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Solutions Architect's Handbook

You're reading from   Solutions Architect's Handbook Kick-start your solutions architect career by learning architecture design principles and strategies

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781838645649
Length 490 pages
Edition 1st Edition
Tools
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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 (18) Chapters Close

Preface 1. The Meaning of Solution Architecture 2. Solution Architects in an Organization FREE CHAPTER 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 and Machine Learning 14. Architecting Legacy Systems 15. Solution Architecture Document 16. Learning Soft Skills to Become a Better Solution Architect 17. Other Books You May Enjoy

Processing data and performing analytics

Data analytics is the process of ingesting, transforming, and visualizing data to discover useful insights for business decision-making. Over the previous decade, more data is collected and customers are looking for greater insight into their data. These customers also wanted this insight in the least amount of time, and sometimes even in real time. They wanted more ad hoc queries to answer more business questions. To answer these questions, customers needed more powerful and efficient systems.

Batch processing typically involves querying large amounts of cold data. In batch processing, it may take hours to get answers to business questions. For example, you may use batch processing to generate a billing report at the end of the month. Stream processing in real time typically involves querying small amounts of hot data and it takes only a short amount of time to get answers. MapReduce-based systems such as Hadoop are examples of platforms that...

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