Book Image

Solutions Architect’s Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4 (2)
Book Image

Solutions Architect’s Handbook - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.
Table of Contents (22 chapters)
20
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21
Index

Data Engineering for Solution Architecture

In the internet and digitization era, data is being generated everywhere with high velocity and volume. 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 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. Modern stream processing systems need to produce continual results based on data with high input rates at low latency.

The concept of big data refers to more than just the collection and analysis of data. The actual value for organizations in their data can be used to gain insight and create competitive advantages. Not all big data solutions must end in visualization. Many solutions such as Machine Learning (ML) and other predictive...