Book Image

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
Book Image

Solutions Architect's Handbook

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)

What is big data architecture?

The sheer volume of collected data can cause problems. With the accumulation of more and more data, managing and moving the data along with its underlying big data infrastructure becomes increasingly difficult. The rise of cloud providers has facilitated the ability to move applications to the data. Multiple sources of data result in increased volumes, velocity, and variety. The following are some common computer-generated data sources:

  • Application server logs: Application logs and games
  • Clickstream logs: From website clicks and browsing
  • Sensor data: Weather, water, wind energy, and smart grids
  • Images and videos: Traffic and security cameras

Computer-generated data can vary from semi-structured logs to unstructured binaries. This data source can produce pattern-matching or correlations in data that generate recommendations for social networking and online gaming in particular. You can also use computer-generated data to track applications or service behavior...