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Solutions Architect's Handbook

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
4.7 (59)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.7 (59)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Master the art of solution architecture and excel as a Solutions Architect 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. Large language models, generative AI, and innovations in deep learning are cutting-edge advancements shaping the future of technology. Topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient and secure architectures remain important in today's landscape. This book provides coverage of these emerging and key technologies and walks you through solution architecture design from key principles, providing you with the knowledge you need to succeed as a Solutions Architect. It will also level up your soft skills, providing career-accelerating techniques to help you get ahead. Unlock the potential of cutting-edge technologies, gain 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
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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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