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

Summary


In this chapter, we focused on looking at data collection systems that are available in the open source community and can be used to implement different varieties of use cases.

We looked at NiFi, which is a highly-scalable and user-friendly system to define data flows. We looked at Sqoop, which addresses a very specific use case of transferring data between HDFS and relational systems. We also discussed the ELK stack, which is very popular in the industry for collecting and visualizing large amounts of data.

The next chapter will focus on the next aspect of handling data, which is data processing.

We will discuss the various requirements of Data Processing, and we will look at various challenges in processing data at scale. Stay tuned.