
Say "Thank You"
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Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 626.80 MB | Duration: 0h 37m
The myth of normalization and how denormalized views powered by event driven or batch processing architecture can help
What you'll learn
What is a normalized dataset
Where normalization falls short and why is normalization a myth
The concept of denormalized views
How to implement denormalized views with event driven (streaming) workflows
The pitfalls of event driven (streaming) replication to your denormalized view
How to implement denormalized views with batch processing workflows
The pitfalls of batch processing replication to your denormalized view
Tradeoffs of denormalized views
When should you really consider denormalized views
Gain a new perspective in your existing microservice architecture
Requirements
High level understanding of backend system design
High level understanding of databases
High level understanding of microservice architecture and distributed systems
Description
We will dive deep into the cutting-edge world of modern database and software engineering, where traditional data normalization is increasingly giving way to more powerful techniques: denormalized views.Whether you're struggling with slow queries, high latency, or scaling data for millions of users, this series will teach you how to harness the true potential of denormalized views.We'll break down complex concepts in simple terms, showing you how modern views replicate and transform data for specific use cases, reduce the need for real-time processing, and improve overall performance. We'll cover how to implement such denormalization process, from using event-driven architecture to using batch processing instead and materialized views. We'll talk about denormalization techniques, and how to map and handle all of the many edge cases they will bring about.In this course, I want to challenge the way we think about databases and system design. In the past, normalization -structuring data into its smallest parts, and forbidding data duplication- was the golden rule for optimizing databases. But nowadays things have changed. When we're dealing with massive amounts of data, oftentimes normalization can actually slow us down.What if there was another approach? I'll show how effective denormalization of your data to what I call "denormalized views" can help provide an alternative solution to complete normalization. We'll demonstrate how replicating your data in a new denormalized format, designed for fast read access for a specific use case, can help you solve a new class of problem you weren't able to serve before because of performance reasons. Perhaps this is about a specific process in your organisation that is taking increasingly longer and more resources to be scaled and that needs to be rethought as soon as possible. Perhaps a new feature request that your team wasn't able to prioritize because there was no known solution to the problem at hand. Either way, this approach to system design is potentially one solution to your problems.Whether you're a backend engineer, system architect, or just someone curious about database performance, this series will arm you with the knowledge to design faster, more scalable, and more reliable systems. Learn how today's largest platforms - from Netflix to Facebook - use these principles to handle massive data volumes and power seamless user experiences.
Overview
Section 1: Normalization and its tradeoffs
Lecture 1 The Myth of Normalization
Lecture 2 What is Normalization? What are its tradeoffs?
Section 2: What are Denormalized Views?
Lecture 3 The Denormalized View Concept
Lecture 4 Caching Denormalized views
Section 3: Implementing Denormalized Views
Lecture 5 Implementing Denormalized Views Replication Workflows
Lecture 6 The complexities of Batch Processing
Lecture 7 The complexities of Event-Driven Workflows
Section 4: Tradeoffs and conclusion
Lecture 8 Trade-offs and Conclusion
Section 5: Examination
Junior to Senior Software engineers with some professional experience in backend software engineering,Senior+ software engineers looking to challenge their views on distributed systems
Screenshots
Say "Thank You"
rapidgator.net:
ddownload.com:
