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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

Building a data search functionality

Oftentimes, you will need to search a large volume of data to solve issues quickly or get business insights. The ability to search your application data will help you to access detailed information and analyze it from different views. To search for data with low latency and high throughput, you need to have search engines as your technology choice.

Elasticsearch is one of the most popular search engine platforms and is built on top of the Apache Lucene library. Apache Lucene is a free and open source software library that is the foundation of many popular search engines. The ELK (short for Elasticsearch, Logstash, and Kibana) stack is easy to use for the automatic discovery of large-scale data and to index it for searching. Because of its properties, there are multiple tools that have been developed around Elasticsearch for visualization and analysis—for example, LogStash works with Elasticsearch to collect, transform, and analyze a large amount...

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