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

Designing big data processing pipelines

One of the critical mistakes many big data architectures make is handling multiple stages of the data pipeline with one tool. A fleet of servers managing the end-to-end data pipeline, from data storage and transformation to visualization, may be the most straightforward architecture, but it is also the most vulnerable to breakdowns in the pipeline. Such tightly coupled big data architecture typically does not provide the best possible balance of throughput and cost for your needs. When you are designing a data architecture, use FLAIR data principles as explained below:

  • F: Findability. The ability to view which data assets are available, access metadata including ownership and data classification, and other mandatory attributes for data governance and compliance
  • L: Lineage. The ability to find the data origin, trace data back, and understand and visualize data as it flows from data sources to consumption
  • A: Accessibility...