Book Image

Architecting Data-Intensive Applications

By : Anuj Kumar
Book Image

Architecting Data-Intensive Applications

By: Anuj Kumar

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
Index

The 3 + 1 Vs and how they affect choice in data processing design


The biggest challenge in data processing is to actually understand the three Vs of the data intensive system and their effect on the overall approach to the design of the data processing pipeline.

The three Vs stand for velocity of the data, volume of the data, and variety of the data. The outcome from the data processing system is generally the fourth V of the equation, that is, the value of the data.

Some experts add more Vs into this equation. For example, data veracity (depicting abnormalities in the data), data validity (the data represents what it is intended to represent), and data volatility (expressing in loose terms the importance of data over a period of time). While they all are important, the author believes that these are more or less covered with the basic three Vs of data.

Since we are talking about data intensive systems, it's safe to assume that the volume of the data will be huge. Thus the data processing system...