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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 intelligence-driven gadgets took the growth of data to a whole new level.

However, across all this, the common theme that exists even today is that data gets produced, processed, stored, and consumed.

Now, even though the history of data and analytics goes back many decades, I don’t want to dig everything up. Since this book revolves around cloud computing technologies, it is important to understand how we got here, what systems were in place in the on-premises data center world, and why those same systems and the architectural patterns surrounding them struggle to cater to the business and technology needs of today.

In this prologue, we will cover the following main topics:

  • Introduction to the data and analytics journey
  • Traditional data platforms
  • Challenges with on-premises data systems
  • What this book is all about

If you are already well versed with the traditional data platforms and their challenges, you can skip this introduction and directly jump to Chapter 1.

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 businesses in the right direction. This was made possible by the rise of artificial intelligence and machine learning and soon, large organizations started investing in predictive analytics projects.

And while we were thinking that we got the big data under control with new frameworks, the data floodgates opened up. The last 10 to 15 years have been revolutionary with the onset of smart devices, including smartphones. Connectivity among all these devices and systems made data grow exponentially. This was termed the Internet of Things (IoT). And to add to the complexity, these devices started to share data in near real time, which meant that data had to be streamed immediately for consumption. The following figure highlights many of the sources from where data gets generated. A lot of insights can be derived from all this data so that organizations can make faster and better decisions:

Figure 00.1 – Big data sources

Figure 00.1 – Big data sources

This also meant that organizations started to carefully segregate their technical workforce to deal with data in personas. The people processing big data came to be known as data engineers, the people dealing with data for future predictions were the data scientists, and the people analyzing the data with various tools were the data analysts. Each type of persona had a well-defined task and there was a strong desire to create/purchase the best technological tool out there to make their day-to-day lives easier.

From a data and analytics point of view, systems started to grow bigger with extra hardware. Organizations started to expand their on-premises data centers with the latest and greatest servers out there to process all this data as fast as possible, to create value for their businesses. However, a lot of architecture patterns for data and analytics remained the same, which meant that many of the old use cases were still getting solved. However, with new demands from these businesses, pain areas started popping up more frequently.

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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
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Edition : 1st
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Publication date : Aug 31, 2023
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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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Trent Oct 28, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book was an easy read and contained lots of useful information. I definitely feel more confident with data concepts on AWS. It was great to hear the author's experience and expertise.
Amazon Verified review Amazon
Daniel Barresi Oct 31, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book does a great job of providing the reader an understanding of running a modern data environment on AWS from data ingestion to customer analytics. The best part is that each section includes business use cases that make not only the builder understand the concepts but also the analyst and lines of business.
Amazon Verified review Amazon
Tanya Silva Oct 02, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Has your company tried to create a centralized big data processing platform in the past couple of decades ( plus or minus X years, depending on when it was founded)? Have you run into an issue of "many visible cracks have started to appear in the home-grown big data platform - scalability issues, reliability issues, multiple outages, constant upgrades and maintenance, performance issues, and,of course, growing costs"? Did it lead you to lose focus on business outcomes? "Modern Data Architecture on AWS" - A practical guide for building Next-Gen Data Platform on AWS by Mr.Behram Irani covers all the topics a modern Data Engineer( Architect, Product Manager, etc) needs to know in order to build Best In Class Data Applications. The book covers Modern Data architecture, Scalable Data Lakes, Batch Data Ingestion, Streaming Data Ingestion, Data Processing, Interactive Analytics, Data Warehousing, Data Sharing, Data Federation, Predictive Analytics, Generative AI, Operational Analytics, Business Intelligence, Data Governance, Data Mesh, Cost Optimization, and Automation/Monetization topics. While the book does not have hands-on tutorials ( you can find those in the links provided at the end of the chapters), it is an excellent resource for those who are new or have been with AWS but need to understand the AWS product roadmap better. I seem to have training on AWS stack about every two or three years, and each time, there is a new offering worth exploring. Even if you have your AWS Architect certification from 5-7 years ago, I strongly recommend looking at this book again to keep your skills up to date.
Amazon Verified review Amazon
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
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