In this chapter, we discussed various reference architectures for a Data-Intensive System. We also looked into the functional components that make the foundation of a Distributed System. We have also covered on why the Lambda architecture is so popular with Distributed systems as well as insight into Kappa architecture which is a simplified version of Lambda architecture. In the coming chapters, we will dissect this reference architecture layer by layer and gain deeper insights into each aspect of the architecture. So stay tuned!
Architecting Data-Intensive Applications
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Architecting Data-Intensive Applications
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Overview of this book
<p>Are you an architect or a developer who looks at your own applications gingerly while browsing through Facebook and applauding it silently for its data-intensive, yet ?uent and efficient, behaviour? This book is your gateway to build smart data-intensive systems by incorporating the core data-intensive architectural principles, patterns, and techniques directly into your application architecture.</p>
<p>This book starts by taking you through the primary design challenges involved with architecting data-intensive applications. You will learn how to implement data curation and data dissemination, depending on the volume of your data. You will then implement your application architecture one step at a time. You will get to grips with implementing the correct message delivery protocols and creating a data layer that doesn’t fail when running high traffic. This book will show you how you can divide your application into layers, each of which adheres to the single responsibility principle. By the end of this book, you will learn to streamline your thoughts and make the right choice in terms of technologies and architectural principles based on the problem at hand.</p>
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Exploring the Data Ecosystem
Defining a Reference Architecture for Data-Intensive Systems
Patterns of the Data Intensive Architecture
Discussing Data-Centric Architectures
Understanding Data Collection and Normalization Requirements and Techniques
Creating a Data Pipeline for Consistent Data Collection, Processing, and Dissemination
Building a Robust and Fault-Tolerant Data Collection System
Challenges of Data Processing
Let Us Process Data in Batches
Handling Streams of Data
Let Us Store the Data
When Data Dissemination is as Important as Data Itself
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Index
Customer Reviews