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
Other Books You May Enjoy
18
Index

Data mesh in AWS

While data lakes are a popular concept, they have their issues. While putting data in one place creates a single source of truth, you are also creating a single source of failure, violating standard architecture principles to build high availability.

The other problem is that the data lake is maintained by a centralized team of data engineers who may need more domain knowledge to clean data. This results in back-and-forth communication with business users. Over time your data lake can become a data swamp.

The ultimate target of collecting data is to get business insight and retain business domain context while processing that data. What is the solution? That’s where data mesh comes into the picture. With data mesh, you can treat data as a product where the business team owns the data, and they expose it as a product that can be consumed by various other teams who need it in their account. It solves the problem of maintaining domain knowledge while...