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Solutions Architect's Handbook

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
4.7 (59)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.7 (59)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Master the art of solution architecture and excel as a Solutions Architect with the Solutions Architect's Handbook. Authored by seasoned AWS technology leaders Saurabh Shrivastav and Neelanjali Srivastav, this book goes beyond traditional certification guides, offering in-depth insights and advanced techniques to meet the specific needs and challenges of solutions architects today. This edition introduces exciting new features that keep you at the forefront of this evolving field. Large language models, generative AI, and innovations in deep learning are cutting-edge advancements shaping the future of technology. Topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient and secure architectures remain important in today's landscape. This book provides coverage of these emerging and key technologies and walks you through solution architecture design from key principles, providing you with the knowledge you need to succeed as a Solutions Architect. It will also level up your soft skills, providing career-accelerating techniques to help you get ahead. Unlock the potential of cutting-edge technologies, gain practical insights from real-world scenarios, and enhance your solution architecture skills with the Solutions Architect's Handbook.
Table of Contents (20 chapters)
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18
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19
Index

Data ingestion, storage, processing, and analytics

To turn raw data into actionable intelligence that can inform decision making and strategic planning for businesses, data needs to be managed through several key stages, beginning with data ingestion—the collection of data from various sources. This can include everything from user-generated data to machine logs, or real-time streaming data. Once collected, the data needs to be stored in data storage, which can be done in databases, data lakes, or cloud storage solutions, depending on the data type and intended use.

Following storage, data processing and analytics come into play, which involves sorting, aggregating, or transforming the data into a more usable form, where analytics can be performed on the processed data to extract meaningful insights. Analytics can range from simple queries and reporting to complex ML algorithms and predictive modeling. Let’s learn about these stages in detail.

Data ingestion

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