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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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Online analytical processing (OLAP)

OLTP and NoSQL databases are useful for application deployment but have limited capabilities for large-scale analysis. A query for a large volume of structured data for analytics purposes is better served by a data warehouse platform designed for faster access to structured data. Modern data warehouse technologies adopt the columnar format and use massive parallel processing (MPP), which helps to fetch and analyze data faster.

The columnar format avoids the need to scan the entire table when you need to aggregate only one column for data—for example, if you want to determine the sales of your inventory in a given month. There may be hundreds of columns in the order table, but you need to aggregate data from the purchase column only. With a columnar format, you will only scan the purchase column, which reduces the amount of data scanned compared to the row format, and thereby increases the query performance.

With massive parallel processing, you...

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