Book Image

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4 (2)
Book Image

AWS for Solutions Architects - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

Are you excited to harness the power of AWS and unlock endless possibilities for your business? Look no further than the second edition of AWS for Solutions Architects! Imagine crafting cloud solutions that are secure, scalable, and optimized – not just good, but industry-leading. This updated guide throws open the doors to the AWS Well-Architected Framework, design pillars, and cloud-native design patterns empowering you to craft secure, performant, and cost-effective cloud architectures. Tame the complexities of networking, conquering edge deployments and crafting seamless hybrid cloud connections. Uncover the secrets of big data and streaming with EMR, Glue, Kinesis, and MSK, extracting valuable insights from data at speeds you never thought possible. Future-proof your cloud with game-changing insights! New chapters unveil CloudOps, machine learning, IoT, and blockchain, empowering you to build transformative solutions. Plus, unlock the secrets of storage mastery, container excellence, and data lake patterns. From simple configurations to sophisticated architectures, this guide equips you with the knowledge to solve any cloud challenge and impress even the most demanding clients. This book is your one-stop shop for architecting industry-standard AWS solutions. Stop settling for average – dive in and build like a pro!
Table of Contents (19 chapters)
17
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18
Index

The purpose of a data lake

You might not need a data lake if your company is a bootstrap start-up with a small client base. However, even the smaller entities that adopt the data lake pattern in their data ingestion and consumption will be nimbler than their competitors. Especially if you already have other systems in place, adopting a data lake will come at a high cost. The benefits must outweigh these costs, but this might be the difference between crushing your competitors and being thrust into the pile of failed companies in the long run.

The purpose of a data lake is to provide a single store for all data types, structures, and volumes, to support multiple use cases such as big data analytics, data warehousing, machine learning, and more. It enables organizations to store data in its raw form and perform transformations as needed, making it easier to extract value from data. When you are building a data lake, consider the following five V’s of big data:

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