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

Processing data and performing analytics

Data analytics is the process of ingesting, transforming, and visualizing data to discover valuable insights for business decision-making. Over the previous decade, more data has been collected, and customers are looking for greater insights into their data.

These customers also want these insights in the least amount of time, sometimes even in real time. They want more ad hoc queries to answer more business questions. To answer these questions, customers need more powerful and efficient systems.

Batch processing typically involves querying large amounts of cold data. In batch processing, it may take hours to get answers to business questions. For example, you may use batch processing to generate a billing report at the end of the month. Stream processing in real time typically involves querying small amounts of hot data, and it takes only a short amount of time to get answers. MapReduce-based systems such as Hadoop are examples of...