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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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Structured data stores

Structured data stores have been around for decades and are the most familiar technology choice when it comes to storing data. Most of the transactional databases such as Oracle, MySQL, SQL Server, and PostgreSQL are row-based due to dealing with frequent data writes from software applications. Organizations often repurpose transactional database for reporting purposes, where frequent data reads are required, but much fewer data writes. Looking at high data-read requirements, there is more innovation coming into an area of query on structured data stores, such as the columnar file format, which helps to enhance data read performance for analytics requirements.

Row-based formats store the data in rows in a file. Row-based writing is the fastest way to write the data to the disk but it is not necessarily the quickest read option because you have to skip over lots of irrelevant data. Column-based formats store all the column values together in the file. This leads...

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