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Solutions Architect's Handbook - Second Edition

You're reading from  Solutions Architect's Handbook - Second Edition

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781801816618
Pages 590 pages
Edition 2nd Edition
Languages
Authors (2):
Saurabh Shrivastava Saurabh Shrivastava
Profile icon Saurabh Shrivastava
Neelanjali Srivastav Neelanjali Srivastav
Profile icon Neelanjali Srivastav
View More author details

Table of Contents (22) Chapters

Preface 1. The Meaning of Solution Architecture 2. Solution Architects in an Organization 3. Attributes of the Solution Architecture 4. Principles of Solution Architecture Design 5. Cloud Migration and Hybrid Cloud Architecture Design 6. Solution Architecture Design Patterns 7. Performance Considerations 8. Security Considerations 9. Architectural Reliability Considerations 10. Operational Excellence Considerations 11. Cost Considerations 12. DevOps and Solution Architecture Framework 13. Data Engineering for Solution Architecture 14. Machine Learning Architecture 15. The Internet of Things Architecture 16. Quantum Computing 17. Rearchitecting Legacy Systems 18. Solution Architecture Document 19. Learning Soft Skills to Become a Better Solution Architect 20. Other Books You May Enjoy
21. 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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