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Solutions Architect's Handbook

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
4.2 (20)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.2 (20)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect. This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect. By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.
Table of Contents (18 chapters)
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Data Engineering and Machine Learning

In the era of the internet and digitization, data is being generated everywhere with high velocity and in high quantities. Getting insight from these huge amounts of data at a fast pace is challenging. We need to innovate continuously to ingest, store, and process this data to derive business outcomes.

With the convergence of many cloud, mobile, and social technologies, advancements in many fields such as genomics and life sciences are growing at an ever-increasing rate. Tremendous value is found in mining this data for more insight. A fundamental difference between streaming systems and batch systems is the requirement to process unlimited data. Modern stream processing systems need to produce continual results at low latency on data with high and variable input rates.

The concept of big data is more than just the collection and analysis...

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