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Modern Data Architecture on AWS
Modern Data Architecture on AWS

Modern Data Architecture on AWS: A Practical Guide for Building Next-Gen Data Platforms on AWS

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Modern Data Architecture on AWS

Prologue

The Data and Analytics Journey So Far

“We are surrounded by data but starved for insights”

– Jay Baer

We have been surrounded by digital data for almost a century now and every decade has had its unique challenges regarding how to get the best value out of that data. But these challenges were narrow in scope and manageable since the data itself was manageable. Even though data was rapidly growing in the 20th century, its volume, velocity, and variety were still limited in nature. And then we hit the 21st century and the world of data drastically changed. Data started to exponentially grow due to multiple reasons:

  • The adoption of the internet picked up speed and data grew into big data
  • Smartphone devices became a common household entity and these devices all generated tons of data
  • Social media took off and added to the deluge of information
  • Robotics, smart edge devices, industrial devices, drones, gaming, VR, and other artificial...

Introduction to the data and analytics journey

The online transaction processing (OLTP) and online analytical processing (OLAP) systems worked great by themselves for a very long time when data producers were limited, the volume of data was under control, and data was mostly structured in tabular format. The last 20 years have seen a seismic shift in the way new businesses and technologies have come up.

As the volume, velocity, and variety of data started to pick steam, data grew into big data and the data processing techniques needed a major rehaul. This gave rise to the Apache Hadoop framework, which changed the way big data was processed and stored. With more data, businesses wanted to get more descriptive and diagnostic analytics out of their data. At the same time, another technology was gaining rapid traction, which gave organizations hope that they could look ahead to the future and predict what may happen in advance so that they could take immediate actions to steer their...

Traditional data platforms

Before we get into architecting data platforms in a modern way, it is important to understand the traditional data platforms and know their strengths and limitations. Once we understand the challenges of traditional data platforms in solving new business use cases, we can design a modern data platform in a holistic matter.

Three-tier architecture

Throughout the 1980s and 1990s, the three-tier architecture became a popular way of producing, processing, and storing data. Almost every organization used this pattern as it met the business needs with ease. The three tiers of this architecture were the presentation tier, the application tier, and the data tier:

  • The presentation tier was the front-facing module and was created either as a thick client – that is, software was installed on the client’s local machine – or as a thin client – that is, a browser-based application.
  • The application tier would receive the data...

Challenges with on-premises data systems

As data grew exponentially, so did the on-premises systems. However, visible cracks started to appear in the legacy way of architecting data and analytics use cases.

The hardware that was used to process, store, and consume data had to be procured up-front, and then installed and configured before it was ready for use. So, there was operational overhead and risks associated with procuring the hardware, provisioning it, installing software, and maintaining the system all the time. Also, to accommodate for future data growth, people had to estimate additional capacity way in advance. The concept of hardware elasticity didn’t exist. The lack of elasticity in hardware meant that there were scalability risks associated with the systems in place, and these risks would surface whenever there was a sudden growth in the volume of data or when there was a market expansion for the business.

Buying all this extra hardware up-front also meant...

What this book is all about

Before we wrap up this prologue and dive into more details in subsequent chapters, I want to lay the foundation for what you should expect from this book and how the content is laid out.

When you think of a data platform in an organization, it contains a lot of systems that work in tandem to make the platform operational. A data platform contains different types of purpose-built data stores, different types of ETL tools and pipelines for data movement between the data stores, different types of systems that allow end users to consume the data, and different types of security and governance mechanisms in place to keep the platform protected and safe.

To allow the data platform to cater to different types of use cases, it needs to be designed and architected in the best possible manner. With exponential data growth and the need to solve new business use cases, these architectural patterns need to constantly evolve, not just for current needs but also...

Summary

With that, this short introduction has come to an end. The idea here was to provide a quick history of how data and analytics evolved. We went through the different types of data warehouse designs, along with their pros and cons. We also looked at how the recent exponential growth of data has made it difficult to use the same type of system architecture for all types of use cases.

