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

Lambda architecture and batch processing


The Lambda architecture had to be involved in the discussion of batch processing since batch processing is one of the layers in the Lambda architecture, and we are building a batch processing layer for a data-intensive application.

So, the following screenshot shows how to quickly review a Lambda architecture:

We will not go in to the details of the Lambda architecture again but instead focus on point 2 in the preceding diagram, labelled the batch layer.

The batch layer in Lambda mainly performs two functions:

  • Manages the master dataset. This master dataset is advised to be defined as an immutable, append-only set of raw data.
  • Precomputes the batch view that feeds into the serving layer being used by the underlying query system.

It is the second function that we will discuss in this chapter. Managing the master dataset does not usually require a lot of development time or effort. Point 1 is mainly the responsibility of the operations team.

 

 

Now, if you...