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 ingestion

Data ingestion is the act of collecting data for transfer and storage. There are lots of places that data can be onboarded. Predominantly, data ingestion falls into one of the categories from databases, streams, logs, and files. Among these, databases are the most popular. These typically consist of your main upstream transactional systems that are the primary data storage for your applications. They take on both relational and non-relational flavors, and there are several techniques for extracting data out of them.

Streams are open-ended sequences of time-series data such as clickstream data from websites or IoT devices, usually published into an API we host. Logs get generated by applications, services, and operating systems. A data lake is a great place to store all of the data for centralized analysis. Data lakes provide a single source of truth to store all data in one place and break data silos across various business units in the organization. In a later...