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Solutions Architect's Handbook

Solutions Architect's Handbook - Third Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4.6 (57)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.6 (57)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Build a strong foundation in solution architecture and excel in your career with the Solutions Architect’s Handbook. Authored by seasoned AWS technology leaders Saurabh Shrivastav and Neelanjali Srivastav, this book goes beyond traditional certification guides, offering in-depth insights and advanced techniques to meet the specific needs and challenges of solutions architects today. This edition introduces exciting new features that keep you at the forefront of this evolving field. From large language models and generative AI to deep learning innovations, these cutting-edge advancements are shaping the future of technology. Key topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient, secure architectures remain essential today. This book covers both emerging and foundational technologies, guiding you through solution architecture design with key principles and providing the knowledge you need to succeed as a Solutions Architect. It also sharpens your soft skills, providing career-accelerating techniques to stay ahead. By the end of this book, you will be able to harness cutting-edge technologies, apply practical insights from real-world scenarios, and enhance your solution architecture skills with the Solutions Architect's Handbook.
Table of Contents (20 chapters)
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18
Other Books You May Enjoy
19
Index

What is big data architecture?

The sheer volume of collected data can cause problems. With the accumulation of more and more data, managing and moving data along with its underlying big data infrastructure becomes increasingly difficult. The rise of cloud providers has facilitated the ability to move applications to the cloud. Multiple sources of data result in increased volumes, velocity, and variety. The following are some common computer-generated data sources:

  • Application server logs: Application logs and games
  • Clickstream logs: From website clicks and browsing
  • Sensor data: Weather, water, wind energy, and smart grids
  • Images and videos: Traffic and security cameras

Computer-generated data can vary from semi-structured logs to unstructured binaries. Computer-generated data sources can produce pattern matching or correlations in data that generate recommendations for social networking and online gaming. You can also use computer-generated data...

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Solutions Architect's Handbook
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