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

Apache Flume


Apache Flume is an open source system that was primarily developed to solve the following use case:

How to efficiently and reliably collect large amounts of Log-related data from different systems, normalize them, and store them in a reliable store.

At first glance, the use case seems simple enough to question the need of developing an entire system around it. But when developing a distributed, reliable, and fault-tolerant system that spans multiple machines running in different regions, a simple use case of aggregating logs from different machines and different application instances suddenly seem humongous.

You must keep a lot of things in mind. For example:

  • All the systems that deploy your distributed application should have the same time zone setting so that logs can be aggregated correctly.
  • Since a request can span across multiple systems before a response is generated, you need a mechanism to correctly correlate log entries in a consistent fashion.
  • Different applications may...