This gives us a perfect launching pad to understand what modern data architecture is and how it can be architected using different AWS data and analytics services.

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Key benefits

  • Learn to build modern data platforms on AWS using data lakes and purpose-built data services
  • Uncover methods of applying security and governance across your data platform built on AWS
  • Find out how to operationalize and optimize your data platform on AWS
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Many IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.

Who is this book for?

This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.

What you will learn

  • Familiarize yourself with the building blocks of modern data architecture on AWS
  • Discover how to create an end-to-end data platform on AWS
  • Design data architectures for your own use cases using AWS services
  • Ingest data from disparate sources into target data stores on AWS
  • Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services
  • Find out how to implement data governance using AWS services
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Publication date : Aug 31, 2023
Length: 420 pages
Edition : 1st
Language : English
ISBN-13 : 9781801813396
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Table of Contents

23 Chapters
Part 1: Foundational Data Lake Chevron down icon Chevron up icon
Prologue: The Data and Analytics Journey So Far Chevron down icon Chevron up icon
Chapter 1: Modern Data Architecture on AWS Chevron down icon Chevron up icon
Chapter 2: Scalable Data Lakes Chevron down icon Chevron up icon
Part 2: Purpose-Built Services And Unified Data Access Chevron down icon Chevron up icon
Chapter 3: Batch Data Ingestion Chevron down icon Chevron up icon
Chapter 4: Streaming Data Ingestion Chevron down icon Chevron up icon
Chapter 5: Data Processing Chevron down icon Chevron up icon
Chapter 6: Interactive Analytics Chevron down icon Chevron up icon
Chapter 7: Data Warehousing Chevron down icon Chevron up icon
Chapter 8: Data Sharing Chevron down icon Chevron up icon
Chapter 9: Data Federation Chevron down icon Chevron up icon
Chapter 10: Predictive Analytics Chevron down icon Chevron up icon
Chapter 11: Generative AI Chevron down icon Chevron up icon
Chapter 12: Operational Analytics Chevron down icon Chevron up icon
Chapter 13: Business Intelligence Chevron down icon Chevron up icon
Part 3: Govern, Scale, Optimize And Operationalize Chevron down icon Chevron up icon
Chapter 14: Data Governance Chevron down icon Chevron up icon
Chapter 15: Data Mesh Chevron down icon Chevron up icon
Chapter 16: Performant and Cost-Effective Data Platform Chevron down icon Chevron up icon
Chapter 17: Automate, Operationalize, and Monetize Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
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JustAnotherCustomer Sep 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book. Very well written and explained. Covers all aspects of building a holistic data platform. Easy to relate using the real-world use-cases and the step by step build up of chapters adds a great flow to the book. Kudos to the author.
Amazon Verified review Amazon
srikanth Oct 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book covers end to end Aws data services and when to use what, how to derive outcomes with Data mesh architectural patterns, analytic services and what teams should focus on.
Amazon Verified review Amazon
Sangita Mahala Oct 05, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Modern Data Architecture on AWS is a well-written and informative book that is essential reading for anyone who is involved in building or managing data platforms on AWS. It is also a valuable resource for technical leaders and decision makers who want to learn more about modern data architecture. If you are looking for a comprehensive and practical guide to building next-generation data platforms on AWS, then I highly recommend Modern Data Architecture on AWS.
Amazon Verified review Amazon
Manpinder Sep 07, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This helped a lot while implementing Modern Data Strategy
Amazon Verified review Amazon
Erik Swensson Oct 04, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book! A thorough exploration of modern data architectures on AWS. I really enjoyed how it went through history of data architectures and how we got to where we are today. The technology deep dives are for sure the most educating and can't miss part of the book, but exploration into critical considerations such as price/performance and governance are also enlightening.
Amazon Verified review Amazon
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