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

Distributed storage


Distributed storage simply means dividing your data into logical chunks and storing them, physically, on different machines. Software logic, written on top of this distributed storage, is responsible for distributing the data as well as querying the data from different physical machines. Capabilities such as aggregation, single-point-of-interaction, and filtering are provided by the software responsible for distributing the data across different machines.

There are mainly two main concepts to understand about distributed storage:

  • Data partitioning
  • Data replication

Let's briefly look at them now. We will talk about them in more detail in the coming chapters.

Data partitioning is the process of dividing the dataset into logical chunks, usually by using some deterministic algorithm and distributing the data over multiple servers, or shards. Each shard is an independent data store, and collectively, the shards make up a single logical data store.

There are many benefits of data...