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Solutions Architect's Handbook

You're reading from   Solutions Architect's Handbook Kick-start your career with architecture design principles, strategies, and generative AI techniques

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835084236
Length 578 pages
Edition 3rd Edition
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Authors (2):
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Neelanjali Srivastav Neelanjali Srivastav
Author Profile Icon Neelanjali Srivastav
Neelanjali Srivastav
Saurabh Shrivastava Saurabh Shrivastava
Author Profile Icon Saurabh Shrivastava
Saurabh Shrivastava
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Toc

Table of Contents (20) Chapters Close

Preface 1. Solutions Architects in Organizations FREE CHAPTER 2. Principles of Solution Architecture Design 3. Cloud Migration and Cloud Architecture Design 4. Solution Architecture Design Patterns 5. Cloud-Native Architecture Design Patterns 6. Performance Considerations 7. Security Considerations 8. Architectural Reliability Considerations 9. Operational Excellence Considerations 10. Cost Considerations 11. DevOps and Solution Architecture Framework 12. Data Engineering for Solution Architecture 13. Machine Learning Architecture 14. Generative AI Architecture 15. Rearchitecting Legacy Systems 16. Solution Architecture Document 17. Learning Soft Skills to Become a Better Solutions Architect 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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